The effects of transcutaneous electroacupuncture ...electroacupuncture.qeeg.co.uk/wp-content/uploads/...Patients often report finding acupuncture treatment relaxing, whether this is

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The effects of transcutaneous electroacupuncture stimulation (TEAS)

on heart rate variability (HRV) and nonlinearity (HRNL):

Is stimulation frequency or amplitude more important? David Mayor,a, 1 Tony Steffert,a, b Deepak Pandaya

a. University of Hertfordshire; b. Open University.

Contents

Background p. 2

Heart rate variability (HRV)

Nonlinearity (NL)

Electroacupuncture

Parameters of EA and TEAS, their effects on HRV, and the rationale for this study

Methods p. 5

Results p. 7

1. Coefficient of variation (CV)

2. Significant differences in HRV measures and HRNL when comparing stimulation

amplitudes and frequencies in the same time slots

3. Changes over time: Significant differences in HRV measures and HRNL between baseline

(Slot 1) and post‐TEAS (Slot 6), for the different TEAS amplitudes and frequencies

4. Significant differences in HRV measures and HRNL within the same time slots and

over time: Comparing effect sizes

5. Numbers of significant differences in HRV measures and HRNL indices when comparing

TEAS amplitudes and frequencies within slots

6. Graphical illustrations of changes in median values of HRV measures and HRNL indices

over time, showing TEAS amplitude and frequency effects

7. Summarising %Diff between median values of HRV measures and HRNL indices over time,

showing TEAS amplitude and frequency effects

8. Increases and decreases in HRV measures over time, between slots 1 and 6

Discussion p. 58

Main findings

Possible directions for further research

Limitations

1 Corresponding author: davidmayorwelwynacupuncture.co.uk

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Conclusions p. 61

Author contributions

Acknowledgements

Appendix p. 62

References p. 63

Background

In March 2019, the corresponding author (DM) presented a poster about the frequency‐specific

effects of transcutaneous electroacupuncture stimulation (TEAS) on heart rate variability (HRV) at

the 21st International Acupuncture Research Symposium in London organised by the Acupuncture

Research Resource Centre (Mayor et al. 2019).

What follows in italics is taken directly from the online background information provided for that

poster.

Heart rate variability (HRV)

Heart rate (HR) is not a constant, but varies, and heart rate variability (HRV) is considered to be a

measure of the interplay between the sympathetic and parasympathetic nervous systems. The

general consensus is that – up to a point – the greater the HR variability or its complexity, the more

healthy are the autonomic and cardiac systems – as well as other physiological functions with which

they interact (see below for further information; further references may be found in Steffert & Mayor

2014). HRV is thus increasingly used, both clinically and experimentally, with over 9000 citations

currently in PubMed, over 130 of which also mention acupuncture (around 1.4% of the total,

although down from 1.5% in 2014). The earliest study on acupuncture and HRV in PubMed dates

back to 1995 (Shi et al.).

Nonlinearity (NL)

Nonlinearity (NL) can be defined as characterising a system whose output is not simply definable or

predictable from knowing its input – in other words, its components interact non‐additively. The

heart, influenced by many physiological factors, is such a system, and its activity – in particular the R‐

to‐R inter‐beat interval (IBI) – is nonlinear as well as variable.

Several HRV measures in the frequency domain – usually computed using the fast Fourier transform

(FFT) – assume linearity of data; other, ‘nonlinear’ measures assume that data are nonlinear. Both

linear and nonlinear measures are often recommended to be used together, and many studies have

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been published with findings that nonlinear measures provide greater diagnostic differentiation than

linear measures alone.

Therefore, it would be useful to have some measure of how nonlinear a data sample is in order to

assess the accuracy of results using measures which assume at least a degree of linearity or

nonlinearity (Acharya et al. 2006).

Pedro Bernaola‐Galván and colleagues at the University of Málaga have proposed a family of NL

indices of a time series based on the deviation of the autocorrelation function of the magnitude

(absolute value, |x|) of the original time series from the same function for a sample of linear

Gaussian noise (Bernaola‐Galván et al. 2017). They have used these indices to explore differences in

nonlinear heart dynamics (heart rate nonlinearity, HRNL) during rest and exercise in soccer players

(Gómez‐Extremera et al. 2018). Ours is the first large‐scale study by other researchers to make use

of the Málaga method.

Electroacupuncture

Electroacupuncture (EA) and transcutaneous electroacupuncture stimulation (TEAS) are generally

applied at low (2‐4 Hz), midrange (8‐25 Hz) or high (50‐200 Hz) frequencies, or using alternating

(‘dense‐disperse’) low and high frequencies (Mayor 2016).

In 2014, DM presented a poster at an earlier Acupuncture Research Symposium on how acupuncture

treatment factors contribute to changes in HRV (Steffert & Mayor 2014), especially when using EA or

TEAS. Based on three small pilot studies (total number of participants 23, attending for 76 visits in

all), the conclusion was that there was a small but non‐significant difference in several HRV measures

in response to stimulation at 2.5 Hz (cycles per second) or pulses per second (pps), with less of an

effect for stimulation at 10 Hz/pps. These effects of frequency on HRV were probably masked by

those of other treatment factors, particularly the individual responsiveness of study participants.

Following these and other pilot studies, in 2015‐2016 we conducted a larger study with the same

objective, namely to ascertain if stimulation has frequency‐specific effects on HRV. In February this

year the ECG R‐to‐R ‘inter‐beat interval’(RR) data from the study finally became available (Fig 1), and

we were able to start analysing the data.

Figure 1. The ECG RR inter‐beat interval, from which all HRV measures are derived

(from Cornforth et al. 2015).

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During the poster presentation at the ARRC Symposium this year, one of the delegates questioned

DM’s unspoken assumption that acupuncture should be non‐stressful to achieve beneficial effects,

and asked whether we had considered the effects of stimulation amplitude on HRV. This second

presentation on HRV from our more comprehensive study is the result of that question.

Parameters of EA and TEAS, their effects on HRV, and the rationale for this study

Patients often report finding acupuncture treatment relaxing, whether this is traditional

acupuncture or electroacupuncture (EA), indicating that both have effects on the autonomic nervous

system (Mayor 2007). However, EA and transcutaneous EA stimulation (TEAS) are often used

without understanding the autonomic effects of different parameters of frequency and amplitude.

This study investigates such effects using HRV measures and HRNL indices.

A brief review of prior research into these effects was provided in our 2014 study (Steffert & Mayor

2014). A further literature review was then undertaken, with 591 papers located in Medline using

the search terms “(TENS OR electroacupuncture) AND (autonomic OR parasympathetic)”.

Findings from the few papers comparing different parameters or reporting parameter‐specific

effects indicated that:

Low frequency (LF) TENS or EA may decrease sympathetic nervous system (SNS) activity and/or

increase parasympathetic nervous system (PNS) activity (Cui et al. 2016; do Amaral Sartori et al.

2018; Liu et al. 2014; Michikami et al. 2006; Olyaei et al. 2004).

In particular, 1 Hz EA at point P4 (ximen) was found to decrease heart rate (HR) more than EA at 5 or

10 Hz (Nakahara et al. 2016), and 2 Hz EA at acupuncture points ST36 (zusanli) and ST37 (shangjuxu)

to increase vagal activity whereas 15 Hz EA increased sympathetic activity (Jia et al. 2011). In

contrast, other researchers found increased SNS activity following 2 Hz EA at ST36 (Chang et al.

2005).

However, the amplitude of 2 Hz EA was also found to be important, with stronger deqi leading to

more sympathetic activation (Yu & Jones 2013), and similarly for high amplitude TENS (20 x motor

threshold, albeit in anaesthetised rats) (Liao et al. 2002). Thus amplitude may have a confounding

effect in some of these studies on the effects of frequency.

Furthermore, it should always be borne in mind that EA tends to have a normalising effect (Mayor

2007), and in acute myocardial ischaemia LF EA may in fact increase sympathetic activity (Cai et al.

2007; Wu et al. 2010).

Mid frequency: Comparing the effects of TENS at 10 Hz and 100 Hz on HRV, Stein et al. (2011) found

the former tended to enhance parasympathetic activity and decrease sympathetic activity, with 100

Hz TENS having an opposite effect. 10 Hz EA similarly stimulated parasympathetic activity and

inhibited sympathetic activity when applied at ST36 in rats suffering restraint stress (Imai et al.

2009), although if applied at ST25, the same stimulation had a stimulatory effect on SNS activity

(Imai et al. 2008).

High frequency: On the other hand, high frequency (HF) EA may enhance SNS activity immediately

after stimulation, although after a further 20 minutes it may also enhance PNS activity

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(Hideaki et al. 2015).2 Other researchers have also found that HF EA may increase PNS activity (Lee

et al. 2011; Waki et al. 2017), and some that neither HF nor LF EA have significant effects on HRV in

healthy volunteers (Chang et al. 2010) or dogs (Kimura & Hara 2008). TENS – whether high‐

frequency/low‐intensity, low‐frequency/high‐intensity, or sham, similarly failed to differentially

effect SNS responses in an earlier study (Reeves et al. 2004).

In the present study, which is larger than most of those cited above, we aim to clarify the effects of

both amplitude and frequency of stimulation on the autonomic nervous system, using a greater

variety of HRV measures than is usual, as well as some innovative HRNL indices. We believe our

findings may have clinical application for those that use EA and TEAS, but will also throw light on

broader issues that are relevant to acupuncture practice as a whole and assist understanding of the

dynamics of the autonomic nervous system.

Methods

The methods used in this single‐centre, randomised, single‐blind, four‐way cross‐over study are

taken directly from the online background material to our earlier poster (Mayor et al. 2019):

Ethics approval for the study was granted by the Health and Human Sciences Ethics Committee with

Delegated Authority of the University of Hertfordshire (UH) – Protocol number HSK/SF/UH/00124.

Participants were healthy volunteers recruited from among staff and students at the University, local

complementary health practitioners and other contacts. Exclusion criteria included past serious head

injury, respiratory conditions that might impair nose breathing, wearing of an implanted electronic

device, impaired peripheral circulation or cutaneous sensory deficit of the hands, or current

shoulder, arm or hand injury. Those with severe learning disabilities unlikely to be able to complete

their involvement in or otherwise comply with the requirements of the study were also excluded, as

was anyone with only minimal understanding of English. Dependency on prescribed or other

psychoactive substances, or very heavy use of caffeine, nicotine or alcohol, also led to exclusion.

Those currently undergoing other non‐routine (i.e. not ‘preventive’) non‐pharmacological or

complementary medical treatments could also be excluded (depending on circumstances). Women

who knew they were pregnant were excluded in all cases.

After completion of some online questionnaires and an explanation of the procedures to be followed,

participants attended for their first session, attending for four in all (except for four who dropped out

after only one session, and another who only completed three sessions).

Informed consent was obtained, further paper questionnaires completed, and the participants were

then prepared for the session. This preparation, which took around 15 minutes, involved fitting an

EEG cap with head movement sensors attached, and affixing ECG electrodes to the forearms, as well

as other sensors to the fingers of both hands (Fig 2A). The EEG cap, ECG electrodes and other sensors

were worn for the remainder of the session (usually around 60 to 90 minutes). The EEG cap and other

sensors were being used for further experiments for which the data have not yet been analysed.

2 This may correspond to patterns of initial activation followed by deactivation found for both high‐ and low‐frequency EA and TEAS in fMRI studies (Jiang et al. 2014; Li et al. 2014).

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Following an initial 5‐minute baseline recording, TEAS was applied for 20 minutes to each hand, with

a short pause halfway through to allow further questionnaires to be completed and participants to

rest briefly. In each 10‐minute period, TEAS was applied first to the left hand at a slowly increasing

amplitude, the output level at which the participant first felt the stimulation (their ‘sensory

threshold’) was recorded, and then output increased to a level considered ‘strong but comfortable’

by the participant. This was recorded and taken to indicate the participant’s ‘tolerance threshold’ on

the left hand. While TEAS on this hand continued, stimulation was turned up in the same way on the

right, and then TEAS continued for ten minutes on both hands (see Mayor 2018 for further details).

ECG recording continued during stimulation, which was between the acupuncture point LI4 (hegu)

and the ulnar border of each hand (JR Worsley’s location for SI3, houxi ). In other words, current only

passed between the electrodes on each hand, and did not flow through the arms and torso, so that it

should not affect the heart directly.3 After stimulation (and completion of other questionnaires),

recording was continued for a further 15 minutes to assess post‐stimulation changes. Electrodes and

sensors were removed, and further questionnaires filled out before the participant left.

Figure 2. A. ECG electrodes for two separate amplifiers on right forearm, with additional pulse

oximeter for photoplethysmography (PPG) on finger. B. The stimulator used in our study.

A charge‐balanced Equinox E‐T388 stimulator (Equinox International, St Peter Port, Guernsey, shown

in Fig 2B) was used in all four sessions, and set at one of four different frequencies – 2.5 alternating

monophasic pulses per second (pps), 10 pps, 80 pps or 160 pps in each session (strictly speaking, the

frequency or number of cycles of stimulation per second, in units of Hertz, was at half these values).

For the three lower frequencies, output amplitude was set to provide a ‘strong but comfortable’

sensation for that particular participant – as described in a presentation on the effects of amplitude

at the AACP conference in Leeds last October (Mayor 2018). In contrast, 160 pps was applied as a

‘sham’ treatment, with the device switched on (and a flashing light visible), but the output amplitude

remaining at zero throughout – although a pretence was made of turning up the amplitude out of

sight of the participants. Nonetheless, the stimulation (at 80 and 160 pps) was visible as an

interference pattern (envelope) on one of the screens showing the recorded ECG (although hidden

3 In our earlier pilot studies, we used acupuncture points LI4 and ST36, in accordance with common practice in studies researching the effects of TEAS (or EA) on the EEG (Mayor 2015, unpublished) and HRV (Liu et al. 2014; Yu & Jones 2013). SI3 was only used to provide a location for the return electrode on each hand, not for any specific therapeutic purpose.

A B

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from participants’ view so as not to distract them), and some participants were aware of a sensation

in their hands at some moments during their sham session. The different stimulation frequencies for

each participant were applied in a semi‐randomised balanced order.

ECG data was collected using two different systems concurrently, the data for this HRV analysis being

collected in eight five‐minute recordings during each session (i.e. for a total of 40 minutes) from a

Mitsar‐EEG‐202 amplifier with WinEEG software v2.114.81 (Mitsar, St Petersburg, Russia), sampled

at 2000 Hz and stored at 500 Hz.

Following collection, the data for each session was split into its eight five‐minute component

recordings (‘slots’), exported into Matlab, and each recording was then processed separately using

Kubios HRV Premium software (v3.1; Kuopio, Finland), with an automatic RR correction algorithm to

deal with artefacts and a ‘smoothness priors’ method of trend removal. For spectrum estimation, a

piecewise cubic spline interpolation was used with the default rate of 4 Hz, and the Lomb‐Scargle

rather than Welch’s periodogram (Clifford & Tarassenko 2005; Van Dongen et al. 1999).

The graphed output from the Kubios HRV software for each of the resulting recordings was then

examined carefully for any remaining unusual findings or artefacts (focusing on plots of the RR inter‐

beat intervals, RR and heart rate (HR) histograms and SD2/SD1 Poincaré plots). RR Data that was too

noisy for automatic artefact correction was then pre‐processed manually in Matlab R2015a

(Mathworks, Cambridge, UK), and the results processed using the Kubios HRV software as before.

Following this lengthy procedure, 1988 5‐minute time series were available for further analysis

(complete datasets for 55 participants, with one session incomplete for each of 2.5 pps and 80 pps,

two for sham and four for 10 pps stimulation). The various measures produced by the software were

finally sorted and collated in Matlab into spreadsheets suitable for statistical analysis using Excel

2010 (Microsoft, Seattle, WA) and SPSS (v 23; IBM, Armonk, NY).

Standard procedures were used to assess whether our HRV data were normally distributed or not,

and non‐parametric statistical methods adopted as a result. For correlations, Spearman’s rho was

used in preference to Pearson’s r (for further details, see Mayor et al. 2019, p. 7). Other non‐

parametric methods used were the Wilcoxon signed ranks test and the Binomial test. Data were

analysed for the various stimulation frequencies (2.5 pps, 10 pps, 80 pps and, where relevant, sham)

and amplitudes. Amplitude was defined as the average of the four tolerance thresholds recorded in

each session (beginning on the left and then on the right hand, at the start of the first and second

ten‐minute periods of stimulation). For each active frequency, amplitude was defined as ‘high’ or

‘low’, relative to the group median amplitude for that frequency. An initial graphical analysis was

also undertaken to obtain an overview of trends and differences.

Results

1. Coefficient of variation (CV)

As described in our ARRC Symposium poster and in the Appendix here, those HRV measures and

heart rate nonlinearity (HRNL) indices with coefficient of variation (CV) greater than 0.4 were initially

considered most likely to show differences in response to stimulation at different frequencies or

amplitudes. Table 1 shows those HRV measures and the two HRNL indices with highest CV.

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Table 1. HRV and main HRNL measures with CV > 4.

Measure PNS SNS HFabs LF/HF D2 D1+D2

Av ‐1.641 1.015 1.841 2.519 41026.168 8.830

SD 45.467 9.989 9.649 15.429 1155221.277 108.559

CV 27.713 9.844 5.240 6.126 28.158 12.294 Measure MSE1 MSE4 MSE5 MSE6 MSE18 MSE20

Av 1.146 1.240 1.198 1.250 2.666 2.649

SD 4.657 6.525 4.946 5.793 13.089 18.0750

CV 4.063 5.262 4.130 4.636 4.910 6.815

However, it may not always be the case that a measure with greater CV is more likely to

demonstrate statistically significant differences between conditions than a measure with lower CV;

other factors may also contribute to such differences (see e.g. Kobayashi et al. 2011). We therefore

did not follow this line of investigation further.

2. Significant differences in HRV measures and HRNL when comparing stimulation amplitudes and

frequencies in the same time slots

Table 2 shows those HRV measures and one HRNL index that most often showed significant

differences between the various TEAS conditions in each of the different slots, using the non‐

parametric Wilcoxon signed ranks test (high vs low amplitude for the same stimulation frequency,

i.e. in 8 x 3 comparisons, and among the different stimulation frequencies disregarding amplitude,

i.e. in 8 x 6 comparisons). These overlap with but are not the same as those shown in Table 1.

Table 2. HRV measures and HRNL index most responsive to different TEAS conditions.

Numbers indicate how many times differences in each measure were significant, summed over

all eight slots. Only those measures in the upper quartile of counts are included (N ≥ Q3).

Category Measure/index N comparing amplitudes (max 24)

N comparing frequencies (max 48)

Shared General PNS Time domain rMSSD 4

NNxx pNNxx Frequency domain HFabs HFlog Nonlinear SD14 SD2/SD1 DFA α1

7 7 7 6 6 6 7 8 12

12 15 11 7 6 5 15 13 6

N ≥ Q3 (6) for amplitude only

General n/a Time domain n/a

4 rMSDSS and SD1 are – although classified as ‘time domain’ and ‘nonlinear’, respectively – computationally identical (Brennan et al. 2001; Ciccone et al. 2017).

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Frequency domain HF% HFnu LFabs LFlog LF% LFnu LF/HF Nonlinearity indices D1+D2

14 14 6 6 15 14 15 6

N ≥ Q3 (5) for frequency only

General SNS Time domain n/a Frequency domain n/a Nonlinear ShannEn SampEn/MSE1 MSE5 MSE7 MSE19

5 8 8 6 7 9

Totals 156 133

Note that the measures in the upper quartile for TEAS amplitude were all (apart from D1+D2) in the

frequency domain, and those in the upper quartile for TEAS frequency were all (apart from SNS) in

the nonlinear (entropy or complexity) domain, and that total numbers of differences between

stimulation amplitudes exceeded those for the differences among stimulation frequencies by around

17%.

Key to abbreviations in Tables 1, 2, 5 and 11. General measures PNS: Parasympathetic nervous system (PNS) tone index [P] This is a relatively new HRV measure developed by Mika Tarvainen’s team in Finland, and is a composite based on two time‐domain HRV measures – the mean R‐to‐R (RR) inter‐beat interval and the square root of the mean squared differences between successive RR intervals (rMSSD) – together with one frequency‐domain HRV measure, normalised power in the high‐frequency band of the HRV spectrum, or HFnu (Mayor et al. 2019 p. 6).5 SNS: Sympathetic nervous system (SNS) tone index [S] This index, again developed by Tarvainen’ group, is a composite based on measures from three different domains – mean heart rate (HR), the square of Baevsky’s ‘Stress index’ (SI) (Baevsky &

5 In later versions of the Kubios HRV software than that used to process our study data (Version 3.1.0, released 19.3.2018), HFnu has now been replaced by the Poincaré plot parameter SD1 (Tarvainen et al. n.d.).

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Berseneva 2008; Tarvainen et al. n.d.) and normalised power in the low‐frequency band of the HRV spectrum, or LFnu (Mayor et al. 2019 p. 6).6 SI: Stress index [S] – See under SNS. Time domain measures RR: Mean R‐to‐R inter‐beat interval in the ECG data [P]. HRmean: Mean heart rate [S]; also HRmax [S] and HRmin [S]. rMSSD: Square root of the mean squared differences between successive RR intervals [P]. NNxx: The number of successive RR interval pairs that differ more than xx ms (with xx= 50 by default) [P]. pNNxx: NNxx divided by the total number of RR intervals (Malik 1996) [P]. Frequency domain measures HFabs: Absolute power (ms2) in the high‐frequency band of the HRV spectrum (0.15 to 0.4 Hz) [P]. HFlog: Natural logarithm transformed value of HFabs [P]. HF%: Relative power in the HF band [P]. HFnu: Ratio of HFabs/(Total Power – VLFabs) x 100 [P]. LFabs: Absolute power (ms2) in the low‐frequency band of the HRV spectrum (0.04 to 0.15 Hz) [S]. LFlog: Natural logarithm transformed value of LFabs [S]. LF%: Relative power in the LF band [S]. LFnu: Ratio of LFabs/(Total Power – VLFabs) x 100 [S]. LF/HF: The ratio of LF and HF powers (LF/HF) [~S] (Heathers 2014) [VLFabs: Absolute power (ms2) in the very low‐frequency band of the HRV spectrum.7] TotPwr: Sum of LFabs, HFabs and VLFabs [P]. HF.Hz: Peak frequency in the HF band [P?]. LF.Hz: Peak frequency in the LF band [S?].

6 Prior analysis of SI values for participants in our study (Mayor et al. 2019) was not altogether reassuring as to its validity, and this may also have impacted the SNS indices that resulted. In recent versions of Kubios HRV, LFnu has now been replaced by the Poincaré plot parameter SD2 (Tarvainen et al. n.d.). 7 VLFabs is only appropriate for ECG recordings lasting longer than a few minutes, so was not analysed in this study.

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Nonlinear (complexity/entropy) measures SD1: Short‐term standard deviation of the RR interval Poincaré plot [P] (Brennan et al. 2001). SD2/SD1: Ratio of the short‐ and long‐term standard deviations of the RR interval Poincaré plot [~S] ( Hsu et al. 2012). ApEn: Approximat entropy [P] (Pincus 1991), a precursor to SampEn, but still often used in HRV studies. SampEn: Sample entropy [P], a ‘conditional entropy’ measure of complexity (Azami & Escudero 2018), considered more accurate than ApEn (Richman & Moorman 2000). ShannEn: Shannon entropy [S?], the simplest form of entropy to calculate, but in some ways more difficult to interpret than conditional entropy measures such as SampEn .8 DFA α1: Detrended fluctuation analysis (DFA) short term fluctuation slopes (alpha1) [S] measures the fractal properties of HRV (Silva et al. 2017). MSE1 [P] to MSE20 [S?]: Multiscale entropy, scales 1 to 20 (Costa et al. 2005), with scale 1 equivalent to Sample entropy (SampEn). Nonlinearity indices D2 and D1+D2 [P]: These are HRNL indices (see above, pp. 2‐3). pD2 [S] is the probability that a given time series is linear when NL is computed using D2. PNS‐like [P] and SNS‐like [S] measures and indices In the list above, those measures whose increase in healthy volunteers may indicate enhanced parasympathetic functioning or beneficial effects are followed by ‘[P]’, and those whose increase may indicate enhanced sympathetic functioning (and possibly stress or less good parasympathetic or other functioning) with ‘[S]’. However, there is still considerable controversy on whether it is correct to allocate even some of the most commonly used HRV measures – HF and LF variants and their ratio – to the ‘[P]’ or ‘[S]’ category (Heathers 2014), so although this classification is useful in distinguishing ‘PNS‐like’ and ‘SNS‐like’ measures, it should not be taken as immutable,9 and for some measures – particularly MSE at scales >2 – attempting such a Procrustean binary classification may be inappropriate. It is therefore often considered advisable to utilise more than one measure when analysing HRV findings (Voss et al. 2006). This is the approach we have adopted here. Further information on HRV measures can be found in Tarvainen et al. 2019, and the HRV literature.

8 In our last conference poster (Mayor et al. 2019), ShannEn was interpreted as ‘PNS‐like’, but further analysis of our HRV study data has indicated that it may change in the opposite direction to SampEn (see below, p. 30). This has been found in certain circumstances by other authors as well (Intharakham et al. 2017). 9 There is a lack of agreement on which MSE scales can be considered ‘PNS‐like’ and which ‘SNS‐like’, for example (Mayor et al. 2019, pp. 13‐14).

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2.1. Comparisons within the same time slots: Effect size (ES) for the significant results found with the

Wilcoxon signed‐ranks test

Effect size (ES) was calculated from Z scores and the total number N of participants in each

comparison using the formula ES = Z/√(N) (Watson 2011). Median ESs are shown in Table 3.

Table 3. Summary of median ES for the various comparisons outlined in Table 2.

Category Measure/index Median ES comparing amplitudes

Median ES comparing frequencies

Shared General PNS Time domain rMSSD

NNxx pNNxx Frequency domain HFabs HFlog Nonlinear SD1 SD2/SD1 DFA α1

0.27 0.26 0.27 0.275 0.31 0.31 0.26 0.295 0.31

0.29 0.30 0.27 0.28 0.295 0.31 0.30 0.37 0.285

N ≥ Q3 (6) for amplitude only

General n/a Time domain n/a Frequency domain HF% HFnu LFabs LFlog LF% LFnu LF/HF Nonlinear D1+D2

0.345 0.35 0.265 0.265 0.34 0.345 0.34 0.30

0.305 0.33 n/a n/a 0.34 0.33 0.28 0.27

N ≥ Q3 (5) for frequency only

General SNS Time domain n/a Frequency domain n/a Nonlinear ShannEn SampEn/MSE1 MSE5 MSE7 MSE19

0.27 0.27 0.25 0.35 0.34 0.375

0.29 0.33 0.315 0.295 0.33 0.34

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Note that ES for a particular ‘shared’ or ‘frequency only’ HRV measure was more often marginally

larger when comparing frequencies than when comparing amplitudes (10 vs 5 instances) , and more

often smaller for HRV measures not in the upper quartile (11 of 14 results in the greyed‐out cells).

The largest ES was for SD2/SD1, comparing frequencies, and the smallest ES for SampEn/MSE1,

comparing amplitudes.

Table 4 shows median values of ES in the different slots, for all measures and indices taken together,

illustrating again that ES was often marginally larger when comparing frequencies than when

comparing amplitudes.10

Table 4. Median values of ES in the different slots, for all measures and indices taken together.

Comparison Slot 1 Slot 2 Slot 3 Slot 4 Slot 5 Slot 6 Slot 7 Slot 8 Median

High/low 2.5 pps

0.33 0.35 0.30 0.34 0.26 0.255 0.25 0.27 0.285

High/low 10 pps

0.28 0.32 0.30 0.30 0.29 0.33 0.315 0.30 0.30

High/low 80 pps

0.26 0.30 0.28 0.29 0.27 0.28 0.265 0.35 0.28

Median All 0.28 0.32 0.30 0.30 0.27 0.28 0.265 0.30 0.29

Comparison Slot 1 Slot 2 Slot 3 Slot 4 Slot 5 Slot 6 Slot 7 Slot 8 Median

2.5 vs sham 0.32 0.32 0.33 0.28 0.32

2.5 vs 10 0.28 0.30 0.315 0.27 0.28 0.28

2.5 vs 80 0.315 0.31 0.26 0.30 0.315 0.28 0.27 0.30

10 vs sham 0.41 0.275 0.29 0.295 0.315 0.30 0.30

10 vs 80 0.33 0.345 0.28 0.285 0.28 0.285

80 vs sham 0.45 0.30 0.30 0.31 0.295 0.31 0.30 0.26 0.30

Median All 0.32 0.31 0.30 0.30 0.305 0.2875 0.3075 0.28 0.30

Median ES across all slots for the difference between amplitudes was greatest at 10 pps, and for the

difference between 2.5 pps and sham.

A somewhat problematic finding here is that there were already some significant differences at

baseline (in Slot 1). Although for the amplitude comparisons the ES of the baseline differences was in

general less than that for those in subsequent slots (except at 2.5 pps from Slot 5 onwards), in

contrast, for the frequency comparisons, median ES was greatest at baseline, reducing thereafter.

This issue is discussed further below.

3. Changes over time: Significant differences in HRV measures and HRNL between baseline (Slot 1)

and post‐TEAS (Slot 6), for the different TEAS amplitudes and frequencies

Table 5 lists those measures which exhibited significant changes between baseline and post‐TEAS,

for different frequencies and amplitudes of stimulation. As previously stated (Mayor et al. 2019), by

Slot 8 several participants were fatigued or experiencing discomfort, so for a first analysis, only the

difference between baseline and Slot 6 is considered here. The measures are separated into ‘PNS‐

10 Although amplitudes were compared for each stimulation frequency separately, and frequencies for high and low amplitudes taken together, the resulting difference in sample sizes would not influence the ESs found (Sullivan & Feinn 2012).

14

like’ measures, whose increase probably indicates more parasympathetic activation, and ‘SNS‐like’

measures, whose increase probably indicates more sympathetic activation. However, this is not

altogether clear‐cut in the literature, with increases in some SNS‐like measures more likely to

represent decreased parasympathetic activity, or a different balance between sympathetic and

parasympathetic activity (Heathers 2014).

Table 5. HRV and HRNL measures exhibiting significant changes between baseline and post‐TEAS.

Changes in measures in red type were in the opposite direction to those expected from the

literature or prior analysis. ES is shown for each significant result using the Wilcoxon signed ranks

test, as well as counts of increases and decreases over time. Maxima in each section of the Table are

shown in bold, minima underlined.

PNS‐like ES Inc:dec SNS‐like ES Inc:dec

sham PNS HF% HFnu SampEn/MSE1

0.44 0.49 0.49 0.33

19:44 19:44 20:43 22:41

HRmean HRmax LFlog LF% LFnu LF/HF SD2/SD1 DFA α1 MSE8 MSE9 MSE19

0.26 0.34 0.32 0.48 0.49 0.40 0.35 0.47 0.26 0.29 0.38

40:23 42:21 37:26 45:18 43:20 43:20 41:22 47:16 38:25 41:22 19:44

2.5 pps (high amp)

SDNN SDHR HF% HFnu TotPwr MSE2

0.64 0.57 0.48 0.48 0.67 0.35

26:5 24:7 7:24 8:23 25:6 11:20

SI LFabs LFlog LF% LFnu LF/HF SD2 SD2/SD1 DFA α1 pD2

0.44 0.71 0.70 0.44 0.48 0.52 0.71 0.40 0.44 0.36

8:23 27:4 27:4 23:8 23:8 23:8 27:4 22:9 20:11 21:9

2.5 pps (low amp)

PNS NNxx pNNxx HF% HFnu

0.48 0.39 0.55 0.59 0.55

10:22 12:18 10:20 8:24 8:24

LF% LFnu LF/HF SD2/SD1 MSE9 MSE16

0.50 0.54 0.53 0.43 0.48 0.37

22:10 24:8 24:8 22:10 22:10 21:11

2.5 ALL PNS SDNN SDHR HF% HFnu TotPwr SampEn/MSE1

0.26 0.37 0.37 0.54 0.52 0.40 0.26

25:38 40:23 41:22 15:48 47:16 41:22 23:40

LFabs LFlog LF% LFnu LF/HF SD2 SD2/SD1 DFA α1 MSE12 MSE18 pD2

0.56 0.50 0.47 0.52 0.53 0.45 0.42 0.42 0.27 0.25 0.32

45:18 45:18 45:18 47:16 47:16 44:19 44:19 38:25 40:23 39:24 38:23

15

10 pps (high amp)

SDNN SDHR HF% HFnu

0.35 0.43 0.46 0.45

21:11 22:10 8:24 8:24

SI LFabs LFlog LF% LFnu LF/HF SD2 DFA α1 MSE4 MSE16

0.39 0.37 0.41 0.40 0.45 0.37 0.37 0.52 0.42 0.39

9:23 23:9 23:9 23:9 24:8 24:8 22:10 24:8 22:10 11:21

10 pps (low amp)

RR ApEn

0.37 0.40

20:11 10:21

HRmean pD1+D2

0.36 0.44

11:20 9:22

10 ALL HF% HFnu

0.25 0.25

24:39 23:40

LF.Hz LF% LFnu LF/HF SD2/SD1

0.28 0.26 0.25 0.25 0.27

20:38 40:23 40:23 40:23 40:23

80 pps (high amp)

HF% HFnu

0.63 0.61

7:23 8:22

LF% LFnu LF/HF SD2/SD1 DFA α1

0.61 0.61 0.48 0.40 0.52

23:7 22:8 22:8 19:11 23:7

80 pps (low amp)

HF% HFnu SampEn/MSE1

0.50 0.47 0.36

8:23 8:23 13:18

LFabs LFlog LF% LFnu LF/HF SD2/SD1 DFA α1 MSE9 MSE19

0.41 0.44 0.43 0.47 0.40 0.45 0.49 0.39 0.56

22:9 22:9 24:7 23:8 23:8 23:8 25:6 22:9 22:9

80 ALL PNS HF% HFnu

0.30 0.56 0.55

27:34 15:46 16:45

HRmin LFabs LFlog LF% LFnu LF/HF SD2/SD1 DFA α1 ShannEn MSE11 MSE19

0.35 0.30 0.39 0.52 0.55 0.45 0.44 0.50 0.28 0.28 0.34

24:37 41:20 41:20 47:14 45:16 45:16 42:19 48:13 38:23 39:22 37:24

ALL PNS SDNN SDHR HF.Hz HF% HFnu TotPwr SampEn/MSE1

0.28 0.24 0.25 0.14 0.46 0.45 0.22 0.21

98:152 154: 96 149:101 101:122 73:177 75:175 151:99 104:146

SI HRmin HRmax LF.Hz LFabs LFlog LF% LFnu

0.19 0.19 0.16 0.13 0.31 0.35 0.43 0.45

104:146 111:139 142:108 105:126 160:90 160:90 177:73 175:75

16

MSE2 0.13 106:144 LF/HF SD2 SD2/SD1 DFA α1 DFA α2 ShannEn MSE8 MSE9

0.41 0.27 0.40 0.42 0.13 0.22 0.16 0.16

175:75 156:94 167:83 170:80 139:111 141:109 139:110 151:98

Thirteen different measures occur in the PNS‐like column in the above Table, 24 in the SNS‐like

column. Those measures occurring most frequently in each category (i.e. in the upper quartile of

counts, > 4 for the PNS‐like measures, > 6.25 for the SNS‐like measures) are shown in Table 6.

Table 6. PNS‐like and SNS‐like measures occurring most frequently

(in the upper quartiles of counts) in Table 5.

PNS‐like SNS‐like

Measure N median ES Measure N median ES

PNS HF% HFnu

5 10 10

0.3 0.49 0.48

LFlog LF% LFnu LF/HF SD2/SD1 DFA α1

7 10 10 10 9 8

0.41 0.455 0.485 0.43 0.40 0.47

4. Significant differences in HRV measures and HRNL within the same time slots and over time:

Comparing effect sizes

Table 7 displays the measures occurring most frequently (in the upper quartiles of counts) in Tables

3 and 5.

Table 7. HRV measures and HRNL indices occurring most frequently

(in the upper quartiles of counts) in Tables 3 and 5.


Median ES comparing frequencies

Median ES comparing Slots 1 and 6

General PNS SNS

0.27 0.27

0.29 0.29

0.3

Time domain rMSSD

NNxx pNNxx

0.26 0.27 0.275

0.30 0.27 0.28

Frequency domain

HFabs HFlog HF% HFnu LFabs LFlog LF% LFnu

0.31 0.31 0.345 0.35 0.265 0.265 0.34 0.345

0.295 0.31 0.305 0.33 0.34 0.33

0.49 0.48 0.41 0.455 0.485

17

LF/HF 0.34 0.28 0.43

Nonlinear/ entropy

SD1 SD2/SD1 DFA α1 ShannEn SampEn/MSE1 MSE5 MSE7 MSE19

0.26 0.295 0.31 0.27 0.25 0.35 0.34 0.375

0.30 0.37 0.285 0.33 0.315 0.295 0.33 0.34

0.40 0.47

HRNL indices D1+D2 0.30 0.27

Median ALL 0.30 0.30 0.455

Clearly, ES for changes in HRV measures over time is greater than ES for differences between either

stimulation amplitude or frequency.

Table 7. Commentary Note In Table 5, several HRV measures are shown in red. SDNN, SDHR, TotPwr and possibly ApEn were considered from the literature to be PNS‐like, SI, LF.Hz and HRmin as SNS‐like (although there is little information available on LF.Hz, as mentioned in Footnote 13 below). However, these all showed changes predominantly in the opposite direction to that expected from the changes in the other, often better‐established measures that were found for the same comparisons. MSE16 and MSE19 showed more decreases than increases with some TEAS conditions (10 pps high amplitude and sham, respectively), and more increases than decreases for others (2 pps low amplitude and 80 pps ALL/low amplitude, respectively). As already mentioned (Footnote 9, p. 11), there is lack of agreement in the literature on the allocation of different MSE scales as PNS‐like or SNS‐like. If these ‘problematic’ measures are removed, median ES for all significant changes at a particular TEAS frequency and amplitude taken together either increases or remains unchanged for the SNS‐like measures for all except sham and 10 pps TEAS (disregarding amplitude). Correspondingly, median ES either increases or remains unchanged for the PNS‐like measures for all except high‐amplitude 2.5 pps and low‐amplitude 10 pps TEAS. Thus, in general, the problematic measures appeared to contribute less to ES than the other measures, irrespective of their predominant directions of change.

5. Numbers of significant differences in HRV measures and HRNL indices when comparing TEAS

amplitudes and frequencies within slots

Counts of significant differences found with the Wilcoxon signed‐ranks test gives some results that

are perhaps easier to understand than some of the above, as shown in Table 8. The upper part of

the Table shows comparisons between amplitudes for each frequency, the lower part comparisons

between pairs of frequencies, ignoring amplitude.

18

Table 8. Numbers of significant differences in the different slots,

for all measures and indices taken together.

Comparison Slot 1 Slot 2 Slot 3 Slot 4 Slot 5 Slot 6 Slot 7 Slot 8 Total

High/low 2.5 pps

9 6 12 9 5 8 4 8 61

High/low 10 pps

20 17 20 17 15 22 17 12 140

High/low 80 pps

2 3 1 1 5 3 2 2 19

Total All 31 26 33 27 25 33 23 22 220

Comparison Slot 1 Slot 2 Slot 3 Slot 4 Slot 5 Slot 6 Slot 7 Slot 8 Total

2.5 vs sham 2 0 0 1 0 0 12 7 22

2.5 vs 10 5 0 2 0 2 1 0 1 11

2.5 vs 80 8 10 5 9 6 6 0 1 45

10 vs sham 1 0 2 7 0 11 15 12 48

10 vs 80 0 15 12 9 8 0 1 0 45

80 vs sham 2 4 12 12 10 1 2 3 46

Total All 18 29 33 38 26 19 30 24 217

The numbers of significant differences for both amplitude and frequency comparisons are

comparable (220 vs 217), but patterns are very different for the different frequencies: amplitude

appears to have had a much greater differential effect on HRV and HRNL at 10 pps than at the other

frequencies, for example, and there are somewhat fewer significant differences between 2.5 pps

and sham, or 2.5 and 10 pps, than between the other frequency combinations. As found in our

previous poster (Mayor et al. 2019), there were fewer significant differences at baseline than in

subsequent slots, but the finding there (Table 6, p. 17) that there were considerably more significant

differences during stimulation than before or after is less marked here for all frequencies considered

together. However, although it is still the case for all comparisons involving 80 pps, for 10 pps (and

2.5 pps) vs sham, greater numbers of significant differences were present after than during or before

stimulation.

An increase in PNS‐like measures or a decrease in SNS‐like measures relative to sham following TEAS

could both be considered a ‘good’ outcome, while a decrease in PNS‐like measures or an increase in

SNS‐like measures relative to sham following TEAS could both be considered a ‘good’ outcome.

Table 9 shows the relative number of ‘good’ and ‘bad’ outcomes, post‐stimulation (i.e. in Slots 6 to

8) when comparing results for the three active stimulation frequencies relative to sham stimulation,

at both high and low amplitudes.

Table 9. Numbers of significant differences from sham in Slots 6 to 8.

2.5 pps vs sham 10 pps vs sham 80 pps vs sham

> sham < sham > sham < sham > sham < sham

High amp PNS‐like 4 3 12 13 2 0

SNS‐like 4 6 1 8 2 1

Low amp PNS‐like 5 3 24 1 1 1

SNS‐like 0 10 0 14 1 2

19

Stimulation at 2.5 pps resulted in 15 more good than bad outcomes, at 10 pps in 45 more good than

bad outcomes, and at 80 pps in just 2 more good than bad outcomes. As stated in our earlier poster

presentation (Mayor et al. 2019), TEAS at 10 pps was more likely to decrease the stress response

than at 2.5 or 80 pps, particularly following stimulation.

Table 9. Commentary Note Comparisons were also made in all slots between active frequencies and sham, with the data for high and low amplitudes of the active frequency considered separately. This resulted in 150 significant differences in HRV measures and HRNL indices, with a further 149 significant differences for comparisons between the active frequencies themselves (with stimulation at both frequencies being either at high or low amplitude, so that mixing amplitudes when comparing frequencies would not be a confounding factor). Thus the total of significant differences between stimulation frequencies when taking amplitude into account was 299 (6 pairwise comparisons), considerably more than the numbers of significant differences between amplitudes when taking frequency into account (220, based on only one comparison).

When taking amplitude into consideration, counts of significant differences in all slots were as

shown in Table 10.

Table 10. Numbers of significant differences in HRV measures and HRNL indices

when comparing stimulation frequencies in all slots.

Amplitude 2.5 vs sham 2.5 vs 10 2.5 vs 80 10 vs sham 10 vs 80 80 vs sham ALL

High 31 19 24 15 31 32 152

Low 8 16 28 47 31 17 147

Totals 39 35 52 62 62 49 299

For the comparisons between the active frequencies, there were very similar numbers of significant

differences at both high and low amplitudes. For two of the comparisons with sham, there were

significantly more significant differences at higher amplitude (using the Binomial test, p < 10‐3 for 2.5

pps vs sham and p=0.044 for 80 pps vs sham), but significantly more at lower amplitude for 10 pps vs

sham (p < 10‐4). Highest numbers of significant differences were found for comparisons between 10

pps and sham, and 10 pps vs 80 pps, fewest for 2.5 pps vs 10 pps.

Those HRV measures and HRNL indices occurring most commonly in these comparisons (in the upper

quartile of counts, i.e. N ≥ 7) are shown in Table 11.

20

Table 11. Incidence of most frequently occurring HRV measures and HRNL indices

in Table 10 (N ≥ 7).

Measure All Amplitude Comparison

Between active freqs With sham

SD2/SD1 [~S] 14 High 7 4

Low 2 1

ShannEn [S] 14 High 2 3

Low 7 2

RMSSD [P] 11 High 1 5

Low 2 3

SD1 [P] 11 High 1 5

Low 2 3

PNS [P] 10 High 2 4

Low 2 2

HFabs [P] 10 High 2 6

Low 0 2

MSE5 [?] 10 High 0 0

Low 4 6

MSE19 [?] 10 High 1 3

Low 2 4

SampEn/MSE1 [P] 8 High 3 1

Low 3 1

MSE3 [?] 8 High 2 2

Low 2 2

MSE7 [?] 8 High 2 1

Low 2 3

HFlog [P] 7 High 2 3

Low 0 2

MSE10 [?] 7 High 1 1

Low 4 1

D2 [P] 7 High 4 3

Low 0 0

pD2 [S] 7 High 3 3

Low 1 0

D1+D2 [P] 7 High 1 1

Low 2 3

Totals 149 High 34 45

Low 35 35

21

6. Graphical illustrations of changes in median values of HRV measures and HRNL indices over

time, showing TEAS amplitude and frequency effects

The Figures below show median values of the major measures listed in Table 7, plotted against time

(Slots 1 to 8). The first set (Figures 1.1 to 1.14) compare values at high and low stimulation

amplitudes for the three active stimulation frequencies. The second set (Figures 2.1 to 2.14)

compare values for sham stimulation and the active frequencies, separately for high and low

amplitude stimulation.11 Brief ‘commentary notes’ are included following each Figure.

6.1. Graphical illustrations of changes in median values of HRV measures and HRNL indices over time,

showing values at high and low stimulation amplitudes for each active frequency and median values

for all frequencies taken together

The percentage differences (‘%Diff’) in each graph are calculated as the medians of the differences

between the values of the measure under consideration at high and low amplitudes, normalised as

percentages of the median of high and low amplitude values in Slot 1 (i.e. at baseline). This method

of normalising the data was adopted as a work‐around to reduce the carry‐over effects in

subsequent slots of pre‐existing differences at baseline (see above, p. 13).

6.1.1. General HRV overview measures

11 Note that that these graphs show differences between median group values, not median values of the differences for each participant, so that sometimes the relationship among ‘high’, ‘low’ and ‘ALL’ plots will appear somewhat counter‐intuitive.

22

Figure 1.1. The ‘benchmark’ general HRV measures, PNS [P] and SNS [S] indices (CV > 0.4).

Figure 1.1. Commentary Note At all stimulation frequencies except 80 pps, median PNS was consistently greater when stimulation was at (or accepted as ‘strong but comfortable’ at) lower amplitudes of TEAS, and less for stimulation at higher amplitudes. This was also true for all active frequencies taken together (Figure bottom left). Correspondingly, SNS values were greater at 10 pps – and for all frequencies taken together – at higher amplitudes of stimulation, but this was reversed at 80 pps and the pattern over time was not consistent for 2.5 pps (Figure top right). Both PNS and SNS show greatest consistent separation between the two amplitude plots at 10 pps, with PNS showing least consistent separation at 80 pps and SNS showing least at 2.5 pps. Note that PNS was in general higher at low amplitude (except sometimes at 80 pps), and SNS higher at low amplitude (except immediately following cessation of stimulation).

23

6.1.2. Time‐domain HRV measures

For comparison, graphs are provided for four time‐domain HRV measures with CV < 0.4, mean RR [P]

and rMSSD [P] (Figure 1.2) and NNxx [P] and pNNxx [P] (Figure 1.3).

Figure 1.2. Two time‐domain HRV measures, RR [P] and rMSSD [P].

24

Figure 1.2. Commentary Note There are intriguing ‘crossovers’ of the high‐ and low‐amplitude plots early on during sessions in both RR and rMSSD at 2.5 pps. There are further crossovers in RR (at 10 pps) and rMSSD (at 80 pps). RR shows greatest consistent separation between the amplitude plots at 80 pps, rMSSD at 10 pps. Note that to a certain extent stimulation amplitude has opposite effects on these two HRV measures.

Figure 1.3. Two further time‐domain HRV measures, NNxx [P] and pNNxx [P].

25

Figure 1.3. Commentary Note NNxx and pNNxx are quite similar, related measures (Bigger et al. 1988). Again, cross‐overs are found at 2.5 and 80 pps, with the greatest consistent separation between amplitude plots at 10 pps and the least at 80 pps. At 10 pps, clearly low stimulation amplitude results in higher values for both these measures, but at 2.5 pps, except during the first two slots, higher amplitude TEAS results in lower values of NNxx and pNNxx.

6.1.3. Frequency‐domain HRV measures

26

Figure 1.4. Two frequency‐domain HRV measures, LF% [S] and the LF/HF ratio [S].

Figure 1.4. Commentary Note LF% and LF/HF are, like NNxx and pNNxx, closely related HRV measures. Both show greatest consistent separation between the amplitude plots at 10 pps, least at 80 pps, with no cross‐overs of the two plots for either measure. Note that both LF% and LF/HF SNS‐like are both ‘SNS‐like’ measures, are greater for high amplitude stimulation at all frequencies, and increase over time in this study.

27

Figure 1.5. Two further frequency‐domain HRV measures, HFabs [P] and HF% [P].

Figure 1.5. Commentary Note HFabs and HF% are closely related HRV measures, well established as ‘PNS‐like’ in the literature. Here their amplitude plots show maximal separation at 10 pps, and almost consistently indicate greater values of the two measures for lower amplitude stimulation. HF% shows a clear decrease over time, whereas HFabs does not.

28

6.1.4. Nonlinear and entropy HRV measures

Figure 1.6. Two measures of HRV complexity derived from Poincaré plots, SD1 [P] and the SD2/SD1

ratio [~S].

Figure 1.6. Commentary Note Although not always considered strictly speaking ‘nonlinear’ (Brennan et al. 2001), the standard deviations of the Poincaré plot axes are often included in discussions of nonlinear HRV measures. SD1 is clearly identical to rMSSD (Ciccone et al. 2017) and is PNS‐like. Whereas SD1

29

was more often greater for low amplitude stimulation, the opposite was true for the SD2/SD1 ratio, more likely to be SNS‐like measure. Both measures show greatest separation between amplitude plots at 10 pps, and least and 80 pps. SD2/SD1 (like LF/HF) appears to have increased over time.

Figure 1.7. Two measures of HRV complexity derived from detrended fluctuation analysis (DFA), DFA

DFA α1 [S] and DFA α2 [S?].

30

Figure 1.7. Commentary Note DFA α1 shows much greater differences between the two amplitude plots than DFA α2, with higher values for stronger simulation, except perhaps at 80 pps. DFA α2 appears perhaps to be higher for stronger stimulation post‐TEAS, except at 2.5 pps, but this pattern is not altogether clear. DFA α1 appears to increase consistently over time.

Figure 1.8. Two measures of HRV complexity, ShannEn [P] and SampEn [P].

31

Figure 1.8. Commentary Note Differences with amplitude for both these measures appear most consistent at 80 pps. However, they also change over time in opposite directions at this frequency.

Figure 1.9. Scales 5 [P?] and 19 [S?] of the multiscale entropy measure (MSE) based on SampEn [P].

32

Figure 1.9. Commentary Note In these graphs, both MSE5 and MSE19 are more often greater at higher amplitudes, with most separation between the amplitude traces at 10 pps for both MSE5 and MSE19. This may contradict the Taiwanese hypothesis that lower scales of SampEn‐based MSE are more PNS‐like and higher scales more SNS‐like (see discussion in Mayor et al. 2019, p. 14). MSE19 appears to increase more than MSE5 over time, but in some other respects their patterns of change are quite similar.

6.1.5. The ‘problematic’ HRV measures from Table 5

Graphs of those measures listed in red in Table 5 are provided here, for comparison.

33

Figure 1.10. Two time‐domain HRV measures based on standard deviations

of the RR interval and heart rate, SDNN [P] and SDHR [P].

Figure 1.10. Commentary Note As shown in Table 5, both these indices increase over time between baseline and Slot 6 at 2.5 and 10 pps, particularly for higher amplitude stimulation, in contrast to a number of other PNS‐like measures shown in these graphs. Also somewhat unexpectedly for PNS‐like measures, SDNN was greater for higher amplitude 2.5 pps TEAS, although greater for lower amplitude TEAS at the other two active frequencies (SDHR – a less used measure12 – follows a similar pattern, although not at 10 pps). Differences between high and low amplitudes are most marked at 2.5 pps.

12 Mika Tarvainen. Personal communication, 8 May 2019.

34

Figure 1.11. Minimum heart rate (HRmin) [S] and Total HRV power (TotPwr) [P].

Figure 1.11. Commentary Note HRmin was almost consistently higher at lower amplitudes of stimulation, and decreases over time – as found for a number of PNS‐like measures. As its presence in red in Table 5 indicates, this suggests that HRmin in this study has itself acted more as a PNS‐like than a SNS‐like measure. Greatest separation of the two HRmin amplitude plots was found at 80 pps, for which the greatest decrease between Slots 1 and 6 also appears. In that it as in general greater for lower amplitude TEAS, TotPwr behaved as expected for a PNS‐like measure, except at 2.5 pps, where it was greater for higher amplitude stimulation. However, it increased over time for all stimulation frequencies. This is a genuine anomaly, but one explanation might be that TotPwr is defined as the sum of LFabs, HFabs and the absolute power in the very low frequency band, VLFabs; the last of these cannot be accurately computed for 5‐minute recordings (Malik 1996).

35

Figure 1.12. Peak frequencies in the HRV HF and LF bands, HF.Hz [P?] and LF.Hz [S?].13

13 HF.Hz – and indeed HF power – may not be meaningful If the respiration rate falls within the HRV LF band. This may be the case for respiratory rate at rest in very physically fit people. In principle LF.Hz should be around 0.1 Hz, but there is little information available in the literature about this measure and how to interpret its changes (Mika Tarvainen. Personal communication, 8 May 2019).

36

Figure 1.12. Commentary Note HF.Hz was greater for stronger stimulation at all three active TEAS frequencies, except after TEAS at 10 pps. LF.Hz, correspondingly, was lower for lower amplitude TEAS, except initially during stimulation at 10 and 80 pps, and for much of the time during stimulation 2.5 pps. This suggests that the tentative attributions of PNS‐like and SNS‐like suggested recently (Mayor et al. 2019) should in fact be reversed. However, over time both HF.Hz and LF.Hz decrease (cf Table 5), so re‐attributing HF.Hz as SNS‐like may not be completely straightforward, given that other SNS‐like measures tend rather to increase over time.

37

Figure 1.13. SI, a general ‘Stress index’ [S] in Kubios HRV software, and ApEn, a measure of entropy

[P?].14

Figure 1.13. Commentary Note SI was greater at lower amplitudes of 2.5 pps stimulation, but in general greater at higher amplitudes for the other two active TEAS frequencies – as was also found for RR, SD1, SDNN, SDHR, TotPwr and ShannEn (the underlined measures appear in Table 5 in red, as does SI). However, it decreased over time between Slots 1 and 6, which was not the case for either these other six measures or other more established SNS‐like measures such as SNS, LF%, LF/HF, SD1/SD2 and DFA α1. Thus the suggestion that SI is itself SNS‐like appears somewhat problematic. ApEn – on which SampEn and MSE are ultimately based (Costa et al. 2005; Richman & Moorman 2000) – appears to be higher for stronger stimulation at all frequencies of TEAS, more consistently so than SD2/SD1, and far more so than ShannEn and SampEn. It is unclear why it dips so much in Slot 6 (cf Table 5, where it appears in red).

14 HF.Hz – and indeed HF power – may not be meaningful If the respiration rate falls within the HRV LF band. This may be the case for respiratory rate at rest in very physically fit people. In principle LF.Hz should be around 0.1 Hz, but there is little information available in the literature about this measure and how to interpret its changes (Mika Tarvainen. Personal communication, 8 May 2019).

38

6.1.6. Heart rate nonlinearity indices

Figure 1.14. Two HRNL indices, D2 [P] and D1+D2 [P].

Figure 1.14. Commentary Note Both HRNL indices – but particularly D1+D2 – are greater at lower simulation amplitudes, like most of the PNS‐like HRV measures, and both appear to decrease over time.

39

Table 12 (below, p. 49) summarises the percentage differences between amplitudes for the various

measures and indices shown in Figure 1.1 to Figure 1.14.

6.2. Comparing percentage differences (%Diff) between sham and the different active frequencies for

median values of HRV measures and HRNL indices

The following graphs each show three plots of percentage differences (‘%Diff’) between the values

of a particular HRV measure or HRNL index at the three active frequencies (2.5, 10 and 80 pps) and

the values for sham stimulation. %Diff was calculated as the difference between the median value of

the measure under consideration from the median value for sham, divided by the median value for

sham and multiplied by 100. The numbers shown for the three frequencies in each graph are the

medians of the absolute percentage differences in the measure between the active frequencies and

sham for all eight time slots. Results are shown separately for high and low amplitudes, and are

normalised as percentages of the median sham value for that HRV measure or HRNL index.

Figure 2.1. The ‘benchmark’ general HRV measures, PNS [P] and SNS [S] indices.

Figure 2.1. Commentary Note At high amplitude, both PNS and SNS showed more negative differences from sham at 80 pps, with more positive differences from sham at the other two active frequencies – particularly 10 pps. At low amplitude, this pattern is reversed, with largest negative differences from sham at 10 pps. For both HRV measures, differences from sham decreased over time at high amplitude, but not at low amplitude.

40

Figure 2.2. Two time‐domain HRV measures, RR [P] and rMSSD [P].

Figure 2.2. Commentary Note At high amplitude, RR %Diff from sham increased most markedly at 80 pps, although %Diff was also positive for the other two active frequencies, while at low amplitude RR %Diff from sham decreased initially at 2.5 and 10 pps. rMSSD showed largest negative %Diff from sham at 10 pps with strong stimulation, but with less strong stimulation rMSSD showed largest positive %Diff from sham at the same frequency.

41

Figure 2.3. Two further time‐domain HRV measures, NNxx [P] and pNNxx [P].

Figure 2.3. Commentary Note Both NNxx and pNNxx %Diff from sham showed opposing trends for 2.5/80 and 10 pps, increasing for 10 pps at low amplitude but decreasing at high amplitude, with the opposite happening for 2.5 and 80 pps stimulation.

Figure 2.4. Two frequency‐domain HRV measures, LF% [S] and the LF/HF ratio [S].

Figure 2.4. Commentary Note LF% %Diff from sham was greatest at 2.5 pps for high amplitude TEAS, but at 10 pps for low amplitude TEAS, with 80 pps somewhere between the two, for the most part. LF/HF shows greatest positive %Diff from sham at 10 pps for high amplitude TEAS, and greatest negative %Diff from sham at the same frequency for low amplitude TEAS.

42

Figure 2.5. Two further frequency‐domain HRV measures, HFabs [P] and HF% [P].

Figure 2.5. Commentary Note Both HFabs and HF% show somewhat similar patterns of %Diff change over time, with the 80 pps plots having greater values than the 10 pps and 2.5 pps plots during high‐amplitude stimulation, but lower values during low‐amplitude stimulation. Greatest %Diff from sham was for 10 pps for both measures at both amplitudes.

43

Figure 2.6. Two measures of HRV complexity derived from Poincaré plots, SD1 [P] and the SD2/SD1

ratio [~S].

Figure 2.6. Commentary Note SD1 (equivalent to rMSSD) showed largest negative %Diff from sham at 10 pps with strong stimulation, but with less strong stimulation showed largest positive %Diff from sham at the same frequency. The plots for 2.5 pps and 80 pps stimulation are somewhat similar and slightly separated from the plot for 10 pps. SD2/SD1 showed an opposite effect, with the plots for 2.5 and 10 pps closer to each other and showing positive %Diff from sham at high amplitude (the plot for 80 pps having a negative %Diff from sham, overall), but at low amplitude the plot for 10 pps (and that for 2.5 pps to some extent) having a negative %Diff from sham, and 80 pps having a positive %Diff from sham.

Figure 2.7. Two measures of HRV complexity derived from detrended fluctuation analysis (DFA), DFA

DFA α1 [S] and DFA α2 [S?].

44

Figure 2.7. Commentary Note DFA α1 shows a pattern that is now becoming familiar: at both amplitudes, the plots for 2.5 and 10 pps %Diff from sham are more bunched together, and directions of %Diff from sham are opposite for the two amplitudes. The plots for DFA α2 again show somewhat opposite effects for the two amplitudes, but are more difficult to interpret – not perhaps surprisingly, as DFA α2 produced only one significant result in this study, with a very low effect size (Table 5).

Figure 2.8. Two measures of HRV complexity, ShannEn [P] and SampEn [P].

Figure 2.8. Commentary Note Like DFA α1, ShannEn shows a familiar pattern: at both amplitudes, the plots for 2.5 and 10 pps %Diff from sham are more bunched together, and directions of %Diff from sham are opposite for the two amplitudes. A similar pattern – although less defined – was found for SampEn/MSE1.

45

Figure 2.9. Scales 5 [P?] and 19 [S?] of the multiscale entropy measure (MSE) based on SampEn [P].

Figure 2.9. Commentary Note These MSE graphs are both difficult to interpret, although it is possible to say that there is more similarity between the two amplitude plots for MSE5 than for MSE19, and that %Diffs from sham are larger for the latter.

Figure 2.10. Two time‐domain HRV measures based on standard deviations

of the RR interval and heart rate, SDNN [P] and SDHR [P].

46

Figure 2.10. Commentary Note The SDNN plots are closer together for 10 and 80 pps, with more separation between these and the plot for 2.5 pps, at both stimulation amplitudes. %Diff from sham at 2.5 pps was positive at high amplitude and negative at low amplitude, with %Diff from sham at 10 and 80 pps showing an opposite pattern. The SDHR plots are more similar at 2.5 and 10 pps, but for those frequencies again the %Diff signs are opposite for each stimulation frequency.

Figure 2.11. Minimum heart rate (HRmin) [S] and Total HRV power (TotPwr) [P].

Figure 2.11. Commentary Note HRmin shows a positive %Diff from sham at low amplitude TEAS, but negative %Diff at high amplitude, with %Diff from sham at 80 pps being more strongly negative during low amplitude than high amplitude stimulation. TotPwr %Diff from sham was consistently positive for 2.5 pps at high amplitude, and consistently negative at low amplitude, with the plots for 10 and 80 pps being more closely bunched together.

47

Figure 2.12. Peak frequencies in the HRV HF and LF bands, HF.Hz [P?] and LF.Hz [S?].

Figure 2.12. Commentary Note These graphs are difficult to interpret in any meaningful way (see too Note 13, p. 35).

Figure 2.13. SI, a general ‘Stress index’ [S] in Kubios HRV software, and ApEn, a measure of entropy

[P?].

48

Figure 2.13. Commentary Note SI appears to be greater than for sham for strong 10 pps stimulation, but less than for sham at less strong stimulation at the same frequency, while it was less than for sham for strong 2.5 pps stimulation and greater than for sham for less strong stimulation at that frequency. %Diff from sham for ApEn is difficult to interpret, although it appears to increase over time.

Figure 2.14. Two HRNL indices, D2 [P] and D1+D2 [P].

Figure 2.14. Commentary Note There do not at first sight appear to be any easily recognisable patterns in %Diff from sham for D2 or D1+D2.

7. Summarising %Diff between median values of HRV measures and HRNL indices over time,

showing TEAS amplitude and frequency effects

The median %Diff results shown in the above graphs were collated. The results are summarised

below.

Table 12 summarises the percentage differences in the 28 measures and indices shown in Figure 1.1

to Figure 1.14 between high and low stimulation amplitudes, showing the medians of all such

differences for each active frequency, as well as counts of how many times maxima and minima

occurred for all the measures and indices at each active frequency (for example, the maximum of

49

highest %Diff in HFabs values between frequencies occurred at 10 pps, the minimum or lowest at 80

pps).

Table 12. Percentage differences in the 28 measures and indices shown in Figure 1.1 to Figure 1.14

between high and low stimulation amplitudes, with the medians for all differences and counts

of how many times maxima (Max) and minima (Min) occurred for all the measures and indices

at each active frequency. Significance values using the Binomial test are shown

for ratios of maxima to minima.

Measure 2.5 pps p 10 pps p 80 pps p

PNS‐like Median Median Median

24.5 27 14

N Max N Min

N Max

N Mini N Max N Min

5 5 ns 12 4 ns 2 10 0.039

SNS‐like Median Median Median

15.95 24.45 8.8

N Max N Min

N Max

N Min N Max N Min

1 3 ns 6 3 ns 3 5 ns

ALL Median Median Median

17.8 26.55 10.1

N Max N Min

N Max

N Min N Max N Min

6 8 ns 18 7 0.043 5 15 0.041

Table 13 summarises median percentage differences from sham for the 28 measures and indices

shown in Figure 2.1 to Figure 2.14, for the three different active frequencies, with results split by

stimulation amplitude (defined as high or low relative to the median amplitude for that frequency).

Also shown are the counts of how many times maxima and minima occurred for all the measures

and indices at each active frequency.

50

Table 13. Median percentage differences from sham for the 28 measures and indices shown in

Figure 2.1 to Figure 2.14, for the three different active frequencies, with results split by stimulation

amplitude (high or low relative to the median for that frequency). Counts are also shown of how

many maxima and minima occurred for all the measures and indices at each active frequency.

Significance values using the Binomial test are shown for ratios of maxima and of minima

for the two stimulation amplitudes.

Measure Descriptives 2.5 pps p 10 pps p 80 pps p

mdn %Diff Hi

mdn %Diff Lo

mdn %Diff Hi

mdn %Diff Lo

mdn %Diff Hi

mdn %Diff Lo

PNS‐like

Median 13.4 7.15 6.1 25.5 8 9.15

N maxima 10 4 ns 4 14 0.031 4 0 ns

N minima 4 7 ns 10 1 0.012 4 11 ns

SNS‐like Median 11.2 8.35 8.8 13.9 5.3 7.55

N maxima 4 0 ns 3 7 ns 3 3 ns

N minima 1 6 ns 3 0 ns 6 4 ns

ALL Median 11.9 7.4 7.25 16.8 7.15 9.05

N maxima 14 4 0.031 7 21 0.013 7 3 ns

N minima 5 13 ns 13 1 0.002 10 15 ns

From Table 12 it can be seen that most maxima were found at 10 pps, and most minima at 80 pps,

indicating that greater differentiation between stimulation amplitude effects occurred at 10 pps

and least at 80 pps– with 2.5 pps showing mostly an intermediate difference between amplitude

effects. At 10 pps, relatively greater numbers of maxima than minima – and greater median

differences – were found for PNS‐like rather than SNS‐like measures and indices, and similarly for

greater numbers of minima than maxima at 80 pps, although here lower median differences were

found for the SNS‐like rather than PNS‐like measures and indices.

In Table 13, most (and greatest) differences from sham were found for 10 pps TEAS at low amplitude

(particularly for PNS‐like measures and indices), and fewest for low‐amplitude stimulation at the

other two active frequencies. Amplitude did not affect differences from sham at 80 pps in any major

way, but at 2.5 pps more and greater differences from sham were found at high rather than low

amplitude.

Those measures and indices in the above graphs with upper quartile median differences in are listed

in Table 14.

51

Table 14. HRV measures and HRNL indices in the upper quartile of median differences

in Figures 1.1 to 1.14 and 2.1 to 2.14.

HRV/HRNL 2.5 pps 10 pps 80 pps

Hi vs Lo Diff to sham hi

Diff to sham lo


Diff to sham lo


Diff to sham lo

N “y”

PNS‐like

PNS index y y y y n y y y n 7

NNxx y y n y y y y n y 7

pNNxx y y n y y n n y y 6

HFabs n n n y y y n n n 3

HF% y n y n y y n n y 5

TotPwr y y y n n n y y y 6

D2 y y y n y y y y y 8

D1+D2 n y y y y y n y y 7

SNS‐like

SNS index n n n y n n y y n 3

HF.Hz n n n n n n y n n 1

LF/HF y y y y y y y y y 9

Five PNS‐like HRV measures , two PNS‐like HRNL indices and one SNS‐like measure appear

particularly useful in differentiating the effects of TEAS amplitude (at fixed frequency) or frequency

(at ‘high’ or ‘low’ amplitude) compared to sham.

Table 15 compares results in Table 13 derived from estimating separations between plots as

aggregate percentage differences over time, with those from Tables 7 and 11 above, which are

based on the significance of differences found.

Table 15. HRV measures and HRNL indices found in Tables 7, 11 and 14.

Table 7 Table 7 Table 7 Table 11 Table 13 ALL


Median EScomparing frequencies

Median EScomparing Slots 1 and 6

Counts (≥Q3) comparing frequencies

N “y” N

General PNS SNS

0.27 0.27

0.29 0.29

0.3 aa

10 aa

7 3

5 3

Time domain rMSSD

NNxx pNNxx

0.26 0.27 0.275

0.30 0.27 0.28

11 aa aa

aa 7 6

3 3 3

Frequency domain

HFabs HFlog HF% HFnu LFabs LFlog LF% LFnu LF/HF TotPwr HF.Hz

0.31 0.31 0.345 0.35 0.265 0.265 0.34 0.345 0.34 aa aa

0.295 0.31 0.305 0.33 aa aa 0.34 0.33 0.28 aa aa

aa aa 0.49 0.48 aa 0.41 0.455 0.485 0.43 aa aa

10 7 aa aa aa aa aa aa aa aa aa

3 aa

5 aa aa aa aa aa 9 6 1

4 3 3 3 1 2 3 3 4 1 1

Nonlinear/ entropy

SD1 SD2/SD1

0.26 0.295

0.30 0.37

aa 0.40

11 14

3 4

52

DFA α1 ShannEn SampEn/MSE1 MSE5 MSE7 MSE19 MSE3 MSE10

0.31 0.27 0.25 0.35 0.34 0.375 aa aa

0.285 0.33 0.315 0.295 0.33 0.34 aa aa

0.47 aa aa aa aa aa aa aa

aa14 8 10 8 10 8 7

3 3 3 3 3 3 1 1

HRNL indices D2 D1+D2 pD2

aa0.30 aa

aa0.27 aa

7 aa 7

8 7 aa

2 3 1

Of the measures and indices shown in Table 15, only PNS appears in all five columns from the

previous Tables, and only three HRV measures in four columns (HFabs, LF/HF and SD2/SD1). Sixteen

further HRV measures and one HRNL index appear in three columns.

Table 15. Commentary Note The relationship between ES and %Diff was explored for a sample of 13 of the HRV measures and HRNL indices in Table 15, in the hope that %Diff, which is quick and easy to calculate in Excel, might be useful as a surrogate for ES based on Wilcoxon (or Mann‐Whitney) tests, for which statistical software is really required.15 This hope was soon dashed, as although ES and %Diff were strongly correlated for some measures and indices (e.g. for HFabs or D1+D2, with Pearson’s R = 0.806 and 0.862, respectively, when comparing values for high and low amplitudes at 2.5 pps), other correlations were not significant (e.g. for PNS, again at 2.5 pps), or even negative (e.g. for pNNxx at 2.5 pps). For some measures, correlations were higher at 10 pps than at 2.5 pps, but for others they were higher at 2.5 pps, so stimulation frequency appeared to have no bearing on correlation between the two measures.

8. Increases and decreases in HRV measures over time, between slots 1 and 6

In our previous 2019 poster, we included Tables showing the counts of HRV measures that increased

or decreased over time, for each stimulation frequency (Mayor et al. 2019, p. 19, Tables 10 & 11).

The measures were considered as ‘PNS‐like’ [P] or ‘SNS‐like’ [S] from the literature, and also

according to their correlations with the ‘benchmark’ general measures of parasympathetic or

sympathetic nervous system activity, PNS and SNS.

In Table 16 we update our previous Tables to include the HRNL indices, take into account stimulation

amplitude, and also to allow for some HRV measures whose ‘PNS‐like’ or ‘SNS‐like’ effect was found

in the literature to be somewhat ambiguous or uncertain. With this re‐categorisation of measures,

21 were considered ‘PNS‐like’, 33 ‘SNS‐like’, and six as equivocal (MSE2 to MSE6 and ShannEn). The

latter were then allocated either as ‘PNS‐like’ or ‘SNS‐like’ to see if a difference in allocation

15 In addition, rather than repeating the Wilcoxon and Mann‐Whitney tests for different time slots in order to calculate ES, a non‐parametric equivalent to the repeated measures ANOVA such as the Friedman test could also be used.

53

markedly changed the significance of the resulting Binomial ratios of ‘increasing’ and ‘decreasing’

HRV/HRNL counts.

Table 16. Numbers of measures increasing or decreasing over time, for each stimulation frequency.

High amp 0 pps 2.5 pps 10 pps 80 pps ALL

Change inc dec inc dec inc dec inc dec inc dec

PNS‐like + equivocal

922 713 492 361 472 385 444 357 2330 1816

SNS‐like 852 1143 433 605 426 614 424 554 2135 2916

equivocal 230 136 111 78 105 85 112 67 558 366



PNS‐like 692 577 381 283 367 300 332 290 1772 1450

SNS‐like + equivocal

1082 1279 544 683 531 699 536 621 2693 3282

equivocal 230 136 111 78 105 85 112 67 558 366

Low amp 0 pps 2.5 pps 10 pps 80 pps ALL



922 713 470 389 442 393 444 390 2278 1885

SNS‐like 852 1143 419 629 436 582 414 598 2121 2952

equivocal 230 136 108 84 108 78 104 82 550 380



PNS‐like 692 577 362 305 334 315 340 308 1728 1505


1082 1279 527 713 544 660 518 680 2671 3332

equivocal 230 136 108 84 108 78 104 82 550 380

Ratios between numbers of increases and decreases were tested for significance using the Binomial

test. Results are shown in Table 17.

Table 17. Significance of the ratios of increases to decreases (from Table 15), using the Binomial test.


Ratio inc:dec p value inc:dec p value inc:dec p value inc:dec p value inc:dec p value


1.293 <10‐6 1.363 <10‐5 1.226 <10‐3 1.244 <10‐2 1.283 <10‐11

SNS‐like 0.745 <10‐10 0.716 <10‐6 0.694 <10‐8 0.765 <10‐4 0.732 <10‐27

equivocal 1.691 <10‐4 1.423 <10‐1 1.235 n.s. 1.672 <10‐2 1.525 <10‐13



PNS‐like 1.199 <10‐2 1.346 <10‐3 1.223 <10‐1 1.145 n.s. 1.222 <10‐7


0.846 <10‐4 1.498 <10‐4 0.999 <10‐5 1.311 <10‐1 0.821 <10‐13

equivocal 1.691 <10‐4 1.423 <10‐1 1.235 n.s. 1.672 <10‐2 1.525 <10‐13




1.293 <10‐6 1.208 <10‐2 1.125 n.s. 1.138 n.s. 1.208 <10‐8

54

SNS‐like 0.745 <10‐10 0.666 <10‐10 0.749 <10‐5 0.692 <10‐8 0.718 <10‐30

equivocal 1.691 <10‐4 1.286 n.s. 1.385 <10‐1 1.268 n.s. 1.447 <10‐7



PNS‐like 1.199 <10‐2 1.187 <10‐1 1.060 n.s. 1.104 n.s. 1.148 <10‐4


0.846 <10‐4 0.739 <10‐6 0.824 <10‐2 0.762 <10‐5 0.802 <1016

equivocal 1.691 <10‐4 1.286 n.s. 1.385 <10‐1 1.268 n.s. 1.447 <10‐7

8.1. Increases and decreases in HRV measures over time: re‐allocating equivocal measures to PNS‐ or

SNS‐like

For low‐amplitude stimulation, whether the equivocal measures are considered PNS‐ or SNS‐like

makes little difference to predominant directions of change, although if considered as themselves

PNS‐like they obviously increase the ratios of increases to decreases for the PNS‐like measures, and

if considered as SNS‐like, they also increase the ratios of increases to decreases for the SNS‐like

measures.

For high‐amplitude stimulation, the equivocal measures again make little difference to directions of

change if considered as PNS‐like, but if taken as SNS‐like, the ratios of increases to decreases are not

only increased for both 2.5 and 10 pps but the predominant directions of change at these

frequencies are also reversed.

This suggests that at least some of the equivocal measures should be considered as PNS‐like.

To investigate this issue further, correlations between changes in the various equivocal and other

HRV measures at each stimulation frequency were examined (changes from baseline to Slots 3, 5

and 6, and changes between Slots 3 and 5). ShannEn was consistently found to change significantly

in the same direction as SD2, SD2/SD1 and DFA α1, indicating that it is more likely to be a SNS‐like

than a PNS‐like HRV measure (cf Footnote 8 above, p. 11).

A similar exploration of MSE was undertaken for Slots 1 and 6 and the changes between them for all

stimulation frequencies, resulting in 12 correlation tables. In 11 of these (but not for the change

between Slots 1 and 6 at 2.5 pps), MSE2 was found to correlate positively with MSE1, but directions

of correlation with MSE1 were not at all consistent at higher MSE scales. MSE1 itself (i.e. SampEn)

correlated positively with the PNS index in 11 comparisons, significantly so in nine of them.

Nonsignificant positive correlations between PNS and MSE1 were for sham stimulation in Slot 1 and

for 80 pps in the change data, with a nonsignificant negative correlation between PNS and MSE1 for

10 pps in the change data. This supports the [P] classification of SampEn/MSE1, and also suggests

that MSE2 could also be considered as PNS‐like, at least in this study.16

16 Correlations between MSE1 and MSE3 to MSE20 in Slots 1 and 6 and the changes between them for all stimulation frequencies resulted in four significant correlations for MSE7, three for MSE3, two for MSE5, MSE6, MSE12 and MSE19, none for MSE8, MSE9 and MSE17, and one for the remainder. Of the latter, seven were negative correlations, all for MSE11 and above; MSE12 and MSE19 showed one negative and one positive correlation each. These findings appear to cast doubt on attempts to classify low MSE scales up to MSE5 or MSE6 as PNS‐like and higher scales as SNS‐like (see Mayor et al. 2019, pp. 13‐14 for a brief discussion).

55

Using these findings, Tables 16 and 17 can be amended, as in Tables 18 and 19.

Table 18. Numbers of measures increasing or decreasing over time, for each stimulation frequency,

with ShannEn and MSE2 re‐allocated as PNS‐like and MSE3 to MSE 6 as SNS‐like.



PNS‐like + MSE2

752 578 411 283 399 300 361 290 1772 1450

SNS‐like + ShannEn, MSE3‐6

1022 1278 514 683 499 699 507 621 2135 2916



PNS‐like + MSE2

752 578 394 305 365 315 370 309 1728 1505


1022 1278 495 713 513 660 488 679 2121 2952

Table 19. Significance of the ratios of increases to decreases (from Table 15), using the Binomial test.



PNS‐like + MSE2

1.031 <10‐5 1.452 <10‐5 1.330 <10‐3 1.245 <10‐2 1.222 <10‐7


0.800 <10‐6 0.753 <10‐5 0.714 <10‐8 0.816 <10‐2 0.732 <10‐27



PNS‐like + MSE2

1.031 <10‐5 1.292 <10‐2 1.159 n.s. 1.197 <10‐1 1.148 <10‐4


0.800 <10‐6 0.694 <10‐9 0.777 <10‐4 0.719 <10‐7 0.718 <10‐31

8.2. Increases and decreases in HRV measures over time: a brief summary

For all stimulation frequencies and at both amplitudes, the PNS‐like measures now show significantly

more increases than decreases, and the SNS‐like measures significantly more decreases than

increases. Thus changes between baseline and Slot 6 (post‐stimulation) indicate an improvement in

autonomic balance, i.e. favouring the PNS but not SNS, for all frequencies.

For all three active frequencies, the ratio of PNS‐like increases to decreases was higher at high than

at low amplitude. For 2.5 pps and 80 pps, this was also the case for the ratio of SNS‐like increases to

decreases at, but for 10 pps this ratio was lower at high than at low amplitude.

56

At high amplitude, 2.5 pps appears to be the frequency with most PNS‐like effects, and sham

stimulation the frequency with fewest PNS‐like effects, while 80 pps has the most SNS‐like effects

and 10 pps the fewest.

At low amplitude, 2.5 pps appeared to have the most PNS‐like effects, and sham stimulation the

most SNS‐like effects, with sham stimulation the fewest PNS‐like effects and 2.5 pps the fewest SNS‐

like effects.

8.3. Using only those measures and indices for which significant differences were found using the

Wilcoxon signed ranks test

Table 20 (based on Table 5) shows corresponding ratios and Binomial test significance values for the

reduced number of HRV measures and HRNL indices which showed significant differences between

Slots 1 and 6 when using the Wilcoxon signed ranks test. The problematic measures in red in Table 5

have been included. Table 21 shows results with them excluded.

Table 20. Significance of the ratios of increases to decreases of significant differences between Slots

1 and 6 (from Table 5), using the Binomial test and including measures with reversed ratios.



PNS‐like + reverse measures

0.465 <10‐8 1.188 n.s. 0.855 n.s. 0.333 <10‐3 0.687 <10‐5

SNS‐like + reverse measures

1.697 <10‐10 2.511 <10‐13 1.783 <10‐6 2.659 <10‐7 1.938 <10‐34




0.465 <10‐8 0.444 <10‐5 0.938 n.s. 0.453 <10‐3 0.497 <10‐14


1.697 <10‐10 2.368 <10‐7 0.476 <10‐2 2.822 <10‐12 1.858 <10‐25

ALL 0 pps 2.5 pps 10 pps 80 pps ALL



0.465 <10‐8 1.110 n.s. 0.595 <10‐2 0.464 <10‐6 0.834 <10‐4


1.697 <10‐10 2.155 <10‐12 1.385 <10‐2 1.996 <10‐17 1.476 <10‐33

Table 21. Significance of the ratios of increases to decreases of significant differences between Slots

1 and 6 (from Table 5), using the Binomial test and excluding measures with reversed ratios.



PNS‐like ‐ reverse

0.465 <10‐8 0.388 <10‐4 0.333 <10‐4 0.333 <10‐3 0.413 <10‐19

57

measures

SNS‐like ‐ reverse measures

1.958 <10‐15 3.280 <10‐18 2.606 <10‐12 2.659 <10‐7 2.369 <10‐49



PNS‐like ‐ reverse measures

0.465 <10‐8 0.444 <10‐5 1.818a n.s. 0.453 <10‐3 0.499 <10‐14


1.958 <10‐15 2.368 <10‐7 0.476b <10‐2 2.822 <10‐12 2.021 <10‐11

ALL 0 pps 2.5 pps 10 pps 80 pps ALL


PNS‐like ‐reverse measures

0.465 <10‐8 0.775 n.s. 0.595 <10‐2 0.464 <10‐17 0.608

<10‐20


1.958 <10‐15 2.155 <10‐12 1.739 <10‐4 2.262 <10‐21 1.716 <10‐11

a. Based on only a single measure, RR; b. based on only HRmean and pD1+D2.

Using only those HRV measures and HRNL indices for which changes were significant, rather than

counting increases and decreases for all the measures and indices, and including the problematic

measures in red in Table 5, at high stimulation amplitudes it appears that 80 pps TEAS is likely to be

experienced as most stressful and sham as least stressful, whereas at low amplitudes 10 pps TEAS is

likely to be experienced as least stressful and possibly 80 pps as most stressful. Disregarding

amplitude, again TEAS at 10 pps TEAS may be experienced as less stressful than at 2.5 or 80 pps.

Excluding the problematic measures, at both high and low stimulation amplitudes 10 pps TEAS may

be experienced as less stressful than TEAS at the other two active frequencies.

58

Discussion

Main findings

Although twice as many comparisons within slots could be made among stimulation frequency

effects as between stimulation amplitudes (48 vs 24 comparisons for each HRV measure or HRNL

index), numbers of such comparisons that were significant were some 17% higher for those between

amplitudes (Table 2). HRV frequency‐domain measures (and one HRNL index) were more likely to

demonstrate differences with amplitude, and entropy or complexity HRV measures more likely to

vary with frequency. However, ES was often marginally larger when comparing frequencies than

when comparing amplitudes (Table 3).

Median ES across all slots for the difference between amplitudes was greatest at 10 pps, and for

frequency differences for the difference between 2.5 pps and sham (Table 4). However, median ES

was greater still for changes over time, between Slots 1 and 6 (Table 7).

Numbers of significant differences for both amplitude and frequency comparisons were comparable

(Table 8), but patterns were very different for the different frequencies: amplitude appears to have

had a much greater differential effect on HRV and HRNL at 10 pps than at the other frequencies (cf

results from the Figures, below). Moreover, when 10 pps and 2.5 pps were compared with sham

stimulation, greater numbers of significant differences were present after than during or before

stimulation.

In particular (Table 9), following stimulation, 10 pps TEAS resulted in 45 more good than bad

outcomes, and at 80 pps in just 2 more good than bad outcomes, if ‘good’ is considered as an

increase in PNS‐like measures or a decrease in SNS‐like measures relative to sham following TEAS,

and ‘bad’ as a decrease in PNS‐like measures or an increase in SNS‐like measures relative to sham

following TEAS.

When the effects of different frequencies were considered in all slots, separating results into those

for high and low amplitudes (Table 10), there were significantly more significant differences at high

amplitude between HRV measures and HRNL indices at 2.5 pps and sham, or 80 pps and sham, as

might be expected, but significantly more significant differences at low amplitude between HRV

measures and HRNL indices at 10 pps and sham.

Examining the first 14 Figures, a consistent finding is that values of PNS‐like measures and indices

tend to be greater at lower amplitudes of TEAS, and less at higher amplitudes (Figure 1.1, 1.3, 1.5,

1.6), and vice versa for SNS‐like measures (Figure 1.4, 1.6, 1.7). Another is that amplitude appears to

have had a much greater differential effect on HRV and HRNL at 10 pps than at the other frequencies

(Figure 1.1, 1.2R, 1.3, 1.4, 1.5, 1.6, 1.9, 1.14R), often least at 80 pps (Figure 1.1, 1.3, 1.4, 1.6). SDNN

and SDHR provide counter‐examples to these findings (Figure 1.10). Total power (TotPwr), SI and

ApEn may also behave in ways contrary to those expected (Figure 1.11, 1.13).

Examining the second 14 Figures, a recurring pattern is that differences from sham for an HRV

measure at a particular stimulation frequency are often of opposite signs at high and low amplitudes

(Figure 2.1, 2.3, 2.4, 2.5R, 2.6, 2.7, 2.8, 2.10, 2.11, 2.12, 2.13L). For some measures, results also

59

appear consistently more similar for the two lower active frequencies (2.5 and 10 pps) (Figure 2.4R,

2.6R, 2.7L, 2.8L, 2.10R, 2.11L), for others the two higher active frequencies (10 pps and 80 pps)

(Figure 2.10L, 2.11R), and for others the upper and lower active frequencies (2.5 and 80 pps) (Figure

2.2R, 2.3, 2.4L, 2.5R, 2.6L).

Summaries of these findings in Table 12 confirm that greater differentiation between stimulation

amplitude effects occurred at 10 pps and least at 80 pps. At 10 pps, relatively greater differences

between stimulation amplitudes were found for PNS‐like rather than SNS‐like measures and indices.

Table 13 confirms that most (and greatest) differences from sham were found for 10 pps TEAS at low

amplitude (particularly for PNS‐like measures and indices), and fewest for low‐amplitude stimulation

at the other two active frequencies.

Those HRV measures and HFNL indices that were found most useful in analysis are shown in Table

15. Of the 29 measures and indices shown, the PNS index would appear to be the most useful,

followed by HFabs, LF/HF and SD2/SD1.

In our earlier report (Mayor et al. 2019), we concluded that overall, stimulation at both 2.5 and 80

pps would appear to increase rather than decrease the stress response, and sham and 10 pps not to

do one or the other, particularly. We now conclude (Tables 18 to 21) that changes between baseline

and Slot 6 (post‐stimulation) indicate an improvement in autonomic balance, i.e. favouring the PNS

but not SNS, for all frequencies.

Furthermore, at both high and low amplitude, 2.5 pps appears to be the frequency with most PNS‐

like effects. At high stimulation amplitudes it appears that 80 pps TEAS is likely to be experienced as

most stressful and sham as least stressful, whereas at low amplitudes 10 pps TEAS is likely to be

experienced as least stressful and possibly 80 pps as most stressful. Disregarding amplitude, again

TEAS at 10 pps TEAS may be experienced as less stressful than at 2.5 or 80 pps.

Possible directions for further research

Findings from this study could be explored in clinical research. With more awareness of the PNS‐like

and SNS‐like effects of different TEAS parameters, and knowledge of whether the parasympathetic

or sympathetic nervous system is involved in a particular condition (or indeed, their imbalance), it

should become possible to tailor TEAS (or EA) treatment to what is required for optimal outcome,

rather than simply relying on more stablished rules of thumb derived from neurochemical

interpretations of how these treatments work.

This study confirms the usefulness of a multivariate approach in HRV research. Although only four

measures (the PNS index, HFabs, LF/HF and SD2/SD1) gave consistent or significant results in four or

more steps of our analysis, other measures from all domains, as well as nonlinear complexity

measures and HRNL indices, were revealing of useful findings. In particular, our finding that HRV

frequency‐domain measures (and one HRNL index) were more likely to demonstrate differences

with amplitude, and entropy or complexity HRV measures more likely to vary with frequency, could

be explored further.

There are many different ways of practising acupuncture‐related therapies like EA and TEAS. Some

practitioners think in terms of qi and use traditional methods that aim to balance the whole system,

60

whereas others focus more on helping to improve specific symptoms – these two approaches, of

course, not necessarily being mutually exclusive. ‘Balance’ can be applied to concepts such as yin

and yang, or to physiological functions such as those of the parasympathetic and sympathetic

divisions of the autonomic nervous system. we think it is probably true to say that most

acupuncturists who think in terms of qi would consider relaxation and parasympathetic activation

therapeutically useful in many cases. On the other hand, many of those who treat specific

musculoskeletal (MSK) conditions might consider that stronger stimulation is sometimes required,

citing evidence of stress‐induced endorphin release or diffuse noxious inhibitory controls (Mayor

2007). One avenue for future exploration would be to test whether those stimulation parameters

found in this study to enhance parasympathetic activity are more appropriate for ‘whole‐person’

conditions involving autonomic imbalance, and those that appear to improve sympathetic activity

for the treatment of specific MSK conditions. This may be a naively dualistic approach, but it would

certainly be worth exploring in further research.

Findings on the HRNL indices have potential application for both linear and nonlinear HRV measures.

Data for which the HRNL indices D2 or D1+D2 are low may provide more accurate values for

frequency‐domain HRV measures but less accurate values for nonlinear measures of complexity or

entropy. Correspondingly, data for which these HRNL indices are high may provide more accurate

values for nonlinear measures of complexity or entropy, but less accurate values for frequency‐

domain HRV measures. This topic could usefully be explored in more detail.

Limitations

As already stated, the classification of HRV measures as ‘PNS‐like’ or ‘SNS‐like’ should considered as

a heuristic and practical expedient, and one that can hopefully be improved in the future as

understanding of HRV develops.

The definition of stimulation amplitude used here is not altogether satisfactory, firstly because it

combines amplitudes from two different locations (each hand) and at two different times (at the

start of stimulation, and after 10 minutes of TEAS), and secondly because some participants

exhibited different ‘high’ and ‘low’ amplitudes at the different stimulation frequencies. Comparisons

between them therefore had to be restricted to cases where amplitude was ‘high’ or ‘low’ for both

frequencies concerned.

For speed and simplicity, graphs show differences between group median values, not median values

of the differences for each participant. This approach should be refined in further analysis.

Changes over time were only considered between baseline and Slot 6, as explained above (p. 13).

Changes during stimulation (Slot 2 to Slot 5) and post‐stimulation (Slot 6 to Slot 8) should also be

examined – particularly in view of the finding that after‐effects of stimulation may themselves

change with time (Hideaki et al. 2015).

Baseline effects are clearly visible in many of the graphs plotted. However, these were, we hope,

mitigated to some extent by the strategy of comparing percentage differences in measures and

indices for high and low amplitudes, divided by the median value for both amplitudes at baseline.

Similarly, when comparing percentage differences in measures and indices between active

frequencies and sham, these were divided by the median value for sham to ‘normalise’ results.

61

Univariate statistical methods were used to analyse data. Further, multivariate methods should be

applied to clarify the relationship between amplitude and frequency effects, and also interactions

between the different HRV measures and HRNL indices used.

Conclusions

On the basis of ECG‐derived HRV measures and HRNL indices, TEAS at 10 pps appeared in this study

to be experienced as less stressful than at 2.5 or 80 pps. Higher amplitude TEAS was in general

experienced as more stressful, and the amplitude high‐low differential had most effect at 10 pps. In

general, stimulation at high and low amplitudes had opposite effects when comparing active

stimulation at all frequencies with sham. Moreover, when 10 pps and 2.5 pps were compared with

sham stimulation, greater numbers of significant differences were present after than during

stimulation, with beneficial changes evident particularly after 10 pps TEAS. Most (and greatest)

differences from sham were found for 10 pps TEAS at low amplitude (particularly for PNS‐like

measures and indices).

Author contributions

DM and TS designed the study; DM organised recruitment; TS provided the requisite equipment; TS

collected and processed the ECG data; DP collated the results; and DM prepared this presentation.

Acknowledgements

To the University of Hertfordshire for permitting us to conduct this study and to Prof Tim Watson in

particular for facilitating it and providing academic supervision. To Lidia Zaleczna and Aiste

Noreikaite for the hours they spent carefully collecting the ECG data, and to the latter for assistance

in processing it. To our volunteers for their participation, to our families and partners for their

continued patience and support, and to many other colleagues for discussions and other input that

helped to shape the study. To the Acupuncture Association of Chartered Physiotherapists (AACP)

and to DM’s patients, whose financial support indirectly made this study possible. To Pedro

Bernaola‐Galván for his work on NL indices and for permission to use them in this presentation prior

to their more formal dissemination. Last, but not least, to Martin Underwood, whose perspicacious

question at the 2019 ARRC Symposium about the effects of stimulation amplitude on HRV acted as a

catalyst for this presentation, and to Mark Bovey, whose critique of our presentation at the 2019

ARRC Symposium also helped to shape this one.

62

Appendix. Additions and amendments to findings in our poster presentation at the 2019 ARRC

Symposium (Mayor et al. 2019)

1.1. Can nonlinearity be considered a benchmark for the HRV measures?

The Tables of correlations between PNS and SNS and the other HRV measures used (Mayor et al.

2019, Tables 1‐2, pp. 8‐9) did not include any nonlinearity indices. Re‐calculating correlations

between these and the HRV measures resulted in no absolute values of Spearman’s rho ≥ 0.5. The highest values of |rho| were for correlations between D1+D2 and HRV frequency domain measures

LF/HF and LFnu (rho = ‐0.430), between D1+D2 and HFnu (rho = 0.430), between D1+D2 and HF%

(rho=0.429) and between D1+D2 and LF% (rho = ‐0.426). Values of rho for correlations between D2

and the HRV measures were all less than 0.200.

In contrast, 30 HRV measures exhibited strong correlations with PNS (|rho| ≥ 0.5), and 17 with SNS. This disparity suggests that neither of the nonlinearity indices investigated, D2 and D1+D2, can be

considered as underlying HRV in any sense, and that nonlinearity is indeed a different attribute of

heart rate dynamics than variability.

1.2. How much do the HRV measures vary in themselves?

The Table of coefficients of variation (CVs) > 4 for the 56 HRV measures used (Mayor et al. 2019,

p. 10) shows only 10 such measures. CVs for the nonlinearity indices are shown in Table A1 below.

Table A1. Nonlinearity indices with CV > 4

Measure D1+D2 pD1+D2 D2 pD2 D2xpD2

Av 8.830 9.959 41026.168 13.198 4685.588

SD 108.559 70.856 1155221.277 66.854 99912.813

CV 12.294 7.114 28.158 5.066 21.323

CV was highest for D2 and D2xpD2, lowest for the two significance measures, pD2 and pD1+D2. All

have CV > 4, suggesting that like the 10 HRV measures in the original Table, nonlinearity indices

might be likely to differ for different stimulation frequencies and amplitudes (but see proviso on p. 8

above).

63

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