Heart Rate Variability (HRV) analysis using simultaneous ......For CC analysis, the results were reported with the correlation coefficient, P-value, and the 95% confidence interval

Heart Rate Variability (HRV) analysis using simultaneous handgrip electrocardiogram and fingertip photoplethysmogram

JAE MOK AHN

Hallym University, [email protected]

Abstract The integration of sympathetic and parasympathetic activity with afferent signals from the

baroreceptors produces beat-to-beat changes in heart rate and heart rate variability (HRV), which reflects autonomic nervous system (ANS) function. Research has indicated that HRV data should be obtained using an electrocardiogram (ECG) signal rather than a photoplethysmogram (PPG) signal. This study presents statistical validation for the interchangeability of two heart rate detection methods for assessing ANS function. Simultaneous ECG and PPG signals were acquired from six healthy volunteers using handgrip Lead II type ECG and fingertip PPG methods while measuring HRV for 5 min. Statistical tests were performed to compare 5-min ECG-PPG data pairs and HRV indices (Ln_VLF, Ln_LF, Ln_HF) after the spectral analysis. In the frequency domain, the concordance correlation coefficient (CCC) was 0.9996, 0.9998, and 0.9863 in Ln_VLF, Ln_LF, and Ln_HF, respectively. In the time domain, the CCC was 0.8741 to 0.9907 for the six data pairs. The correlation coefficient, r, was calculated with a 0.9917 maximum for ECG-PPG pair6 and with a 0.8751 minimum for ECG-PPG pair3. The results suggest that ECG and PPG signal recordings could be interchanged for HRV analysis, including the time and frequency domains.

Keywords: Electrocardiogram, Heart rate variability, Photoplethysmogram

1. Introduction

In clinical research, the power spectral density (PSD) of heart rate variability (HRV) has been used to assess autonomic nervous system (ANS) function [1]. The clinical importance of HRV became apparent in the late 1980s when it was confirmed that HRV was a strong and independent predictor of mortality following an acute myocardial infarction [2]. Many research groups have proposed various spectral analysis methods to obtain a reliable assessment of HRV, including microcontroller-based time-domain analysis [3-6].

The PSD analysis provides the basic information of how power distributes as a function of frequency. Independent of the method employed, only an estimate of the true PSD of the signals can be obtained by proper mathematical algorithms. An accurate HRV assessment requires time-series data on normal-to-normal intervals between successive heartbeats with a sampling frequency of at least 1 kHz. In an electrocardiogram (ECG) recording, each QRS complex is detected, and the instantaneous RR intervals are determined. The detection of the RR peak has been demonstrated to be highly accurate and reproducible [7,8].

However, in certain applications, ECG-derived signals exhibit a greater susceptibility to electromagnetic and biologic interference than PPG-derived signals [8]. Therefore, the photoplethysmogram (PPG) has been used as an alternative HRV assessment to determine the successive heart rate employing the peak-to-peak interval of continuous monitoring of pulse waveforms in blood volume in a portion of the peripheral microvasculature [5,10,11,12,13]. PPG is a simple and low-cost optical measurement method.

In this study, 5-min HRV data pairs of handgrip ECG and fingertip PPG recordings were simultaneously obtained from six young subjects (20 - 39 year-old) using two types of equipment. We performed power spectral analysis to evaluate three main spectral components with very low frequency (VLF), low frequency (LF), and high frequency (HF). To confirm the agreement between HRV indices pairs, including the time series pairs, we used various statistical analyses with graphical presentations.

2. Materials and methods 2.1 Subject data

Heart Rate Variability (HRV) analysis using simultaneous handgrip electrocardiogram and fingertip photoplethysmogram JAE MOK AHN

Advances in information Sciences and Service Sciences(AISS) Volume5, Number13, August 2013

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The HRV data pairs included simultaneous ECG and PPG signals from six young healthy subjects while measuring HRV for 5 min. All of the subjects provided written informed consent before participating in this investigation. The data acquisition was performed using two types of instrument: the TAS9 VIEW (or CANOPY9 RSA, IEMBIO Co. Ltd., Chuncheon-si, South Korea) for the fingertip PPG signal and the CANOPY9 VIEW (IEMBIO Co. Ltd., Chuncheon-si, South Korea) for the handgrip ECG signal.

Figure 1 shows how we simultaneously measured the ECG-PPG data pairs in time series. The ECG and PPG signals were sampled to 1000 samples s-1. All of the recordings were inspected to select data pairs without time delays that could affect the results. The measurement equipment included the algorithm of spectral analysis in the frequency domain by DFT (discrete Fourier transform). The three frequency bands were as follows: VLF corresponds to 0.0033 to 0.04 Hz, LF corresponds to 0.04 to 0.15 Hz, and HF corresponds to 0.15 to 0.4 Hz. The HF range reflects the fast changes in the beat-to-beat variability which are due to parasympathetic or vagal stimulation, whereas the VLF is thought to reflect mostly sympathetic stimulation. The LF region represents a mixture of both sympathetic and parasympathetic stimulation of the heart.

The data were converted to natural logarithms from the decimal value: Ln_VLF, Ln_LF, and Ln_HF. Table 1 lists the spectral values (milliseconds squared) analyzed from the six ECG-PPG data pairs. Table 2 shows all of parameters analyzed on frequency domain of HRV.

Figure 1. The equipment used for the simultaneous ECG and PPG measurements. Handgrip Lead II

ECG was applied with three electrode inputs, which connected both hands and the left leg. CANOPY9 VIEW (rear) and TAS9 VIEW (or CANOPY RSA) (front) with the fingertip PPG sensor and the

handgrip ECG electrode, respectively.

Table 1. HRV indices. PSD analyzed from 5-min HRV data. Tests Methods Ln_VLF Ln_LF Ln_HF Pair1 ECG1 5.5790 6.0556 6.0375

PPG1 5.5813 6.0661 5.9764

Pair2 ECG2 6.4247 6.7117 6.4179 PPG2 6.4234 6.7355 6.4288

Pair3 ECG3 6.1961 4.2540 5.1399 PPG3 6.1978 4.2233 5.2802

Pair4 ECG4 5.5568 4.7168 5.1809 PPG4 5.5693 4.7089 5.2689

Pair5 ECG5 5.4747 4.4336 5.0654 PPG5 5.4943 4.4343 5.1037

Pair6 ECG6 5.5624 4.6079 5.3341 PPG6 5.5673 4.5923 5.4176


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Table 2. Parameters of frequency domain analysis of HRV.

Parameter Unit Description TP (total power) VLF

ms2

ms2 PSD in all the frequency ranges PSD in very low-frequency range (0.0033~0.04Hz)

LF ms2 PSD in low-frequency range (0.03~0.15Hz)

HF ms2 PSD in high-frequency range (0.15~0.4Hz)

R ratio of LF/HF

2.2 Statistical analysis

We applied three statistical methods to evaluate the comparison data: the concordance correlation coefficient (CCC), the correlation coefficient (CC), and Passing and Bablok regression (PBR) using MedCalc (MedCalc software, Belgium), which are considered to be useful methods for investigating the interchangeability between ECG and PPG measurements. The CCC evaluates the agreement between data from the same sample by measuring the variation from the 45-degree line through the origin. The CCC (ρC) is a measurement of precision (ρb) and accuracy (Cb): ρC = ρb * Cb, where ρb is the Pearson correlation coefficient, which measures how far each observation deviates from the best-fit line, and Cb is a bias correction factor that measures how far the best-fit line deviates from the 45-degree line through the origin.

For CC analysis, the results were reported with the correlation coefficient, P-value, and the 95% confidence interval. PBR provides the proportional differences and the residual standard deviation (RSD) of the differences, which were used to evaluate a measure of the proportional differences between two HRV measurements and how well a linear model fits the data, respectively [14].

2.3 Spectral analysis

The main advantages of power spectral density (PSD) analysis over the time domain measures is that it supplies information on how the power is distributed (the variance) as a function of frequency. The spectral components (VLF, LF, and HF) of the HRV which represent the different branches of the autonomic system were calculated using Discrete Fourier Transform (DFT).

A DFT was used to determine three spectral energies in 5-minute dataset. For the 5-minute segment, the frequency limits correspond to very low frequency (VLF 0.0033 to 0.04 Hz), low frequency (LF 0.04 to 0.15 Hz), and high frequency (HF 0.15 to 0.4 Hz). It was calculated by dividing 1000 data points by the measurement time in milliseconds.

The mathematical equations for calculating the PSD for each frequency range were employed in the following. A set X of dimensional vectors represents the PSD corresponding to frequency, k.

= |()|.. = |()|.. = |()|..

The PSD is reported in absolute values, expressed as milliseconds squared, and converted into a

natural logarithm: Ln_VLF, Ln_LF, and Ln_HF. Ln_LF is divided by Ln_HF to obtain the R ratio, representing the ratio of sympathetic nerve activity to parasympathetic nerve activity. The HF range reflects the fast changes in the beat-to-beat variability due to parasympathetic stimulation, whereas the VLF is thought to reflect mostly sympathetic stimulation. The LF region represents a mixture of both sympathetic and parasympathetic stimulation of the heart.


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3. Results

We investigated the feasibility of using the fingertip PPG and the handgrip Lead II ECG recordings for HRV assessment. The VLF, LF, and HF of 5-min HRV data were selected as the comparison parameters of HRV indices as well as time series data. The statistical results using the CCC, CC, and PBR methods are detailed in Table 3. For the CCC, the ρC was 0.9996 in Ln_VLF with 0.9983 to 0.9999 (95% CI), 0.9998 in Ln_LF with 0.9995 to 0.9999 (95% CI), and 0.9863 in Ln_HF with 0.9364 to 0.9971 (95% CI). The CC, r, was 0.9999 in Ln_VLF with 0.9990 to 1.0000 (95% CI), 1.0000 in Ln_LF with 0.9996 to 1.0000 (95% CI), and 0.9954 in Ln_HF with 0.9564 to 0.9995 (95% CI). A high correlation coefficient (ρC > 0.98 and r > 0.99) demonstrated that there was no significant difference between the HRV index pairs.

The PBR results demonstrated that the RSD was 0.005581 in Ln_VLF, 0.007734 in Ln_LF, and 0.04446 in Ln_HF. The RSD from the original value was within -1.0% to 1.0% on average. The linearity assessment is shown in Figure 2, which also demonstrates the degree to which each pair (Ln_VLFs, Ln_LFs, and Ln_HFs) deviated from the 45-degree line, which represents perfect linearity (top row). Cumulative frequency (%) was plotted against each spectral component in Figure 2 (middle row).

Figure 2. CC, Wilcoxon, and PBR plots between ECG and PPG HRV indices. The top row (CC plot) shows that the data that deviated from the 45-degree line through the origin are comparable. The

middle row (Wilcoxon plot) represents the cumulated values of the HRV index pairs. The bottom row (PBR plot) shows the running total of the number of pairs vs. residuals, which represents the difference

of pairs from the zero regression line.


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The cumulative results indicated that ECG signals and PPG signals had good linearity for the comparison data on Ln_VLF, Ln_LF, and Ln_HF. Figure 2 (bottom row) presents the arithmetic difference between the HRV indices with respect to the total number of samples, shown as Rank (x,y). All of the results obtained from the three graphs were in agreement with those calculated from the CCC, CC, and PBR. Table 4 shows that for the ECG-PPG time-series comparison data, the CCC and CC values are lower than those of the spectral HRV indices by 0.02 points on average. For the six ECG-PPG pairs in the time series, the ρC values were between 0.8741 and 0.9907, whereas r obtained 0.9917 for pair6 in maximum and 0.8751 for pair3 in minimum.

The PBR slope estimate for the time series pairs differs slightly from that for the HRV index pairs. The slope coefficient is equal to 0.9792 to 1.0000 with a 95% confidence interval, including 1. This result reveals that although there is a low correlation coefficient (r < 0.90) in the time series, the spectral components do not differ between HRV indices for each pair. Figure 3 shows the residual plot of each point for the estimation of agreement using PBR. Table 3. Statistical results of the HRV indices for ECG-PPG data pairs. Data were analyzed using CCC,

CC, and PBR. ρC, concordance correlation coefficient; Cb, bias correction factor; r, Pearson correlation coefficient; 95% CI, ρC confidence interval; RSD, residual standard deviation; slope, first-

order polynomial curve fitting. Statistics Parameters Ln_VLF Ln_LF Ln_HF

Concordance correlation coefficient

ρC 0.9996 0.9998 0.9863 95% CI 0.9983~0.9999 0.9995~0.9999 0.9364~0.9971

ρb 0.9999 1.0000 0.9954 Cb 0.9997 0.9999 0.9909

Correlation coefficient

r 0.9999 1.0000 0.9954 P P<0.0001 P<0.0001 P<0.0001

95% CI 0.9990~1.0000 0.9996~1.0000 0.9564~0.9995

Passing & Bablok regression

RSD 0.005581 0.007734 0.04446 Slope 1.0225 0.9816 1.0718

95% CI 1.0043~1.8500 0.9340~1.0313 0.6992~1.4134 Table 4. Statistical results of ECG-PPG time series data. Data were analyzed using CCC, CC, and PBR.

ρC, concordance correlation coefficient; Cb, bias correction factor; r, Pearson correlation coefficient; 95% CI, ρC confidence interval; RSD, residual standard deviation; slope, first-order polynomial curve

fitting. Statistics Para. Pair1 Pair2 Pair3 Pair4 Pair5 Pair6

Concordance correlation coefficient

ρC 0.9770 0.9871 0.8741 0.9808 0.9546 0.9907

95% CI 0.9716 ~0.9814

0.9841 ~0.9896

0.8495 ~0.8949

0.9768 ~0.9841

0.9447~0.9628

0.9887 ~0.9923

ρb 0.9776 0.9878 0.8751 0.9817 0.9558 0.9917 Cb 0.9994 0.9994 0.9988 0.9991 0.9988 0.9990

Correlation coefficient

r 0.9776 0.9878 0.8751 0.9817 0.9558 0.9917 P P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001 P<0.0001

95% CI 0.9723 ~0.9819

0.9849 ~0.9901

0.8506 ~0.8958

0.9778 ~0.9849

0.9460 ~0.9638

0.9899 ~0.9932

Passing & Bablok

regression

RSD 8.6606 6.4024 12.5126 5.0678 6.8006 3.0538 Slope 1.0000 1.0000 0.9800 0.9896 1.0000 0.9792

95% CI 0.97767 ~1.0090

0.9831 ~1.0192

0.9535 ~1.0000

0.9730 ~1.000

0.9895 ~1.0417

0.9655 ~1.0000


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Figure 3. PBR presents the residual plot of each point in terms of the orthogonal distance to the fitted line as a function of the ranks of the PPG.

4. Discussion

We compared three HRV indices from fingertip PPG-derived and handgrip ECG-derived HRV data. All of the statistical results indicated no systematic or proportional differences between the two methods. The agreement between the comparison data in the different measurements of HRV signals indicates that the fingertip PPG is as effective as the handgrip ECG for HRV assessment.

In conclusion, this study established the following aspects of HRV assessment: (a) the compared ECG-PPG data pairs in time series are closely related; (b) the HRV index pairs are correlated; (c) a relatively low coefficient of Ln_HF rather than Ln_VLF and Ln_LF is responsible for the pair3 of the time series in which the ρC is 0.8741; (d) the linearity is better in the frequency components than in the time series; (e) a fixed 5-min heart rate data length has no effect on the reliability of HRV indices.

The outcome of handgrip ECG- and PPG-based HRV analysis was identical in six. It is confident that the PPG may serve as a surrogate technique for the ECG in all parameters of HRV analysis at least in frequency domain. 5. Acknowledgments This research was supported by the Hallym University Research Fund, HRF-201301-012. 6. References [1] HeartMath (US). Autonomic Assessment Report. California, “HeartMath Research Center “,1996. [2] Kleiger RE, Miller JP, Bigger JT, Moss AJ, “Decreased heart rate variability and its association

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Heart Rate Variability (HRV) analysis using simultaneous ......For CC analysis, the results were reported with the correlation coefficient, P-value, and the 95% confidence interval