Prediction of intrauterine pressure from electrohysterography using optimal

linear filtering

Mark D. Skowronski

Computational Neuro-Engineering Lab

Electrical and Computer Engineering

University of Florida

Gainesville, FL, USA

August 31, 2005

Overview• Introduction

• What are IUP and EHG?

• Previous studies

• Wiener filter prediction

• Results and discussion

• Conclusions and future work

Collaborators

• Neil Euliano* (P.I.), Convergent Eng., Gainesville, FL• John Harris*, Assoc. Prof. ECE, CNEL, UF• Tammy Euliano, Assoc. Prof. Anesthesiology, UF• Dorothee Marossero*, Convergent Eng., Gainesville, FL• Rod Edwards, Obstetrics and Gynecology, UF• Support from NSF, DMI-0239060

* = current/former members of CNEL

Introduction• Biology inspires models

– Human factor cepstral coeffs– Energy redistribution– Freeman model, ESN, LSM– Spike-based circuits, algorithms

• Apps. with biological signals– HFCC, ER– Bat acoustics– Brain-machine interfaces– EEG, fMRI research– Electrohysterography

BIOLOGYMODELS

Prenatal monitoring• Intrauterine pressure (IUP)• Tocodynamometry (Toco)• Electrohysterography (EHG)• Ultrasound

Labor monitoring• Intrauterine pressure

– Uterine muscle activity (contractions) exerts force on the fetus towards cervix.

– Force is measured using intrauterine pressure catheter (IUPC).

– Used to monitor progression of labor.

• IUPC limitations– Used only after membrane rupture.– Internal, invasive technique, infection risk.– Requires presence of obstetric indicators to

justify risk.

Labor monitoring• Electrohysterography

– Skin electrodes, noninvasive.– Macroscopic muscle activity.– Multiple simultaneous measurements possible, more

information about labor state.– Useful throughout pregnancy.

• EHG limitations– Difficult to reliably measure muscle activity through

skin.• Variable skin resistance, preparation.• Variable distance to muscles (fetal shifts).

– Electrode placement repeatability.– Indirect monitoring method.

EHG and IUP example

Previous EHG studies• Correlation with IUP

– Generated from same underlying phenomenon.– Hand-excised contractions, correlation

• IUP feature: integral• EHG feature: energy between 0.3-1.0 Hz• r = 0.76, Maul et al., 2004

• Predicting delivery– EHG feature: spectral peak freq., 0.3-1.0 Hz– Peak freq. increases as time to delivery decreases– Accurate 24 hours before delivery, Maner et al., 2003

• No previous studies of continuous IUP prediction from EHG

IUP prediction from EHG• Proposed method: Wiener filter solution– y(n)--model output– x(n)--EHG input– w(n)--Wiener filter coefficients, length N

• Properties– Causal, linear FIR filter, optimal in MSE sense.– Closed-form solution, easy to train.– Output is projection of input space onto vector of

filter coefficients, real-time implementation.– Competent baseline algorithm, useful in developing

future more sophisticated prediction models.

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Methods• Data collection

– 303 pregant females monitored at Shands between July 2003 and Jan. 2005.

– 8-channel EHG data was collected, 200 samples/sec/channel, 16-bit resolution.

– Of those, 32 simultaneously monitored with IUPC, 2 samples/sec, 8-bit resolution.

– Of those, 14 remained after screening• At least 30 minutes of data (10 patients)• At term (3 patients)• No obvious data artifacts (5 patients)

Methods, con’t• IUP signal preprocessing

– Non-causal median filter, ±5 seconds, to remove spiky noise.

– Downsampled from 2 Hz to 0.2 Hz

Methods, con’t• EHG signal preprocessing

1. Zero mean, unity variance.2. Downsampled from 200 Hz to 4 Hz

(relavent bandwidth from literature).3. Rectified (nonlinear operation, crude

energy estimate).4. Downsampled from 4 Hz to 0.2 Hz (shorter

filters, faster training, no affect on under training).

Experiments• Single channel, single patient

– 10-minute test/train windows– Each line below is from the best model/best

channel/best test window for each patient (test-on-train results excluded)

Performance saturates at 50 sec.

Experiments, N = 50 sec• Single channel, single patient

– Each group of points is from the best model/best test window for each patient/channel

Prediction examples, N = 50 sec

Pt. 41, ch. 2, r = 0.90, RMS error = 3.7 mmHg

Pt. 229, ch. 8, r = 0.86, RMS error = 10.0 mmHg

Analysis of variance• 4-way ANOVA

– Dependent variable: RMS error.– Independent variables: patient, channel, time (test

window), model (train window).– All interactions not listed below were insignificant.

Factor d.f. F p Range, mmHg

Patient 13 21.8 0 5.2-13.7

Channel 7 0.76 0.62 9.3-10.3

Time 16 30.3 0 8.7-11.2

Model 16 11.2 0 9.2-10.5

Pt*Ch 91 16.9 0 3.4-17.6

Ch*Time 112 3.4 0 7.3-12.1

Ch*Model 112 0.76 0.98 8.7-11.8

Conclusions• Wiener filter/rectified EHG useful for

predicting IUP– Best of the best: r > 0.90, RMS error < 9

mmHg– RMS error sensitive to factors: patient, time,

model, pt*ch, ch*time, ch*model– RMS error not sensitive to factors: channel,

pt*time, pt*model, time*model, all higher interactions

Future work• Better figures of merit

• Single patient, multi-channel

• Multi-patient, multi-channel

• Better features besides rectified EHG

• Non-causal Wiener filter

• More powerful prediction models

• Weighted RMS error/squared prediction