1. 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

2. Overview Introduction What are IUP and EHG? Previous studies
Wiener filter prediction
Results and discussion
Conclusions and future work

3. 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
* = current/former members of CNEL

4. Introduction Biology inspires models Apps. with biological signals
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
BIOLOGY
MODELS

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

6. Labor monitoring Intrauterine pressure IUPC limitations
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.

7. Labor monitoring Electrohysterography EHG limitations
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.

8. EHG and IUP example

9. Previous EHG studies Correlation with IUP Predicting delivery
Generated from same underlying phenomenon.
Hand-excised contractions, correlation
IUP feature: integral
EHG feature: energy between Hz
r = 0.76, Maul et al., 2004
Predicting delivery
EHG feature: spectral peak freq., 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

10. 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.

11. Methods Data collection
303 pregant females monitored at Shands between July 2003 and Jan
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)

12. Methods, con’t IUP signal preprocessing
Non-causal median filter, ±5 seconds, to remove spiky noise.
Downsampled from 2 Hz to 0.2 Hz

13. Methods, con’t EHG signal preprocessing Zero mean, unity variance.
Downsampled from 200 Hz to 4 Hz (relavent bandwidth from literature).
Rectified (nonlinear operation, crude energy estimate).
Downsampled from 4 Hz to 0.2 Hz (shorter filters, faster training, no affect on under training).

14. 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.

15. Experiments, N = 50 sec Single channel, single patient
Each group of points is from the best model/best test window for each patient/channel

16. 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

17. 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
Channel 7
0.76
0.62
Time 16
30.3
Model
11.2
Pt*Ch 91
16.9
Ch*Time
112
3.4
Ch*Model
0.98

18. 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

19. 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