Transcript Biologically inspired noise-robust speech recognition for

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
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Introduction
What are IUP and EHG?
Previous studies
Wiener filter prediction
Results and discussion
Conclusions and future work
Collaborators
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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
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Human factor cepstral coeffs
Energy redistribution
Freeman model, ESN, LSM
Spike-based circuits,
BIOLOGY
algorithms
• Apps. with biological signals
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HFCC, ER
Bat acoustics
Brain-machine interfaces
EEG, fMRI research
Electrohysterography
MODELS
Prenatal monitoring
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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
N 1
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– y(n)--model output
y(n)  w(i) x(n  i)
– x(n)--EHG input
i 0
– 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.
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
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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-ontrain 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
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21.8
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5.2-13.7
Channel
7
0.76
0.62
9.3-10.3
Time
16
30.3
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8.7-11.2
Model
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11.2
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9.2-10.5
Pt*Ch
91
16.9
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3.4-17.6
Ch*Time
112
3.4
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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
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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