Uterine EMG analysis: a dynamic approach for change detection and classification

Toward the goal of detecting preterm birth by characterizing events in the uterine electromyogram (EMG), the authors propose a method of detection and classification of events in this signal. Uterine EMG is considered as a nonstationary signal and the authors' approach consists of assuming piec...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2000-06, Vol.47 (6), p.748-756
Main Authors: Khalil, M., Duchene, J.
Format: Article
Language:English
Subjects:
Algorithms
Analysis of Variance
Bioengineering
Change detection
Classification
Decomposition
Detectors
Distributed control
Dynamics
Electromyography
Electromyography - classification
Electromyography - methods
Electromyography - statistics & numerical data
Event detection
Female
Fetal monitoring
Fetal Monitoring - classification
Fetal Monitoring - methods
Fetal Monitoring - statistics & numerical data
Frequency
Gestation
Gynecology
Humans
Labeling
Labor, Obstetric - physiology
Life Sciences
Likelihood Functions
Matrix decomposition
Neural networks
Nonlinear Dynamics
Pregnancy
Signal detection
Signal processing
Studies
Time Factors
Urology
Uterus - physiology
Wavelet transforms
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Summary:Toward the goal of detecting preterm birth by characterizing events in the uterine electromyogram (EMG), the authors propose a method of detection and classification of events in this signal. Uterine EMG is considered as a nonstationary signal and the authors' approach consists of assuming piecewise stationarity and using a dynamic change detector with no a priori knowledge of the parameters of the hypotheses on the process state to be detected. The detection approach is based on the dynamic cumulative sum (DCS) of the local generalized likelihood ratios associated with a multiscale decomposition using wavelet transform. This combination of DCS and multiscale decomposition was shown to be very efficient for detection of both frequency and energy changes. An unsupervised classification based on the comparison between variance-covariance matrices computed from selected scales of the decomposition was implemented after detection. Finally a class labeling based on neural networks was developed. This algorithm of detection-classification-labeling gives satisfactory results on uterine EMG: in most cases more than 80% of the events are correctly detected and classified whatever the term of gestation.
ISSN:0018-9294
1558-2531
DOI:10.1109/10.844224