Classification of non stationary signals using multiscale decomposition

Abstract

The aim of this article is to develop an automatic algorithm for the classification of non stationary signals. The application context is to classify uterine electromyogram (EMG) events to prevent the onset of preterm birth. The idea is to discriminate between the events by allocating them to the physiological classes: contractions, foetus motions, Alvarez or Long Duration Low Frequency waves. Our method is based on the Wavelet Packet (WP) decomposition and the choice of a best basis for classification purpose. Before classification, there is a need to detect events in the recorded signals. The discrimination criterion is based on the calculation of the ratio between intra-class variance and total variance (sum of the intra-class and inter-class variances), calculated directly from the coefficients of the selected WP. We evaluated the performance of the algorithm on real signals by using the classification methods Neural Networks (NN) and Support Vector Machines (SVM). Subband energies of the best selected WP are used as effective features. The determined best basis is applicable to a wide range of uterine EMG signals from large range of patients. In most cases, more than 85% of events are well classified whatever the term of gestation.

Automatic Contraction Detection Using Uterine Electromyography

“…The electrohysterogram (EHG) is an alternative to the TOCO and IUP methods, and it is obtained noninvasively through electrodes placed on the abdominal surface [6][7][8][9]. The EHG has been considered an appropriate tool for long-term pregnancy and childbirth monitoring due to its non-invasive nature.…”

“…A frequency content criterion was a central parameter. Later, Chendeb et al [7] used wavelet packets for the same above-mentioned classes as in [20], using as the main criteria its frequential content. Wavelet packet nodes were allocated to the defined EHG contraction classes.…”