J. Biomedical Science and Engineering, 2010, 3, 193-199 doi:10.4236/jbise.2010.32025 Published Online February 2010 (http://www.SciRP.org/journal/jbise/ JBiSE ). Published Online February 2010 in SciRes. http://www.scirp.org/journal/jbise Classification of non stationary signals using multiscale decomposition Marwa Chendeb 1,2 , Mohamad Khalil 2* , David Hewson 1 , Jacques Duchêne 1 1 ICD, FRE CNRS 2848, University of Technology of Troyes, Troyes, France; 2 LaSTRe Laboratory, Engineering Section 1, Lebanese University, Tripoli, Lebanon. Email: * mkhalil@ieee.org Received 15 July 2008; revised 5 December 2009; accepted 11 December 2009. ABSTRACT The aim of this article is to develop an automatic al- gorithm for the classification of non stationary sig- nals. 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 pur- pose. Before classification, there is a need to detect events in the recorded signals. The discrimination criterion is based on the calculation of the ratio be- tween 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 ap- plicable 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. Keywords:Uterine EMG; Preterm Birth; Wavelet Packet; Best Basis; Event Classification 1. INTRODUCTION The automatic classification of non stationary signals is an important studied problem especially as the nonsta- tionarity precludes classification in the time or frequency domain [1]. The aim of this paper is to use the nonpara- metric representation wavelet packet transform (WPT) which is suitable for nonstationary signals and choose among the wavelet packets (WPs) the best basis for clas- sification. The application context is the classification of uterine electromyographic (EMG) events used for the prevention of preterm birth. The progress of labour can be assessed non-invasively using EMG signals from the uterus (the driving force for contractility) recorded from the abdominal surface [2,3]. Preterm labour and resultant preterm birth are the most important problems in perinatology [2,4,5]. Knowledge of labor commencement, as well as the pos- sible prediction of its starting time, would be of great interest in terms of limiting unnecessary stays in hospi- tals and adapting treatment to the actual state of the pregnancy. The principal events extracted from the rele- vant activities of uterine EMG are the contractions (CT). Other events can be of value for pre-term birth diagnosis: Alvarez (Alv) waves, foetus motions (MAF) and long-duration low-frequency (LDBF) waves [2] (Figure 1). Several works have been carried out on mammals, with electrodes placed on the uterine surface [2,5]. They demonstrated a modification in electrical activity during both preterm and term labor. In [6] the uterine EMG signals are classified using artificial neural networks method to distinguish the normal term labour from ab- normal preterm labour signals. [7] applied the wavelet transform on the uterine signals recorded using abdomi- nal surface electrodes. In literature, the best basis algo- rithm is used to find the best-adapted WP for a lot of goals such the detection [8,9], denoising [10], feature extraction and classification [11,12], etc. Saito and Coifman introduced the Local Discriminant Bases (LDB) to search a best basis for classification [12]. Wavelet packet analysis is used to extract the features of the sample DNA sequences in [13]. An index of discrimina- tion based on Kullback-Leibler distance is proposed as a way to select most discriminant wavelet packets for tex- ture classification in an image [14]. In this work, classification of uterine EMG events by allocating them to the physiological classes: CT, MAF, Alv, or LDBF waves.is based on their energy distribu- tion throughout the wavelet packet transform (WPT) which is used because it is characterized by the frequency