Automatic Heart Sound Analysis Module Based on Stockwell Transform Applied on Auto-Diagnosis and Telemedicine Applications Ali Moukadem, Alain Dieterlen MIPS Laboratory University of Haute Alsace Mulhouse, FRANCE ali.moukadem@uha.fr, alain.dieterlen@uha.fr Chrisitian Brandt University Hospital of Strasbourg CIC, Inserm christian.brandt@wanadoo.fr Abstract—The aim of this paper is to present an automatic heart sound analysis method which can be used for auto- diagnosis and telemedicine applications. One of the first and most important phases in the analysis of heart sounds, is the segmentation process which partitions the sound into cardiac cycles and further into S1 (first heart sound), systole, S2 (second heart sound) and diastole. The heart sounds (S1 and S2) are localized by applying the Shannon energy of the local spectrum calculated by the S-Transform. Then, to distinguish between the first and the second heart sound, a feature extraction method based on S-Transform is also presented. The methods are evaluated on a dataset of 80 subjects, including 40 patients with cardiac pathologies sounds. Keywords-component; Time-Frequency, S-Transform, Heart Sounds, Auto-Diagnosis. I. INTRODUCTION The advancement of technology has paved the way for signal processing methods to be implemented and applied in many simple tools useful in everyday life. This is most notable in the medical technology field where contributions involving the intelligent applications have boosted the quality of diagnosis. Proposing an objective signal processing methods able to extract relevant information from biosignals is a great challenge in telemedicine and auto- diagnosis fields. For the cardiac system, many signals can be treated and monitored; ElectroCardioGram (ECG), PhonoCardioGram (PCG), Echo/Doppler and pressure monitor. The interest of this paper is the PCG signal. PCG and auscultation are noninvasive, low-cost and accurate for diagnosing some heart diseases. The PCG signal confirms, and mostly, refines the auscultation data and provides further information about the acoustic activity concerning the chronology of the pathological signs in the cardiac cycle, by locating them with respect to the normal heart sounds. The cardiac sounds are by definition non-stationary signals, and are located within the low frequency range, approximately between 10 and 750 Hz. The analysis of the cardiac sounds, solely based on the human ear, remains insufficient for a reliable diagnosis of cardiac pathologies, and for a clinician to obtain all the qualitative and quantitative information about cardiac activity especially in the field of time intervals. Information, such as the temporal localization of the heart sounds, the number of their internal components, their frequency content, and the significance of diastolic and systolic murmurs, could all be studied directly on the PCG signal. In order to recognize and classify cardiovascular pathologies, advanced methods and techniques of signal processing and artificial intelligence will be used. For that, different approaches could be considered for improve the electronic stethoscope:  Tool with embedded autonomous analysis, simple for home use by the general public for the purpose of auto- diagnosis, monitoring and warning in case of necessity.  Tool with sophisticated analysis (coupled to a PC, Bluetooth link) for the use of professionals in order to make an in-depth medical diagnosis and to train the medical students. Whatever the approach, one of the first and most important phases in the analysis of heart sounds, is the segmentation of heart sounds. Heart sound segmentation partitions the PCG signals into cardiac cycles and further into S1 (first heart sound), systole, S2 (second heart sound) and diastole. Identification of the two phases of the cardiac cycle and of the heart sounds with robust differentiation between S1 and S2 even in the presence of additional heart sounds and/or murmurs is a first step in this challenge. Then there is a need to measure accurately S1 and S2 allowing the progression to automatic diagnosis of heart murmurs with the distinction of ejection and regurgitation murmurs. This phase of autonomous detection, without the help of ECG is based on signal processing tools such as: Shannon energy [1], Hilbert Transform [2], high order statistics [3], hidden Markov model [4], etc. In this study, we present a new module for heart sounds analysis that aims to segment automatically the heart sound. The goal of this study is to develop a generic tool, suitable for clinical and home monitoring use, robust to noise, and applicable to diverse pathological and normal heart sound signals without the necessity of any previous information about the subject. The proposed module can be divided into two main blocks: localization of heart sounds and classification block to distinguish between S1 and S2. The proposed methods are evaluated based on a database of 80 subjects (40 pathologic). This study is made under the 259 Copyright (c) IARIA, 2013. ISBN: 978-1-61208-252-3 eTELEMED 2013 : The Fifth International Conference on eHealth, Telemedicine, and Social Medicine