Alireza Zaeemzadeh - Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering College of Engineering, University of Tehran Tehran, Iran
Zahra Nafar - Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering College of Engineering, University of Tehran Tehran, Iran
Seyed-Kamaledin Setarehdan - Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering College of Engineering, University of Tehran Tehran, Iran

Heart sound segmentation is the primary step in automatic diagnosis of heart sounds. Since heart sound components have great diversity in frequency and amplitude, thefocus of this paper is on time domain analysis. Time intervals between consequent peaks have been clustered in time domainand statistical data were extracted. Then a reference point was labeled by using the clustered data. We propose a novel algorithm to segment the heart sound signals, by using extracteddata and the reference point. The performance of the algorithm has been evaluated using 240 periods of heart sound signalsrecorded from 12 subjects including normal and abnormal sounds. The algorithm has achieved a 93.8 percent precision and 100 percent of sensitivity during evaluation.

heart sound segmentation, recurrence time statistics,first and second heart sound detection, clustring