Phonocardiogram (PCG) monitoring on newborns is one of the most important and\nchallenging tasks in the heart assessment in the early ages of life. In this paper, we present a novel\napproach for cardiac monitoring applied in PCG data. This basic system coupled with denoising,\nsegmentation, cardiac cycle selection and classification of heart sound can be used widely for a large\nnumber of the data. This paper describes the problems and additional advantages of the PCG\nmethod including the possibility of recording heart sound at home, removing unwanted noises\nand data reduction on a mobile device, and an intelligent system to diagnose heart diseases on\nthe cloud server. A wide range of physiological features from various analysis domains, including\nmodeling, time/frequency domain analysis, an algorithm, etc., is proposed in order to extract features\nwhich will be considered as inputs for the classifier. In order to record the PCG data set from multiple\nsubjects over one year, an electronic stethoscope was used for collecting data that was connected to\na mobile device. In this study, we used different types of classifiers in order to distinguish between\nhealthy and pathological heart sounds, and a comparison on the performances revealed that support\nvector machine (SVM) provides 92.2% accuracy and AUC = 0.98 in a time of 1.14 seconds for training,\non a dataset of 116 samples.
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