Automatic Acoustic Diagnosis of Heartbeats

Automatic identification of heart irregularities based on the respective acoustic emissions is a relevant research field which receives ever-increasing attention over the last years. Devices such as digital stethoscope and smartphones can record the heartbeat sounds and are easily accessible, making this method more appealing. This paper presents different automatic procedures to classify heartbeat sounds coming from such devices into five different labels: normal, murmur, extra heart sound, extrasystole and artifact so that even people without medical knowledge can detect heart irregularities. The data used in this paper come from two different datasets. The first dataset is collected through an iPhone application whereas the second one is collected from a digital stethoscope. To be able to classify heartbeat sounds, time and frequency domain features are extracted and modeled by different machine learning algorithms, i.e. k-NN, random forest, SVM and ANNs. We report the achieved performances and a thorough comparison.