Phonocardiogram-based diagnosis using machine learning : parametric estimation with multivariant classification

The heart sound signal, Phonocardiogram (PCG) is difficult to interpret even for experienced cardiologists. Interpretation are very subjective depending on the hearing ability of the physician. mHealth has been the adopted approach towards quick diagnosis using mobile devices. However, it has been challenging due to the required high quality of data, high computation load, and high-power consumption. The aim of this paper is to diagnose the heart condition based on Phonocardiogram analysis using Machine Learning techniques assuming limited processing power to be encapsulated later in a mobile device. The cardiovascular system is modelled in a transfer function to provide PCG signal recording as it would be recorded at the wrist. The signal is, then, decomposed using filter bank and the analysed using discriminant function. The results showed that PCG with a 19 dB Signal-to-Noise-Ratio can lead to 97.33% successful diagnosis.