PCG classification through spectrogram using transfer learning

Abstract

Heart rate classification is a challenging problem primarily due to spectral overlap of normal heart sound with internal sources like extra heart sounds, extra systole, murmurs, respiration sounds and external sources like body motion. In order to address this challenging problem, we have proposed a technique that relies on signal filtering, time segmentation, spectrogram generation, hybrid classification and finally a voting based mechanism. The proposed method carries out analysis at cycle as well as at signal level. Evaluation of the proposed technique on a challenging public dataset (PASCAL 2011) results in precision, recall and accuracy values of greater than 95% using 5-fold cross validation. Furthermore, the reported results also validate our claim that 2–3 s of data suffices for classification.