Human identity verification based on Mel frequency analysis of digital heart sounds

This paper presents new results in human identity verification via frequency analysis of cardiac sounds. More specifically, the paper proposes a pattern recognition approach based on a feature set of 13 Mel Frequency Cepstral Coefficients (MFCCs) extracted from the first (S1) and second (S2) heart sounds and a metric based on the power ratio of S1 to S2. The new algorithm yields significantly better performances with respect to the previous method based on the Chirp z-transform, guaranteeing an equal error rate (EER) below 9%.