Fractional Fourier Time-Frequency Representation for Heart Sound Classification - A. Nehary, Ebrahim; Rajan, Sreeraman (CMBES Proceedings, 2024)
Early detection of heart sounds can significantly reduce mortality rates by allowing physicians to intervene on time. However, manual heart sound analysis is subjective and it relies heavily on the skills and experience of the physi- cian. Fortunately, deep learning has emerged as a promising method for heart sound classification. Time-frequency repre- sentations (TFR) such as spectrograms and continuous wavelet transforms (CWT), and Mel-Frequency ceptral coefficients have been widely accepted input representations for heart sound rep- resentation. This study proposes a combination of fractional Fourier time-frequency representation (FrFT_TFR) and a deep learning model for heart sound classification and uses a public dataset to demonstrate the efficacy of the proposed representa- tion.Classification using deep learning model with FrFT_TFR as input outperforms that obtained with spectrograms and CWT as inputs by approximately 4% and by 13% over Mel-frequency cepstral coefficients (MFCC) as inputs. The results underscore the effectiveness of using FrFT_TFR for heart sound classifica- tion for initial heart sound diagnosis.