Comparative Analysis of the Phonocardiogram Denoising System Based-on Empirical Mode Decomposition (EMD) and Double-Density Discrete Wavelet Transform (DDDWT)

Phonocardiogram (PCG), one of the auscultation-based technique used as a diagnostic method of the heart condition, is a patient’s heart sound recording. The simplicity, non-invasive, and passive brings an advantage to implement this method as a diagnosis system. Nevertheless, PCG recordings are often interrupted by various sources, for instance, noise from the surrounding environment, respiratory or lung sounds, power disturbances, and movement of the surrounding skin, so inhibit the PCG implementation as a diagnosis method. Therefore, it requires an appropriate method to eliminate the noise that exists in the PCG signals. To get an appropriate method in the PCG system, we compare the Empirical Mode Decomposition (EMD) and Double-Density Discrete Wavelet Transform (DD-DWT) method as a denoising system to minimize the noise effect in the PCG signal. Observation of the system performance used thirty data from the normal heart sound added by the additive white Gaussian noise (AWGN), and the performance parameter used signal-to-noise ratio (SNR) and mean square error (MSE). Based on the result, we obtained the best SNR value of 25.55 dB for the EMD method and SNR value of 18.19 dB for DD-DWT. Also, we perceived the best MSE value of 0.01% for the EMD method, and 0.42% for the DD-DWT. The results obtained show that the denoising process using the EMD method is better than the DD-DWT to implement in the PCG signal.