Empirical Mode Decomposition (EMD) Based Denoising Method for Heart Sound Signal and its Performance Analysis

On considering the physical changes, the human cycle undergoes various issues and health problems either temporarily or permanently, in this cardio-vascular disease causes a serious issue on comparing to other diseases. This cardio-vascular disease that affects the blood vessels in the heart which might increase the risk of getting affected from chest pain, stroke, heart attack and severe heart abnormalities. Here the cardio-vascular disease can be predicted using certain techniques with help of PCG signals. The phonocardiogram is the recorded data of the heart beat and these recordings of PCG signals have the unwanted environmental noises. This work involved in removing the unwanted noise(preprocessing) of the PCG signal using empirical mode decomposition (EMD) technique. This work provides an PCG denoising approach based on EMD that is successful and efficient. The EMD method is flexible and effective, and it is frequently utilized in the biomedical industry. The EMD is based on signal extraction linked with distinct intrinsic time scales, ranging from high frequency modes to low frequency modes. An iterative procedure called the Sifting process divides a noisy signal into many Intrinsic Mode Functions (IMF). The method has effective noise reduction, it can be assessed by calculating the signal to noise ratio value in comparison to the raw and filtered PCG signal.