Adults born after 1970s are more prone to cardiovascular diseases. Death rate percentage is quite high due to heart related diseases. Therefore, there is necessity to enquire the problem or detection of heart diseases earlier for their proper treatment. As, Valvular heart disease, that is, stenosis and regurgitation of heart valve, are also a major cause of heart failure; which can be diagnosed at early-stage by detection and analysis of heart sound signal, that is, HS signal. In this proposed work, an attempt has been made to detect and localize the major heart sounds, that is, S1 and S2. The work in this article consists of three parts. Firstly, self-acquisition of Phonocardiogram (PCG) and Electrocardiogram (ECG) signal through a self-assembled, data-acquisition set-up. The Phonocardiogram (PCG) signal is acquired from all the four auscultation areas, that is, Aortic, Pulmonic, Tricuspid and Mitral on human chest, using electronic stethoscope. Secondly, the major heart sounds, that is, S1 and S2are detected using 3rd Order Normalized Average Shannon energy Envelope (3rd Order NASE) Algorithm. Further, an auto-thresholding has been used to localize time gates of S1 and S2 and that of R-peaks of simultaneously recorded ECG signal. In third part; the successful detection rate of S1 and S2, from self-acquired PCG signals is computed and compared. A total of 280 samples from same subjects as well as from different subjects (of age group 15–30 years) have been taken in which 70 samples are taken from each auscultation area of human chest. Moreover, simultaneous recording of ECG has also been performed. It was analyzed and observed that detection and localization of S1 and S2 found 74% successful for the self-acquired heart sound signal, if the heart sound data is recorded from pulmonic position of Human chest. The success rate could be much higher, if standard data base of heart sound signal would be used for the same analysis method. The, remaining three auscultations areas, that is, Aortic, Tricuspid, and Mitral have smaller success rate of detection of S1 and S2 from self-acquired PCG signals. So, this work justifies that the Pulmonic position of heart is most suitable auscultation area for acquiring PCG signal for detection and localization of S1 and S2 much accurately and for analysis purpose.

Abstract-Phonocardiograph is a continuous, non-invasive, low cost and accurate recording and monitoring of valve functioning of heart using heart sound signal. When Hilbert Transform is directly applied, an Analytic Signal of Heart Sound Signal is obtained. In order to decipher the details of the signal, Hilbert-Huang Transform is applied on the Heart Sound Signal. The HHT has two step analyses: The first one is Empirical Mode Decomposition (EMD) and the second part is Hilbert Spectral Analysis (HSA). The HHT analysis has been applied for pre-exercise and post-exercise data acquired from a subject. The subject had to go for stair-exercise (75 stepped stairs) for this purpose.

The recent reports of the World Health Organization showed that a huge extent of the population below 55 years has become much prone to cardiac disease, and the death percentage has increased caused by various cardio vascular diseases (CVD). Moreover, in the Covid-19 pandemic situation, the people suffering from heart disease were found severely vulnerable to viral infections, which proved to be a major cause of increased death percentage. The CVD could be caused by dysfunction of heart valves which could end up with cardiac arrest. The prime method for early-stage detection of the heart valve dysfunction is analysis of major heart sounds occurring in a cardiac cycle. The proposed work dealt with exploration of S1 and S2, which are supposed to be prime sounds of Phonocardiogram (PCG) signal. Here, the proposed analysis has six steps. First, signalacquisition set-up which was assembled for acquiring PCG and ECG signals from the people having age between 15 to 40 years. Second step, pre-processing: in which the samples of PCG and ECG signals were prepared and the signal was denoised using modified Butterworth worth filter. The third step was the incorporation of Empirical Mode Decomposition to get Intrinsic Mode Functions i.e., frequency components of the PCG. Further, only two appropriate IMFs were selected and recombined to generate a combined component signal (CChs). In the fourth step; a Modified Shannon Energy Envelope algorithm (MSEE) i.e., 4th order Shannon energy Envelope was implemented to frame energy envelopes. In the fifth step; an adaptive-thresholding was used for the time-lobes formation followed by peak correction algorithm i.e., correction of time-lobe peaks. In the sixth and final step; time-lobes of the PCG signal were computed and were correlated with R-peaks of ECG signal, through which localization of S1 and S2 was done. A total of 40 samples of the PCG signal consisting of 195 cardiac cycles were taken for the analysis. It came out from the analysis of the self-acquired PCG signal that the best result of localization of S1 and S2 is obtained for the PCG signal acquired from the Pulmonic position. After analyzing the confusion matrix for the findings of the proposed method; accuracy & precision were 90.20%, sensitivity 100%, and an error rate of 9.8% was obtained. The accuracy of the method was found lesser if the PCG was acquired from the remaining three auscultation areas of the human chest. The proposed method was compared with three other earlier algorithms, out of which the proposed method showed a greater improvement. Moreover, the implementation of EMD followed by choosing a few specific IMFs for the formation energy envelope reduced the computation cost and enhanced the accuracy of the method, too.