Processing Human Physiological Signals from Experimental Data

In our experimental setup, we perform comprehensive processing on acquired human physiological signals. The initial phase involves extracting heart sound signals using signal segmentation algorithms, typically implemented through envelope detection or wavelet transform techniques. We then conduct both time-domain and frequency-domain analysis on these extracted signals. For time-domain analysis, we examine temporal characteristics and waveform variations using methods like amplitude analysis, duration measurement, and heart sound segmentation, which can be implemented through peak detection algorithms and waveform feature extraction functions. Frequency-domain analysis employs Fourier Transform or Power Spectral Density calculations to reveal frequency components and spectral characteristics of the heart sounds. These analytical approaches provide deeper insights into heart sound features and variations, offering valuable information for cardiovascular health research and diagnostic applications. The processing pipeline can be implemented using signal processing libraries such as MATLAB's Signal Processing Toolbox or Python's SciPy library with appropriate filtering and spectral analysis functions.