Shannon Energy Algorithm for Heart Sound Signal Envelope Extraction

The Shannon Energy Algorithm serves as a method for extracting envelopes from heart sound signals. In practical implementation, this algorithm typically involves calculating the Shannon energy of the signal frames, followed by smoothing operations to obtain the envelope curve. The algorithm effectively enhances high-amplitude components while suppressing low-amplitude noise, making it particularly suitable for heart sound analysis. Furthermore, a complementary training program is included for signal processing, whose main functionality involves extracting signal features such as statistical characteristics, frequency domain parameters, and time-frequency features to facilitate signal classification and identification using machine learning classifiers like SVM or neural networks. Beyond these core methods, other algorithms and techniques can be considered to further optimize signal processing effectiveness, including wavelet transform for multi-resolution analysis, time-frequency analysis for non-stationary signal characterization, and empirical mode decomposition for adaptive signal processing. By continuously improving and optimizing the signal processing pipeline through feature selection optimization and classifier parameter tuning, the accuracy and reliability of heart sound signal analysis can be significantly enhanced, thereby providing greater possibilities for research and applications in related medical diagnostic fields.