Logistic Regression-HSMM-based Heart Sound Segmentation

The identification of the exact positions of the first and second heart sounds within a phonocardiogram (PCG), or heart sound segmentation, is an essential step in the automatic analysis of heart sound recordings, allowing for the classification of pathological events. While threshold-based segmentation methods have shown modest success, probabilistic models, such as hidden Markov models, have recently been shown to surpass the capabilities of previous methods. Segmentation performance is further improved when apriori information about the expected duration of the states is incorporated into the model, such as in a hidden semi-Markov model (HSMM). This code provides Matlab algorithms to perform HSMM-based heart sound segmentation.

在心音图（PCG）中精确识别第一和第二心音的位置，即心音分割，是自动分析心音记录的关键步骤，它使得对病理事件的分类成为可能。尽管基于阈值的分割方法已显示出一定的成功，但概率模型，例如隐马尔可夫模型，最近已被证明超越了先前方法的能力。当将关于预期状态持续时间的先验信息纳入模型时，如隐半马尔可夫模型（HSMM）中，分割性能将得到进一步改善。本代码提供了基于HSMM的心音分割的Matlab算法。