Accurate Heart Sound Segmentation With Temporal Convolutional Network-Enhanced Duration Hidden Markov Model and Adaptive Calibration

OBJECTIVE: Accurate segmentation of heart sound signal stages is critical in cardiovascular disease analysis. METHODS: This study proposed the integration of a duration hidden Markov model (DHMM) with a temporal convolutional network (TCN) and an adaptive calibration mechanism (based on electrocardiogram signals) to enable the precise segmentation of complex heart sound signals. Multiple features of heart sound signals are extracted and utilized as model inputs, constructed a segmentation model architecture improved by TCN-based observation probability estimation and an attention mechanism integrated into the Viterbi algorithm. RESULTS: The experimental results demonstrated that the average accuracy of this method is 94.71 ± 2.64% at a segmentation error of 50 ms. The enhanced Viterbi algorithm elevated performance by approximately 9 percentage points. Furthermore, the adaptive calibration mechanism yielded an additional average accuracy increase of 1.41 percentage points and reduced the standard deviation by 1.21 percentage points. CONCLUSION: Compared to traditional methods employing Gaussian distribution-based observation probability estimation, the utilization of a TCN substantially enhanced state discrimination accuracy, achieving an improvement of approximately 3 percentage points. The refined Viterbi algorithm demonstrated superior performance relative to prior methodologies. SIGNIFICANCE: This method enables effective segmentation of complex heart sound data, delivering a high-precision solution for the automated analysis of heart sounds.