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    This study introduces a novel method for precise heart sound segmentation using a duration hidden Markov model (DHMM) and temporal convolutional network (TCN). The approach achieves high accuracy, aiding cardiovascular disease analysis.

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    Area of Science:

    • Cardiology
    • Biomedical Signal Processing
    • Machine Learning

    Background:

    • Accurate segmentation of heart sound signal stages is crucial for diagnosing cardiovascular diseases.
    • Traditional methods face challenges in precisely segmenting complex heart sound signals.

    Purpose of the Study:

    • To develop an accurate and robust method for segmenting complex heart sound signals.
    • To improve the precision of automated heart sound analysis for cardiovascular disease detection.

    Main Methods:

    • Integration of a duration hidden Markov model (DHMM) with a temporal convolutional network (TCN).
    • Incorporation of an adaptive calibration mechanism using electrocardiogram (ECG) signals.
    • Development of a segmentation model architecture featuring TCN-based observation probability estimation and an attention mechanism within the Viterbi algorithm.

    Main Results:

    • Achieved an average accuracy of 94.71 ± 2.64% with a 50ms segmentation error.
    • The enhanced Viterbi algorithm improved performance by approximately 9 percentage points.
    • The adaptive calibration mechanism further increased accuracy by 1.41 percentage points and reduced standard deviation by 1.21 percentage points.

    Conclusions:

    • The proposed TCN-based method significantly enhances state discrimination accuracy compared to traditional Gaussian distribution methods.
    • The refined Viterbi algorithm demonstrates superior performance for heart sound segmentation.
    • This method provides a high-precision solution for automated heart sound analysis, aiding in cardiovascular disease diagnosis.