Heart sound feature recognition method based on wavelet spectral analysis

Through the heart sound feature recognition method based on wavelet spectrum analysis, the limitations and insufficient adaptability of the existing technology central sound signal processing are solved, and efficient feature extraction and recognition are achieved to adapt to changes in different individuals and physiological states.

CN119760588APending Publication Date: 2025-04-04THE FIRST MEDICAL CENT CHINESE PLA GENERAL HOSPITAL
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Patent Information

Application Number
CN202411695958.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-25
Publication Date
2025-04-04

AI Technical Summary

Technical Problem

The existing heart sound analysis methods have limitations in dealing with non-stationary and nonlinear heart sound signals, and it is difficult to effectively deal with interference such as environmental noise, breathing sound and body movement. In addition, traditional feature extraction and classification algorithms lack adaptability and are difficult to adapt to changes in heart sound characteristics in different individuals and physiological states.

Method used

The heart sound feature recognition method based on wavelet spectrum analysis is adopted, and discrete wavelet transformation decomposition is performed through signal filtering and segmentation processing, multi-dimensional statistical features are calculated, and dimensionality reduction is achieved through principal component analysis, and feature fit and recognition is performed using Gaussian mixed model and hidden Markov model, and dynamic adjustment factors are introduced to adaptively adjust the classification threshold.

Benefits of technology

The quality and signal-to-noise ratio of the heart sound signal are improved, the distinction and characterization ability of the characteristics are enhanced, and the adaptability and accuracy of the heart sound feature recognition of different individuals and physiological states is achieved.

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Abstract

The invention relates to the technical field of heart sound signal processing, and discloses a heart sound feature recognition method based on wavelet spectral analysis. The method comprises the following steps: collecting heart sound signal data and carrying out signal filtering and segmentation processing to obtain segmented cardiac cycle signals; performing n-level discrete wavelet transform decomposition to obtain an approximation coefficient and n detail coefficients; performing multi-dimensional statistical feature calculation to obtain a multi-dimensional feature vector; performing principal component analysis dimension reduction processing to obtain a dimension reduction feature vector; fitting feature distribution by using a Gaussian mixture model and introducing a dynamic adjustment factor to obtain an adaptive classification threshold; the hidden Markov model is constructed through the dimension reduction feature vectors, the likelihood probability is calculated through a forward algorithm, the likelihood probability is compared with a self-adaptive classification threshold value, and the heart sound feature recognition result is output. The time-frequency features of the heart sound signals are fully utilized, the feature distinction degree and the recognition accuracy are improved, and the recognition accuracy is improved. And meanwhile, the method has good robustness and generalization ability.
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