Heart sound classification method and system based on CNN combined with improved frequency wavelet slice transformation

Abstract

The invention provides a heart sound classification method and system based on CNN combined with improved frequency wavelet slice transformation, and the method comprises the steps: carrying out the preprocessing of an obtained heart sound signal, finding the position of each cardiac cycle through a hidden half Markov model, and carrying out the interception of each cardiac cycle signal; converting each intercepted one-dimensional cardiac cycle signal into a two-dimensional time-frequency image by using improved frequency slice wavelet transform; calculating sample entropies of the acquired heart sound signals respectively; comparing the two-dimensional time-frequency image with a preset sample entropy threshold, when the sample entropy of the heart sound signal is greater than the presetsample entropy threshold, performing network training and classification according to the two-dimensional time-frequency image by using a first convolutional neural network, otherwise, performing network training and classification according to the two-dimensional time-frequency image by using a second convolutional neural network; firstly, signals with different interference degrees are distinguished by using sample entropy, and then classification is performed by using different convolutional neural network models for different signals, so that the accuracy of heart sound signal classification is greatly improved.

Description

technical field [0001] The present disclosure relates to the technical field of heart sound classification, in particular to a heart sound classification method and system based on CNN combined with improved frequency wavelet slice transformation. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] Cardiovascular and cerebrovascular diseases have already been the diseases with the largest number of deaths in the world. Heart sound is an important physiological signal reflecting the health of the heart. Abnormal heart sounds can reflect many heart diseases and can help doctors make a diagnosis of a patient's condition. According to the heart activity, the heart sound signal can be divided into four phases, namely the first heart sound (S1 phase), the systolic phase, the second heart sound (S2 phase) and the diastolic phase. Abnormal heart ...

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Problems solved by technology

These problems create factual difficulties in classifying heart sounds

[0004] The inventors of the present disclosure have found that (1) most of the current heart sound classification researchers have adopted feature selection, some algorithms of machine learning and deep learning, such as neural networks, support vector machines, random forests, decision trees and k-nearest neighbors, but The heart sound signal contains many interference signals, and it is often impossible to train a better model algorithm by directly extracting it, so that accurate classification cannot be achieved; (2) The difference between the heart sound signals of different individuals and between different sampling devices The feature extraction and training of heart sound signals are simple, and the trained network model often cannot accurately classify various heart sound signals.

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