Method and device for performing united classifying on electrocardio signal and cardiac vibration signal based on neural network
An electrocardiographic signal and neural network technology, applied in the field of medical signal processing, can solve the problems of classification accuracy discount, signal quality limitation, complex cardiac cycle, etc., to achieve the effect of increasing dimension, making breakthroughs in accuracy, and facilitating data analysis.
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[0038] The present invention will be further described below in conjunction with specific examples and drawings.
[0039] Such as Figure one Shown is an overall technical block diagram of an embodiment of the present invention. The present invention is a method for joint classification of ECG and ECG signals based on neural networks, which is divided into two stages: the first stage, the ECG and ECG data sets are used to train their respective networks, including network training modules; In the stage, the collected ECG and concussion signals are classified, including signal preprocessing and denoising module, feature wave extraction and frequency map conversion module, neural network module and classification module. The signal preprocessing and denoising module is used to denoise and filter the signal; the characteristic wave extraction and frequency map conversion module is used to extract the characteristic waves such as R wave (ECG signal) and AO wave (heart shock signal) ...
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