Electrocardiograph detection method based on multi-scale deep learning neural network

Abstract

The invention provides an electrocardiograph detection method based on a multi-scale deep learning neural network. The method includes the steps that a server receives to-be-analyzed electrocardiograph data sent by a client side, wherein the to-be-analyzed electrocardiograph data is collected by a collecting unit of the client side or collected by a collecting device of an intelligent client side and then transmitted to the client side; the server conducts arrhythmia recognition treatment on the to-be-analyzed electrocardiograph data through a pre-established arrhythmia detection model to obtain a recognition result, wherein the arrhythmia detection model is established in advance through a multi-scale deep convolution artificial neural network algorithm training electrocardiograph data sample; the server sends the recognition result to the client side so that the recognition result can be displayed by the client side. By means of the method, the electrocardiograph data can be accurately analyzed, the method is convenient and rapid to implement, and practicability is improved.

Description

technical field [0001] The invention relates to electrocardiographic detection technology, in particular to an electrocardiographic detection method based on a multi-scale deep learning neural network. Background technique [0002] The current arrhythmia analysis instrument mainly adopts waveform analysis method and template matching method. The waveform analysis method is based on the analysis of the characteristic waveform in the ECG signal (such as the electrocardiogram). The characteristic waveform of the ECG signal refers to some indicative peaks and valleys in the ECG signal, such as P, Q, R, S, T waves, etc., where the P wave represents atrial depolarization, and the QRS complex (such as figure 1 shown) represents ventricular depolarization, and T wave represents ventricular repolarization. The waveform analysis method first obtains the characteristic waveform parameters, such as the characteristic waveform amplitude, time length, rise / fall time, waveform interval, ...

AI Technical Summary

Problems solved by technology

For example, the waveform analysis method uses the empirical threshold value of the solidified characteristic waveform parameters as the judgment basis. Although it is universal, when encountering people with special age groups and special constitutions, especially when neonatal ECG detection, neonatal ECG The signal is weak, the duration of the QRS wave is short, and the crying of the newborn will greatly interfere with the ECG detection, so it is easy to get wrong arrhythmia analysis results

However, the template matching method can only make effective judgments when the R wave shape of the detected person is quite different from the template, and cannot effectively judge the arrhythmia waveforms with insignificant differences.

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