Quick recognizing and processing method and system for ECG signal

Abstract

The invention discloses a quick recognizing and processing method and system for an ECG signal. The method comprises the following steps: denoising an input original ECG signal to obtain an ECG signal; inputting the ECG signal in a basic characteristic signal recognizing and processing flow or a neural network signal recognizing and processing flow according to the complexity of the ECG signal; and outputting a recognizing and processing result. The provided quick recognizing and processing method and system for the ECG signal improves the signal recognition efficiency and accuracy of analysisresults, and can be applied to various embedded platforms.

Description

Technical field [0001] The invention relates to the technical field of electrocardiographic signal analysis, in particular to a method and system for rapid identification and processing of electrocardiographic signals. Background technique [0002] At present, machine learning and deep learning are often used to identify and classify ECG signals, especially convolutional neural networks (CNN). However, in the process of real-time detection of ECG signals, if CNN has been used to identify ECG signals Classification, because a large number of ECG signals have been recognized indiscriminately, calculation delays are often generated and time-consuming, which also brings a large burden to the computer; at the same time, due to the need to transmit terminal data with a huge amount of sampling rate data Return to the data server or in the hospital for storage and analysis. If it is returned in real time, packet loss or transmission errors are prone to occur, resulting in missing recorde...

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Problems solved by technology

[0002] At present, machine learning and deep learning are often used to identify and classify ECG signals, especially using convolutional neural network (CNN). However, in the process of real-time detection of ECG signals, if CNN is always used to identify Classification, because a large number of ECG signals have been identified indiscriminately, calculation delays often occur, and it takes a long time, which also brings a large burden to the computer; at the same time, due to the need to transmit terminal data with a huge sampling rate After returning to the data server or hospital for storage and analysis, if the real-time return is prone to packet loss or transmission errors, resulting in the loss of recorded or displayed waveforms; at this time, if the data is sent to the neural network for recognition, it will Wrong results appear, which further leads to low accuracy of analysis results

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