Cardiac beat classification method and device, wearable equipment and storage medium

Abstract

The invention relates to a heart beat classification method and device, wearable equipment and a storage medium. The method comprises the steps of obtaining target electrocardiogram data; identifyinga plurality of heart beats to be detected from the electrocardiogram image corresponding to the target electrocardiogram data; and inputting the plurality of heart beats to be measured into a pre-trained deep learning model, and determining a preset classification of each heart beat to be measured based on an output result of the deep learning model. According to the method and the device, the to-be-detected heart beats are identified from the electrocardiogram image corresponding to the target electrocardiogram data, and then the input to-be-detected heart beats are divided through the pre-trained deep learning model, so that the accuracy of classifying the to-be-detected heart beats can be improved.

Description

technical field [0001] The present disclosure relates to the field of wearable technology, and in particular to a heart beat classification method, device, wearable device and storage medium. Background technique [0002] Electrocardiogram (ECG) signals can record the electrophysiological activity of the heart in units of time, and have important reference value for evaluating the basic functions of the heart and pathological research. In recent years, with the development of wearable technology, the use of wearable technology to measure ECG signals has gradually been accepted by the public. [0003] At present, wearable devices that can measure ECG signals include bracelets, watches, chest stickers, and smart clothes, etc., which provide great convenience for users to perform self-service ECG detection. [0004] However, the existing wearable devices are easily affected by various factors (such as the user is in motion, dry skin, myoelectric interference or the electrode i...

AI Technical Summary

Problems solved by technology

[0004] However, the existing wearable devices are easily affected by various factors (such as the user is in motion, dry skin, myoelectric interference or the electrode interference of the sensor itself on the wearable device, etc.) in the process of detecting the user's ECG data. , resulting in the inaccuracy of the collected ECG data, which in turn will reduce the accuracy of subsequent heart beat classification based on the collected ECG data

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