Mobile ECG (electrocardiogram) monitoring system and monitoring method

Abstract

The invention discloses a mobile ECG (electrocardiogram) monitoring system and monitoring method. ECG signals are collected; in addition, IR-UWB (impulse radio-ultra wide band) radar signals are collected; after the collected ECG signals and the IR-UWB radar signals are synchronized and processed, a cascade connection CNN (convolutional neural network) is used for feature extraction, integration analysis and diagnosis classification to obtain monitoring results to be output. According to the embodiment of the invention, the IR-UWB radar signals are introduced to be used as the supplement of the monitored ECG signals during the monitoring; through the synchronization on the two signals, the correlation between the two signals is obtained; the monitoring accuracy is improved. The cascade connection CNN with high applicability and stable performance is used for feature extraction, integration analysis and diagnosis classification; the system performance stability is effectively ensured.

Description

technical field [0001] The invention relates to the monitoring technology of human physiological parameters, in particular to a movable electrocardiogram monitoring system and a monitoring method. Background technique [0002] Heart disease is a serious threat to human life and health, and its morbidity and mortality are ranked first among various diseases, and the suddenness of heart disease increases the difficulty of treatment. Therefore, prevention and treatment of heart disease has just become the top priority of people's maintenance of health. Mobile electrocardiogram (ECG) monitoring systems provide a good solution for effective heart disease prevention. On the one hand, its convenient portability enables this monitoring system to monitor the ECG signal of heart activity for a long time, so that it can catch occasional arrhythmia and other sudden heart diseases in time. On the other hand, with the rapid development of mobile Internet of Things and information techno...

AI Technical Summary

Problems solved by technology

However, ECG signal recording is inevitably affected by motion artifacts due to human muscle movement, resulting in a decrease in the accuracy of classification algorithms

Although some motion artifact suppression algorithms have been proposed, such as adaptive filtering methods and blind source separation methods, it is still difficult to completely remove motion artifacts

[0008] In summary, based on the inaccuracy of monitoring ECG signals and the subsequent inaccuracy of classifying ECG signals, the current ECG signal monitoring system is inaccurate in obtaining monitoring results based on the monitored ECG signal records, which leads to the inaccuracy of the human body. Misdiagnosis of arrhythmia

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