Wearable electrocardiogram monitoring system based on cloud framework and monitoring method thereof

Abstract

The invention provides a wearable electrocardiogram monitoring system based on a cloud framework and a monitoring method thereof. A wearable electrocardiogram monitoring device can basically meet therequirements of convenience and comfort in wearing; a denoising circuit aiming at the special processing of electrocardiogram signals is used for denoising at multiple layers of hardware and software,so that the anti-interference rate of the electrocardiogram signals is increased, and the accuracy and integrity of the signals are guaranteed. By adopting an intelligent electrocardiogram algorithmprovided by an intelligent mobile phone terminal and a method for judging, intercepting and simply classifying and then packaging and uploading electrocardiogram signals, the problems of large electrocardiogram data volume and incapability of uploading in time are solved, the defects of an automatic diagnosis technology are overcome, and the efficiency of the monitoring system is improved. By utilizing the respective advantages of doctors and the intelligent algorithm, the content of a dynamic electrocardiogram report, the alarm mechanism of cardiac abnormalities and the analysis mechanism ofshort-term electrocardiogram anomaly recording are perfected. The intelligent big data analysis is carried out on a cloud platform server to establish the more intelligent remote electrocardiogram monitoring system.

Description

technical field [0001] The invention belongs to the technical field of medical and health management, and relates to a cloud-based wearable ECG monitoring system technology. Background technique [0002] With the rapid aging of my country's population, the improvement of people's living standards, the accelerated pace of life, and the increase in work pressure, heart disease has a clear trend of rejuvenation, and it has become one of the major diseases that are increasingly popular and seriously threaten human life and health. On the other hand, the combination of all-round and multi-field technologies such as the Internet of Things, big data, cloud storage, and artificial intelligence with medical health provides great development opportunities for remote monitoring of heart diseases. [0003] ECG signal is one of the biological signals studied earlier by human beings and applied in clinic. It has the characteristics of easy detection and intuitive regularity. ECG signal d...

AI Technical Summary

Problems solved by technology

In order to reduce the influence of wires and meet the requirements of comfort, the existing equipment uses dry electrodes instead of wet electrodes, but this will lead to defects such as poor quality of ECG acquisition signals and less lead data.

If wet electrodes are used, such as Holter, the wires will be exposed seriously, making it inconvenient for users to wear and affecting normal activities

[0006] (2) The ECG signal collected by the monitoring equipment has large noise interference and many types, and the noise can easily cover the ECG signal at the onset of the disease, resulting in poor signal integrity and accuracy

Generally, software is used to remove noise, but sometimes it is difficult to remove due to high overlap between noise and source signal due to equipment hardware

[0007] (3) Due to the long-term continuous monitoring of ECG signals and the huge amount of data, the current storage capacity and network speed of mobile phones often cannot meet the requirements of real-time storage and transmission

[0008] (4) The normal signals in the long-term continuous monitoring of ECG signals account for the majority. If you manually check each data frame, it will cause a lot of manual waste

With the continuous in-depth research in the field of automatic analysis and diagnosis of ECG, the accuracy of automatic ECG diagnosis is also continuously improving, but it still cannot reach a satisfactory level. The existing technology cannot completely replace manual diagnosis. There is a certain percentage of false detections and missed detections in all methods

[0009] (5) Users generally get a simple report after monitoring a problem, which cannot meet the user's deep-seated requirements

It is impossible to provide targeted professional advice for individual users, so that the late effect of remote ECG monitoring is not obvious

In addition, the existing monitoring products cannot make full use of the collected data, and the data mining is not sufficient

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