Energy-Efficient Intelligent ECG Monitoring for Wearable Devices

Wearable intelligent ECG monitoring devices can perform automatic ECG diagnosis in real time and send out alert signal together with abnormal ECG signal for doctor's further analysis. This provides a means for the patient to identify their heart problem as early as possible and go to doctors fo...

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Bibliographic Details
Published in:IEEE transactions on biomedical circuits and systems 2019-10, Vol.13 (5), p.1112-1121
Main Authors: Wang, Ning, Zhou, Jun, Dai, Guanghai, Huang, Jiahui, Xie, Yuxiang
Format: Article
Language:English
Subjects:
Accuracy
Adult
Aged
Aged, 80 and over
Arrhythmias, Cardiac - physiopathology
Biomedical monitoring
Compression
Diagnosis
ECG
Echocardiography
EKG
Electrocardiography
Electrocardiography - instrumentation
Electrocardiography - methods
End-to-End
Energy efficiency
Energy management
Energy-Efficient
Feature extraction
Female
Heart beat
Humans
Intelligent Diagnosis
Male
Medical treatment
Middle Aged
Monitoring
Neural networks
Neural Networks, Computer
Physicians
Power consumption
Power demand
Power management
Signal Processing, Computer-Assisted
Wearable
Wearable Electronic Devices
Wearable technology
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Summary:Wearable intelligent ECG monitoring devices can perform automatic ECG diagnosis in real time and send out alert signal together with abnormal ECG signal for doctor's further analysis. This provides a means for the patient to identify their heart problem as early as possible and go to doctors for medical treatment. For such system the key requirements include high accuracy and low power consumption. However, the existing wearable intelligent ECG monitoring schemes suffer from high power consumption in both ECG diagnosis and transmission in order to achieve high accuracy. In this work, we have proposed an energy-efficient wearable intelligent ECG monitor scheme with two-stage end-to-end neural network and diagnosis-based adaptive compression. Compared to the state-of-the-art schemes, it significantly reduces the power consumption in ECG diagnosis and transmission while maintaining high accuracy.
ISSN:1932-4545
1940-9990
DOI:10.1109/TBCAS.2019.2930215