A wearable ECG monitoring device with flexible embedded denoising and compression

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A wearable ECG monitoring device with flexible embedded denoising and compression

Wang, C. (author): Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China.

Jin, H. (author): Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China.

Qin, Y. (author): Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China.;Shanghai Engn Res Ctr Assist Devices, Shanghai 200093, Peoples R China.

Zheng, Li-rong (author): KTH,Integrerade komponenter och kretsar,Industriell och Medicinsk Elektronik,Fudan University, China,Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China.

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Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China.;Shanghai Engn Res Ctr Assist Devices, Shanghai 200093, Peoples R China. (creator_code:org_t)

Institute of Electrical and Electronics Engineers (IEEE), 2016
2016
English.
In: European Solid-State Circuits Conference. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 87-90, s. 79-82, s. 87-90

Related links:: https://urn.kb.se/re...; show more...; https://doi.org/10.1...; https://urn.kb.se/re...; https://urn.kb.se/re...; show less...

Conference paper (peer-reviewed)

Abstract Subject headings

A wearable electrocardiogram (ECG) monitoring device with a customized SoC is reported. The SoC amplifies the ECG signal from passive electrodes and then digitizes and transforms it into wavelet coefficients. A low-power microcontroller (MCU) and a radio frequency (RF) module in the device resolve and send the wavelet coefficients to a mobile platform. The mobile platform uses machine learning algorithms to improve the performance of signal denoising and data compression by exploiting the characteristics of the sensed data, and consequently reduces power consumption in the wearable device. Measurement results show that the device can resolve ECG data from MIT-BIH arrhythmia database and actual ECG signals from human testers. After processing the ECG data with various noise models, the proposed device can improve the signal to noise ratio (SNR) and mean square error (MSE) by 23.8dB and 88.9%, respectively. When resolving actual ECG signals from testers, the typical compression ratio (CR) is 4.8:1 with 1.56% percentage root mean square difference (PRD). The SoC is fabricated in TSMC 0.18μm technology, and consumes 45μw for different applications.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kirurgi (hsv//swe)
MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Surgery (hsv//eng)
TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Keyword

Artificial intelligence
Data compression
Data handling
Electrocardiography
Learning algorithms
Learning systems
Mean square error
Mobile phones
Reconfigurable hardware
Signal to noise ratio
System-on-chip
Wavelet transforms
Wearable computers
Wearable technology
Low-power microcontrollers
Mobile platform
Monitoring device
Percentage root-mean-square differences
Radio frequencies
Wavelet coefficients
Wearable devices
Wearable ECG
Biomedical signal processing

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ref (subject category)
kon (subject category)

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By the author/editor: Wang, C.; Jin, H.; Qin, Y.; Zheng, Li-rong

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MEDICAL AND HEALTH SCIENCES: MEDICAL AND HEAL ...; and Clinical Medicin ...; and Surgery

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Signal Processin ...

Articles in the publication: European Solid-S ...; ESSCIRC CONFEREN ...

By the university: Royal Institute of Technology

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