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A wearable ECG moni...
A wearable ECG monitoring device with flexible embedded denoising and compression
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- Wang, C. (författare)
- Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China.
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- Jin, H. (författare)
- Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China.
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- Qin, Y. (författare)
- Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China.;Shanghai Engn Res Ctr Assist Devices, Shanghai 200093, Peoples R China.
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- Zheng, Li-rong (författare)
- 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
- Engelska.
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Ingår i: European Solid-State Circuits Conference. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 87-90, s. 79-82, s. 87-90
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- 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)
Nyckelord
- 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
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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