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Open AccessArticle

An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications

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Electrical Engineering Department, Sukkur IBA University, Sukkur 65200, Pakistan
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Decision Information Systems and Production LAB, University Lumiere Lyon2, Bron-69500, France
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School of Computing Science and Engineering, VIT University, Vellore 632014, Tamil Nadu, India
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Department of Physics, Shah Abdul Latif University, Khairpur Mirs 66111, Sindh, Pakistan
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Department of English, University of Sindh, Jamshoro 71000, Sindh, Pakistan
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Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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Authors to whom correspondence should be addressed.
Sensors 2018, 18(3), 923; https://doi.org/10.3390/s18030923
Received: 21 January 2018 / Revised: 24 February 2018 / Accepted: 26 February 2018 / Published: 20 March 2018
(This article belongs to the Special Issue Internet of Things and Ubiquitous Sensing)
Rapid progress and emerging trends in miniaturized medical devices have enabled the un-obtrusive monitoring of physiological signals and daily activities of everyone’s life in a prominent and pervasive manner. Due to the power-constrained nature of conventional wearable sensor devices during ubiquitous sensing (US), energy-efficiency has become one of the highly demanding and debatable issues in healthcare. This paper develops a single chip-based wearable wireless electrocardiogram (ECG) monitoring system by adopting analog front end (AFE) chip model ADS1292R from Texas Instruments. The developed chip collects real-time ECG data with two adopted channels for continuous monitoring of human heart activity. Then, these two channels and the AFE are built into a right leg drive right leg drive (RLD) driver circuit with lead-off detection and medical graded test signal. Human ECG data was collected at 60 beats per minute (BPM) to 120 BPM with 60 Hz noise and considered throughout the experimental set-up. Moreover, notch filter (cutoff frequency 60 Hz), high-pass filter (cutoff frequency 0.67 Hz), and low-pass filter (cutoff frequency 100 Hz) with cut-off frequencies of 60 Hz, 0.67 Hz, and 100 Hz, respectively, were designed with bilinear transformation for rectifying the power-line noise and artifacts while extracting real-time ECG signals. Finally, a transmission power control-based energy-efficient (ETPC) algorithm is proposed, implemented on the hardware and then compared with the several conventional TPC methods. Experimental results reveal that our developed chip collects real-time ECG data efficiently, and the proposed ETPC algorithm achieves higher energy savings of 35.5% with a slightly larger packet loss ratio (PLR) as compared to conventional TPC (e.g., constant TPC, Gao’s, and Xiao’s methods). View Full-Text
Keywords: electrocardiogram (ECG) signal processing; wearable platform; noise; energy-efficiency; Filters; healthcare; ETPC; ubiquitous; beats per minute (BPM) electrocardiogram (ECG) signal processing; wearable platform; noise; energy-efficiency; Filters; healthcare; ETPC; ubiquitous; beats per minute (BPM)
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Sodhro, A.H.; Sangaiah, A.K.; Sodhro, G.H.; Lohano, S.; Pirbhulal, S. An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications. Sensors 2018, 18, 923.

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