Comparison of adaptive noise cancelers for ECG signals in wireless biotelemetry system

Khaliq, Aleem; Waseem, Athar; Munir, M. Fahad; Ahmad, Rabiah

doi:10.1109/intelse.2016.7475117

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Moreover, a lot of unobtrusive and continuous ECG measuring methods with wearable devices have been proposed in the field of Body Sensor Networks (BSNs) in recent years [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. In association with them, many kinds of signal processing methods and a framework based on wearable sensors and energy-efficient communication algorithms are proposed in the field [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors

Mitsukura

Sumali

Nagura

et al. 2020

Sensors

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Ballistocardiogram (BCG) is a graphical representation of the subtle oscillations in body movements caused by cardiovascular activity. Although BCGs cause less burden to the user, electrocardiograms (ECGs) are still commonly used in the clinical scene due to BCG sensors’ noise sensitivity. In this paper, a robust method for sleep time BCG measurement and a mathematical model for predicting sleep stages using BCG are described. The novel BCG measurement algorithm can be described in three steps: preprocessing, creation of heartbeat signal template, and template matching for heart rate variability detection. The effectiveness of this algorithm was validated with 99 datasets from 36 subjects, with photoplethysmography (PPG) to compute ground truth heart rate variability (HRV). On average, 86.9% of the inter-beat intervals were detected and the mean error was 8.5ms. This shows that our method successfully extracted beat-to-beat intervals from BCG during sleep, making its usability comparable to those of clinical ECGs. Consequently, compared to other conventional BCG systems, even more accurate sleep heart rate monitoring with a smaller burden to the patient is available. Moreover, the accuracy of the sleep stages mathematical model, validated with 100 datasets from 25 subjects, is 80%, which is higher than conventional five-stage sleep classification algorithms (max: 69%). Although, in this paper, we applied the mathematical model to heart rate interval features from BCG, theoretically, this sleep stage prediction algorithm can also be applied to ECG-extracted heart rate intervals.

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Section: Introductionmentioning

confidence: 99%

Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors

Mitsukura

Sumali

Nagura

et al. 2020

Sensors

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“…The Gabor Kuwahara filter was proposed, which decreases the noise without harming the edges of information [ 46 ]. Authors in References [ 47 , 48 , 49 , 50 , 51 , 52 ] present ECG filtering methods, but they do not focus on the energy-efficient communication in healthcare. Joao Caldeira et al [ 53 ] propose a novel method for the medical monitoring of patients, but they do not focus on wearable device development or energy-efficient solutions.…”

Section: Related Workmentioning

confidence: 99%

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

Sodhro

Sangaiah

Sodhro

et al. 2018

Sensors

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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).

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5G-Based Transmission Power Control Mechanism in Fog Computing for Internet of Things Devices

et al. 2018

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Fog computing has become the revolutionary paradigm and one of the intelligent services of the 5th Generation (5G) emerging network, while Internet of Things (IoT) lies under its main umbrella. Enhancing and optimizing the quality of service (QoS) in Fog computing networks is one of the critical challenges of the present. In the meantime, strong links between the Fog, IoT devices and the supporting back-end servers is done through large scale cloud data centers and with the linear exponential trend of IoT devices and voluminous generated data. Fog computing is one of the vital and potential solutions for IoT in close connection with things and end users with less latency but due to high computational complexity, less storage capacity and more power drain in the cloud it is inappropriate choice. So, to remedy this issue, we propose transmission power control (TPC) based QoS optimization algorithm named (QoS-TPC) in the Fog computing. Besides, we propose the Fog-IoT-TPC-QoS architecture and establish the connection between TPC and Fog computing by considering static and dynamic conditions of wireless channel. Experimental results examine that proposed QoS-TPC optimizes the QoS in terms of maximum throughput, less delay, less jitter and minimum energy drain as compared to the conventional that is, ATPC, SKims and constant TPC methods.

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Comparison of adaptive noise cancelers for ECG signals in wireless biotelemetry system

Cited by 3 publications

References 28 publications

Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors

Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors

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

5G-Based Transmission Power Control Mechanism in Fog Computing for Internet of Things Devices

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