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

Abstract

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).

A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

“…However, due to the sporadic nature of ECG signals, it is necessary to monitor patients continuously to have for accurate analysis of the heart problems [4]. Recently, advancements in Internet of Things (IoT) based medical sensors have grown progressively [5][6][7][8][9][10][11][12][13][14][15]; especially in heartbeat sensors that generate real-time delay-sensitive data that require immediate action for the results [16,17]. Generally, these sensors are integrated with limited constraint devices.…”

A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic Low-Cost Biomedical Sensor

“…The problem of data quality in ECG sensors has also been tackled in [8], where authors developed a single-chip based wearable ECG hardware system for monitoring patients and offer a high-quality ECG signal by applying three types of filters. Nevertheless, they did not focus on a heart disease classification approach.…”

Time-Varying IIR Notch Filter with Reduced Transient Response Based on the Bézier Curve Pole Radius Variability

“…Typical application areas include for example biomedical engineering and communications [1,2]. Removing powerline interferences from ECG, EEG, and EMG recordings [3], filtering of humming GSM mobile telephone noise [4], and estimation of power system frequency [5] are specific examples of using notch filters. The design procedure of notch filters is usually a difficult task, particularly when the signals on a very narrow frequency band must be suppressed and those in the immediate neighborhood should be maintained without distortions.…”