Abstract
The acquisition of physiological data are essential to efficiently predict and treat cardiac patients before a heart attack occurs and effectively expedite motor recovery after a stroke. This goal can be achieved by using wearable wireless sensor network platforms for real-time healthcare monitoring. In this paper, we present a wireless physiological signal acquisition device and a smartphone-based software platform for real-time data processing and monitor and cloud server access for everyday ECG/EMG signal monitoring. The device is implemented in a compact size (diameter: 30 mm, thickness: 4.5 mm) where the biopotential is measured and wirelessly transmitted to a smartphone or a laptop for real-time monitoring, data recording and analysis. Adaptive digital filtering is applied to eliminate any interference noise that can occur during a regular at-home environment, while minimizing the data process time. The accuracy of ECG and EMG signal coverage is assessed using Bland–Altman analysis by comparing with a reference physiological signal acquisition instrument (RHS2116 Stim/Recording System, Intan). Signal coverage of R-R peak intervals showed almost identical outcome between this proposed work and the RHS2116, showing a mean difference in heart rate of 0.15 ± 4.65 bpm and a Wilcoxon’s p value of 0.133. A 24 h continuous recording session of ECG and EMG is conducted to demonstrate the robustness and stability of the device based on extended time wearability on a daily routine.
Highlights
The prevention of ischemic stroke and stroke recurrence is an important public health concern
The ubiquity of the internet and smartphones can support remote clinical participation, but home-based physiological signal acquisition devices are vulnerable to external noise in an everyday environment, especially the powerline interference (PLI) [28,29,30]
We present a set of data analysis based on the R-peak detection, followed by a calculation of heart-rate variability (HRV) and BPM
Summary
The prevention of ischemic stroke and stroke recurrence is an important public health concern. 25% of strokes in the United States are recurrent strokes and stroke causes approximately 1 in 20 deaths [1]. As electrocardiography is one of the most important physiological signals for cardiovascular health and the autonomic nervous system (ANS), cardiac monitoring has been proven to demonstrate relevance to stroke. Several ECG studies have been reported the quantitative ECG measurements in clinical applications to evaluate the relationship between cardiac, neurological, and functional outcomes of ischemic stroke [2,3]. After the strike of stroke, survivors often suffer from hemiplegia which highly affects their daily activities [4]. Hemiplegia generally reveals asymmetrical deficits in gait and is one of the most common disabilities observed in the post-stroke phase
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