An IoMT-based cardiovascular healthcare system with a wearable ECG patch achieved an on-patch compression ratio of 12.07 and a cloud-based disease prediction accuracy of 97%.
A novel IoMT-based wearable ECG patch system demonstrates high signal compression efficiency and accurate cloud-based disease prediction, offering a low-power solution for cardiovascular monitoring.
Nowadays, cardiovascular disease is still one of the primary diseases that limit life expectation of humans. To address this challenge, this work reports an Internet of Medical Things (IoMT)-based cardiovascular healthcare system with cross-layer optimization from sensing patch to cloud platform. A wearable ECG patch with a custom System-on-Chip (SoC) features a miniaturized footprint, low power consumption, and embedded signal processing capability. The patch also integrates wireless connectivity with mobile devices and cloud platform for optimizing the complete system. On the big picture, a "wearable patch-mobile-cloud" hybrid computing framework is proposed with cross-layer optimization for performance-power trade-off in embedded-computing. The measurement results demonstrate that the on-patch compression ratio of the raw ECG signal can reach 12.07 yielding a percentage root mean square variation of 2.29%. In the test with the MIT-BIH database, the average improvement of signal to noise ratio and mean square error are 12.63 dB and 94.47%, respectively. The average accuracy of disease prediction operation executed in cloud platform is 97%.
Qin et al. (Fri,) conducted a other in Cardiovascular disease. IoMT-based cardiovascular healthcare system with wearable ECG patch was evaluated on On-patch compression ratio and disease prediction accuracy. An IoMT-based cardiovascular healthcare system with a wearable ECG patch achieved an on-patch compression ratio of 12.07 and a cloud-based disease prediction accuracy of 97%.
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