Abstract - Cardiac arrhythmias remain a leading contributor to global cardiovascular mortality, and continuous monitoring outside the clinic has become central to early detection. This review examines the convergence of edge computing and the Internet of Things (IoT) in wearable electrocardiogram (ECG) systems for arrhythmia monitoring, with particular attention to three-channel acquisition using commercial-grade analog front ends. It surveys system architectures spanning cloud, fog, and edge deployment; classification approaches ranging from handcrafted-feature machine learning to deep convolutional and recurrent networks; and TinyML techniques that bring inference onto microcontroller-class hardware. Wearable ECG acquisition hardware built around chips such as the AD8232, ADS1292R, and ADS1293 is reviewed alongside the communication protocols, Bluetooth Low Energy, Wi-Fi, and LoRa, used to move data between sensor, edge node, and cloud. A comparative table positions eight representative prior implementations against channel count, communication method, computation location, classification algorithm, and reported accuracy. A second analysis draws on the lead configurations recorded in the MIT-BIH, PTB-XL, Chapman-Shaoxing, and related public databases to justify Lead I, Lead II, and Lead V1 as the three-channel configuration best supported by prior hardware and by the largest annotated public datasets. The review closes by identifying gaps in dataset availability for true three-lead simultaneous recording, in energy-accuracy trade-offs for on-device inference, and in standardized benchmarking across embedded platforms.
Ravikiran B A, Chandana N E, Devika Nataraj et al.· Zenodo (CERN European Organi...· 0 citations
Abstract - Cardiac arrhythmias remain a leading contributor to global cardiovascular mortality, and continuous monitoring outside the clinic has become central to early detection. This review examines the convergence of edge computing and the Internet of Things (IoT) in wearable electrocardiogram (ECG) systems for arrhythmia monitoring, with particular attention to three-channel acquisition using commercial-grade analog front ends. It surveys system architectures spanning cloud, fog, and edge deployment; classification approaches ranging from handcrafted-feature machine learning to deep convolutional and recurrent networks; and TinyML techniques that bring inference onto microcontroller-class hardware. Wearable ECG acquisition hardware built around chips such as the AD8232, ADS1292R, and ADS1293 is reviewed alongside the communication protocols, Bluetooth Low Energy, Wi-Fi, and LoRa, used to move data between sensor, edge node, and cloud. A comparative table positions eight representative prior implementations against channel count, communication method, computation location, classification algorithm, and reported accuracy. A second analysis draws on the lead configurations recorded in the MIT-BIH, PTB-XL, Chapman-Shaoxing, and related public databases to justify Lead I, Lead II, and Lead V1 as the three-channel configuration best supported by prior hardware and by the largest annotated public datasets. The review closes by identifying gaps in dataset availability for true three-lead simultaneous recording, in energy-accuracy trade-offs for on-device inference, and in standardized benchmarking across embedded platforms.
Ravikiran B A, Chandana N E, Devika Nataraj et al.· Zenodo (CERN European Organi...· 0 citations
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