Journal
IEEE ACCESS
Volume 9, Issue -, Pages 26093-26106Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3056509
Keywords
Electrocardiography; Monitoring; Sensors; Cloud computing; Intelligent sensors; Heart beat; Heart; Internet of Things (IoT); arrhythmia; electrocardiogram (ECG); deep learning (DL); convolutional neural network (CNN); smart health; smart sensor
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Funding
- Deanship of Scientific Research at Umm Al-Qura University [19-COM-1-01-0009]
- National Priorities Research Program (NPRP) from the Qatar National Research Fund (a member of Qatar Foundation) [NPRP13S-0205-200270]
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This study proposes a Deep Learning-based Lightweight Arrhythmia Classification method to enhance the intelligence of ultra-edge IoT sensors. The results demonstrate that the proposed method not only improves efficiency and generalization ability but also outperforms traditional approaches.
Due to the proliferation of the Internet of Things (IoT), the IoT devices are becoming utilized at the edge network at a much higher rate. Conventionally, the IoT devices lack the computation resources required for carrying out ultra-edge analytics. In this paper, we go beyond the typical edge analytics paradigm, which is mostly limited to user-smartphones, and investigate how to embed intelligence into the ultra-edge IoT sensors. To conceptualize the smart IoT sensors with enhanced intelligence, we select the arrhythmia detection task employing Electrocardiogram (ECG) trace as one of the mobile health (mHealth) cases. The existing approaches are not feasible for ultra-edge IoT sensors due to the extensive noise-filtering and manual feature extraction phase. Hence, in this paper, to facilitate the analytics, we propose a Deep Learning-based Lightweight Arrhythmia Classification (DL-LAC) method, which employs only single-lead ECG trace and does not require noise-filtering and manual feature extraction steps. As the proposed technique, we design a one-dimensional Convolutional Neural Network (CNN) architecture. Complying with the ANSI/AAMI EC57:1998 standard, four heartbeat types are taken into consideration as class labels. The efficiency and the generalization ability of the proposed model are evaluated, employing four different datasets from PhysioNet. The experimental results demonstrate that the proposed DL method outperforms traditional methods such as the Delay Differential Equation (DDE)-based optimization, K-Nearest Neighbor (KNN), and Random Forest (RF). The proposed DL-LAC illustrates encouraging performance in terms of time and memory requirement when the trained model is transferred to virtualized microcontrollers connected to IoT sensors.
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