A Proof-of-Concept of Ultra-Edge Smart IoT Sensor: A Continuous and Lightweight Arrhythmia Monitoring Approach

IEEE Access

Nidal Nasser¹  Waleed Alasmary²  Sadman Sakib³  Zubair Md. Fadlullah³  Mostafa M. Fouda⁴ 

[1] College of Engineering, Alfaisal University, Riyadh, Saudi Arabia;Department of Computer Engineering, Umm Al-Qura University, Makkah, Saudi Arabia;Department of Computer Science, Lakehead University, Thunder Bay, ON, Canada;Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID, USA;

关键词: Internet of Things (IoT);    arrhythmia;    electrocardiogram (ECG);    deep learning (DL);    convolutional neural network (CNN);    smart health;   

DOI  :  10.1109/ACCESS.2021.3056509

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown