TinyML-Based Classification in an ECG Monitoring Embedded System  

作　　者：Eunchan Kim Jaehyuk Kim Juyoung Park Haneul Ko Yeunwoong Kyung 

机构地区：[1]Department of Intelligence and Information,Seoul National University,Seoul,08826,Korea [2]Nexon Korea Cop.,Seongnam,Kyungki,13487,Korea [3]Advanced CP Lab.Samsung Electronics,Suwon,Kyungki,16677,Korea [4]Department of Electronic Engineering,Kyung Hee University,Yongin,Kyungki,17104,Korea [5]Division of Information&Communication Engineering,Kongju National University,Cheonan,Chungcheongnam,31080,Korea

出　　处：《Computers, Materials & Continua》2023年第4期1751-1764,共14页计算机、材料和连续体（英文）

基　　金：supported by National Research Foundation (NRF)of Korea Grant funded by the Korean Government (MSIP) (No.2022R1F1A1063183).

摘　　要：Recently, the development of the Internet of Things (IoT) hasenabled continuous and personal electrocardiogram (ECG) monitoring. In theECG monitoring system, classification plays an important role because it canselect useful data (i.e., reduce the size of the dataset) and identify abnormaldata that can be used to detect the clinical diagnosis and guide furthertreatment. Since the classification requires computing capability, the ECGdata are usually delivered to the gateway or the server where the classificationis performed based on its computing resource. However, real-time ECG datatransmission continuously consumes battery and network resources, whichare expensive and limited. To mitigate this problem, this paper proposes atiny machine learning (TinyML)-based classification (i.e., TinyCES), wherethe ECG monitoring device performs the classification by itself based onthe machine-learning model, which can reduce the memory and the networkresource usages for the classification. To demonstrate the feasibility, afterwe configure the convolutional neural networks (CNN)-based model usingECG data from the Massachusetts Institute of Technology (MIT)-Beth IsraelHospital (BIH) arrhythmia and the Physikalisch Technische Bundesanstalt(PTB) diagnostic ECG databases, TinyCES is validated using the TinyMLsupportedArduino prototype. The performance results show that TinyCEScan have an approximately 97% detection ratio, which means that it has greatpotential to be a lightweight and resource-efficient ECG monitoring system.

关 键 词：HOLTER ECG ARDUINO internet of things(IoT) TinyML 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]