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In this paper, we investigate how to incorporate intelligence into the human-centric IoT edges to detect arrhythmia, a heart condition often associated with morbidity and even mortality. We propose a classification algorithm based on the intrapatient convolutional neural network model and the interpatient attention residual network model to automatically identify the type of arrhythmia in the edges. As the imbalance categories in the MIT-BIH arrhythmia database which needs to be used in thedoi:10.1155/2021/5563338 fatcat:k7ltvr2q3fbstpt4qthdlxhbjq