铅(地质)
计算机科学
人工智能
过程(计算)
提前期
深度学习
模式识别(心理学)
简单
机器学习
数据挖掘
工程类
操作系统
地貌学
地质学
哲学
认识论
运营管理
作者
Majid Sepahvand,Fardin Abdali-Mohammadi
标识
DOI:10.1016/j.ins.2022.01.030
摘要
Deep learning models developed through multi-lead electrocardiogram (ECG) signals are considered the leading methods for the automated detection of arrhythmia on computer systems. However, due to the amplitudes of input signals, these models generate too many parameters for practical use. Therefore, they are rarely used on devices with limited computational resources in the newly-emerged technology of the Internet of medical things (IoMT). Knowledge distillation was utilized in this paper to propose a method for bridging the gap between the arrhythmia classification model with multi-lead ECG signals and the arrhythmia classification model with single-lead ECG signals by minimizing the performance decline. The proposed method consists of a teacher model with advanced architecture and a student model with simple architecture. The teacher model was already developed through multi-lead ECG signals, whereas the student model was developed through single-lead signals under the supervision of the teacher. Despite its simplicity, the student model receives the dark knowledge of multi-lead ECG signals from the teacher by imitating the teacher’s behavior in the development process. According to the results, the student model was nearly 262.18 times more compressed than its teacher. Moreover, the student experienced approximately 0.81% of accuracy decline in Chapman ECG with 10646 patients.
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