IEICE Transactions on Communications
Online ISSN : 1745-1345
Print ISSN : 0916-8516
Special Section on IoT Sensor Networks and Mobile Intelligence
ECG Classification with Multi-Scale Deep Features Based on Adaptive Beat-Segmentation
Huan SUNYuchun GUOYishuai CHENBin CHEN
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2020 Volume E103.B Issue 12 Pages 1403-1410

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Abstract

Recently, the ECG-based diagnosis system based on wearable devices has attracted more and more attention of researchers. Existing studies have achieved high classification accuracy by using deep neural networks (DNNs), but there are still some problems, such as: imprecise heart beat segmentation, inadequate use of medical knowledge, the same treatment of features with different importance. To address these problems, this paper: 1) proposes an adaptive segmenting-reshaping method to acquire abundant useful samples; 2) builds a set of hand-crafted features and deep features on the inner-beat, beat and inter-beat scale by integrating enough medical knowledge. 3) introduced a modified channel attention module (CAM) to augment the significant channels in deep features. Following the Association for Advancement of Medical Instrumentation (AAMI) recommendation, we classified the dataset into four classes and validated our algorithm on the MIT-BIH database. Experiments show that the accuracy of our model reaches 96.94%, a 3.71% increase over that of a state-of-the-art alternative.

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© 2020 The Institute of Electronics, Information and Communication Engineers
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