For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
ECG Classification with Multi-Scale Deep Features Based on Adaptive Beat-Segmentation
Huan SUN Yuchun GUO Yishuai CHEN Bin CHEN
IEICE TRANSACTIONS on Communications
Publication Date: 2020/12/01
Online ISSN: 1745-1345
Type of Manuscript: Special Section PAPER (Special Section on IoT Sensor Networks and Mobile Intelligence)
ECG classification, adaptive beat segmentation, multi-scale deep features, channel attention module,
Full Text: PDF>>
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.