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An efficient algorithm for cardiac arrhythmia classification using ensemble of depthwise Separable convolutional neural networks

Ihsanto, Eko and Ramli, Kalamullah and Sudiana, Dodi and Gunawan, Teddy Surya (2020) An efficient algorithm for cardiac arrhythmia classification using ensemble of depthwise Separable convolutional neural networks. Applied Sciences, 10 (2). pp. 483-499. E-ISSN 2076-3417

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Abstract

Many algorithms have been developed for automated electrocardiogram (ECG) classification. Due to the non-stationary nature of the ECG signal, it is rather challenging to use traditional handcraft methods, such as time-based analysis of feature extraction and classification, to pave the way for machine learning implementation. This paper proposed a novel method, i.e., the ensemble of depthwise separable convolutional (DSC) neural networks for the classification of cardiac arrhythmia ECG beats. Using our proposed method, the four stages of ECG classification, i.e., QRS detection, preprocessing, feature extraction, and classification, were reduced to two steps only, i.e., QRS detection and classification. No preprocessing method was required while feature extraction was combined with classification. Moreover, to reduce the computational cost while maintaining its accuracy, several techniques were implemented, including All Convolutional Network (ACN), Batch Normalization (BN), and ensemble convolutional neural networks. The performance of the proposed ensemble CNNs were evaluated using the MIT-BIH arrythmia database. In the training phase, around 22% of the 110,057 beats data extracted from 48 records were utilized. Using only these 22% labeled training data, our proposed algorithm was able to classify the remaining 78% of the database into 16 classes. Furthermore, the sensitivity ( Sn ), specificity ( Sp ), and positive predictivity ( Pp ), and accuracy ( Acc ) are 99.03%, 99.94%, 99.03%, and 99.88%, respectively. The proposed algorithm required around 180 μs, which is suitable for real time application. These results showed that our proposed method outperformed other state of the art methods.

Item Type: Article (Journal)
Additional Information: 5588/78347
Uncontrolled Keywords: depthwise separable convolution (DSC); all convolutional network (ACN); batch normalization (BN); ensemble convolutional neural network (ECNN); electrocardiogram (ECG); MIT-BIH database
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Kulliyyah of Engineering
Depositing User: Dr Teddy Surya Gunawan
Date Deposited: 30 Jan 2020 12:51
Last Modified: 05 Feb 2020 08:46
URI: http://irep.iium.edu.my/id/eprint/78347

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