Classification of arrhythmia’s ECG signal using cascade transparent classifier

Issue title: Digital transformation through advances in artificial intelligence and machine learning

Guest editors: Hasmat Malik, Gopal Chaudhary and Smriti Srivastava

Article type: Research Article

Authors: Setiawan, Noor Akhmad^{a; *} | Nugroho, Hanung Adi^a | Persada, Anugerah Galang^a | Yuwono, Tito^b | Prasojo, Ipin^c | Rahmadi, Ridho^d | Wijaya, Adi^a

Affiliations: [a] Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia | [b] Department of Electrical Engineering, Universitas Islam Indonesia, Yogyakarta, Indonesia | [c] Department of Biomedical Engineering Technology, ITS PKU Muhammadiyah, Surakarta, Indonesia | [d] Department of Informatics, Universitas Islam Indonesia, Yogyakarta, Indonesia

Correspondence: [*] Corresponding author. Noor Akhmad Setiawan, Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia. E-mail: noorwewe@ugm.ac.id.

Abstract: Arrhythmia is an abnormality often encountered in patients with cardiac problems. The presence of arrhythmia can be detected by an electrocardiogram (ECG) test. Automatic observation based on machine learning has been developed for long time. Unfortunately, only few of them have capability of explaining the knowledge inside themselves. Thus, transparency is important to improve human understanding of knowledge. To achieve this goal, a method based on cascaded transparent classifier is proposed. Firstly, ECG signals were separated and every single signal was extracted using feature extraction method. Several of extracted feature’s attributes were selected, and the final step was classifying data using cascade classifier which consists of decision tree and the rule based classifier. Classification performance was evaluated with publicly available dataset, the MIT-BIH Physionet Dataset. The methods were tested using 10-fold cross validation. The average of both accuracy and number of rules generated was considered. The best result using rule-based classifier achieves the accuracy and the number of rules 92.40% and 40, respectively. And the best result using cascade classifier achieves the accuracy and the number of rules 92.84% and 80, respectively. As a conclusion, transparent classifier shows a competitive performance with reasonable accuracy compared with previous research and promising in addressing the need for interpretability model.

Keywords: Physionet, arrhythmia, cascade, transparent classifier

DOI: 10.3233/JIFS-189768

Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1015-1025, 2022