Data mining analysis of the influences of electrocardiogram P-wave morphology parameters on atrial fibrillation

Issue title: Selected papers from the 9th International Multi-Conference on Engineering and Technology Innovation 2019 (IMETI2019)

Guest editors: Wen-Hsiang Hsieh

Article type: Research Article

Authors: Ouyang, Chen-Sen^a | Chen, Yenming J.^b | Tsai, Jinn-Tsong^{c; d} | Chang, Yiu-Jen^d | Huang, Tian-Hsiang^e | Hwang, Kao-Shing^{d; f} | Ho, Yuan-Chih^{g; *} | Ho, Wen-Hsien^{d; h; *}

Affiliations: [a] Department of Information Engineering, I-Shou University, Kaohsiung, Taiwan | [b] Department of Logistics Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan | [c] Department of Computer Science, National Pingtung University, Pingtung, Taiwan | [d] Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan | [e] Center for Big Data Research, Kaohsiung Medical University, Kaohsiung, Taiwan | [f] Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan | [g] Division of Cardiology, Department of Internal Medicine, Yuan’s General Hospital, Kaohsiung, Taiwan | [h] Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan

Correspondence: [*] Corresponding author. Yuan-Chih Ho, Division of Cardiology, Department of Internal Medicine, Yuan’s General Hospital, Kaohsiung, Taiwan. E-mail: [email protected] and Wen-Hsien Ho, Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan. E-Mail: [email protected].

Abstract: Atrial fibrillation (AF) is a type of paroxysmal cardiac disease that presents no obvious symptoms during onset, and even the electrocardiograms (ECG) results of patients with AF appear normal under a premorbid status, rendering AF difficult to detect and diagnose. However, it can result in deterioration and increased risk of stroke if not detected and treated early. This study used the ECG database provided by the Physionet website (https://physionet.org), filtered data, and employed parameter-extraction methods to identify parameters that signify ECG features. A total of 31 parameters were obtained, consisting of P-wave morphology parameters and heart rate variability parameters, and the data were further examined by implementing a decision tree, of which the topmost node indicated a significant causal relationship. The experiment results verified that the P-wave morphology parameters significantly affected the ECG results of patients with AF.

Keywords: Atrial fibrillation, electrocardiogram (ECG), data mining, decision tree

DOI: 10.3233/JIFS-189612

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7901-7908, 2021