Title: Detection of supraventricular tachycardia using decision tree model
Authors: Monalisa Mohanty; Asit Subudhi; Mihir Narayan Mohanty
Addresses: Department of Electronics and Communication Engineering, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Electronics and Communication Engineering, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Electronics and Communication Engineering, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
Abstract: Supra Ventricular Tachycardia (SVT) refers to an abnormally fast heartbeat that arises because of the improper electrical activity in the upper chamber of the heart. In this paper, authors have attempted to detect the SVT of human subjects. The ECG recordings have been collected from the MIT-BIH supraventricular arrhythmia database (SVDB) of the Physionet repository. Using Gain Ratio Attribute Evaluation method features are extracted. The evaluated features are then ranked according to their weightage value using the Ranker Search algorithm. The set of features are extracted for ST, N and VF. Machine learning-based classifiers such as Multi-Layer Perceptron (MLP) and Logistic Model Tree (LMT) are utilised to classify the ECG signals from the feature set. It is found that the proposed LMT model outperforms the MLP model and provides 99.23% accuracy. Also, the performance measures are done with sensitivity, specificity and precision as exhibited in the result section.
Keywords: tachycardia; SVT; feature extraction; MLP; LMT.
DOI: 10.1504/IJCAT.2021.117285
International Journal of Computer Applications in Technology, 2021 Vol.65 No.4, pp.378 - 388
Received: 01 Aug 2020
Accepted: 24 Sep 2020
Published online: 31 Aug 2021 *