AccScience Publishing / AIH / Volume 1 / Issue 4 / DOI: 10.36922/aih.3367
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ORIGINAL RESEARCH ARTICLE

A multi-adaptive neuro-fuzzy inference system with variable thresholds for heartbeat classification

Roghayeh Rafieisangari1 ,  Nabiollah Shiri2*
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1 Department of Electrical Engineering, University of Notre Dame, United States of America
2 Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
AIH 2024, 1(4), 43–60; https://doi.org/10.36922/aih.3367
Submitted: 4 April 2024 | Accepted: 26 June 2024 | Published: 24 October 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Various heart disorders are non-invasively diagnosed using electrocardiograms (ECGs). An ECG records a variety of waveforms, including P, QRS, and T waves, which represent the electrical activity of the human heart. Cardiovascular diseases are diagnosed by examining the length, form, and spacing of these waveforms. This research develops a multi-adaptive, neuro-fuzzy inference system (MANFIS), enhanced by a variable threshold approach, to enhance heartbeat classification accuracy. The MIT-BIH arrhythmia database was utilized, and seven features were extracted from each record. A subtractive clustering method was employed to prepare the inputs for the MANFIS, enabling heartbeat classification. By applying a variable threshold to the MANFIS outputs, classification accuracy was further enhanced. The proposed method, termed variable-threshold MANFIS, can separately detect normal sinus rhythm, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature condition, and paced beat. This is achieved using six different ANFIS classifiers, each with its own threshold. The system was evaluated, achieving an accuracy of 98.33%, a sensitivity of 93.12%, a specificity of 99.66%, a precision of 98.33, and an F1-score of 95.44. A distinct feature of this machine-learning-based model is its controllable threshold, which delivers promising results across all training, testing, and validation datasets. The proposed diagnostic system is applicable in new automated medical instrumentation and serves as a valuable tool in cardiology.

Keywords
Electrocardiograms signals
Feature extraction
Classification
Adaptive neuro-fuzzy inference system
Subtractive clustering
Variable threshold
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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