AccScience Publishing / AIH / Online First / 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 Shiri1*
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1 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.
References
  1. Aziz S, Ahmed S, Alouini MA. ECGbased machinelearning algorithms for heartbeat classification. Sci Rep. 2021;11:18738. doi: 10.1038/s41598-021-97118-5

 

  1. Khan F, Yu X, Yuan Z, Rehman A. ECG classification using 1-D convolutional deep residual neural network. PLOS One. 2023;18:e0284791. doi: 10.1371/journal.pone.0284791

 

  1. Weimann K, Conrad TO. Transfer learning for ECG classification. Sci Rep. 2021;11:5251. doi: 10.1038/s41598-021-84374-8

 

  1. Cheng J, Zou Q, Zhao Y. ECG signal classification based on deep CNN and BiLSTM. BMC Med Inform Decis Mak. 2021;21:365. doi: 10.1186/s12911-021-01736-y

 

  1. Sumathi S, Lilly Beaulah H, Vanithamani R. A wavelet transform based feature extraction and classification of cardiac disorder. J Med Syst. 2014;38:98. doi: 10.1007/s10916-014-0098-x

 

  1. Leite JP, Moreno RL. Heartbeat classification with low computational cost using Hjorth parameters. IET Signal Process. 2018;12(4):431-438. doi: 10.1049/iet-spr.2017.0296

 

  1. Varatharajan R, Manogaran G, Priyan MK. A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimedia Tools Applic. 2018;77:10195-10215. doi: 10.1007/s11042-017-5318-1

 

  1. Sharma P, Ray KC. Efficient methodology for electrocardiogram beat classification. IET Signal Process. 2016;10(7):825-832. doi: 10.1049/iet-spr.2015.0274

 

  1. Übeyli ED. Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents. Comput Methods Programs Biomed. 2009;93:313-321. doi: 10.1016/j.cmpb.2008.10.012

 

  1. Zhang L, Peng H, Yu C. An approach for ECG Classification Based on Wavelet Feature Extraction and Decision Tree. In: 2010 International Conference on Wireless Communications and Signal Processing. 2010. p. 1-4. doi: 10.1109/WCSP.2010.5633782

 

  1. Karpagachelvi S, Arthanari M, Sivakumar M. Classification of electrocardiogram signals with support vector machines and extreme learning machine. Neural Comput Applic. 2012;21:1331-1339. doi: 10.1007/s00521-011-0572-z

 

  1. El-Saadawy H, Tantawi M, Shedeed HA, Tolba MF. Hybrid hierarchical method for electrocardiogram heartbeat classification. IET Signal Process. 2018;12(4):506-513. doi: 10.1049/iet-spr.2017.0108

 

  1. Chikh MA, Ammar M, Marouf R. A neuro-fuzzy identification of ECG beats. J Med Syst. 2012;36:903-914. doi: 10.1007/s10916-010-9554-4

 

  1. Gupta A, Thomas B, Kumar P, Kumar S, Kumar Y. Neural Network based Indicative ECG Classification. In: 2014 5th International Conference - Confluence The Next Generation Information Technology Summit. 2014. p. 277-279. doi: 10.1109/CONFLUENCE.2014.6949262

 

  1. Banerjee S, Mitra M. Application of cross wavelet transform for ECG pattern analysis and classification. IEEE Trans Instrument Measure. 2014;63(2):326-333. doi: 10.1109/TIM.2013.2279001

 

  1. Bouaziz F, Oulhadj H, Boutana D, Siarry P. Automatic ECG arrhythmias classification scheme based on the conjoint use of the multi-layer perceptron neural network and a new improved metaheuristic approach. IET Signal Process. 2019;13(8):726-735. doi: 10.1049/iet-spr.2018.5465

 

  1. Sahoo S, Kanungo B, Behera S, Sabut S. Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities. Measurement. 2017;108:55-66. doi: 10.1016/j.measurement.2017.05.022

 

  1. Satija U, Ramkumar B, Manikandan MS. A new automated signal quality-aware ECG beat classification method for unsupervised ECG diagnosis environments. IEEE Sensors J. 2019;19(1):277-286. doi: 10.1109/JSEN.2018.2877055

 

  1. Jing J, Huaifeng Z, Dechang P, Chenglong D. A novel multi-module neural network system for imbalanced heartbeats classification. Expert Syst Applic X. 2019;1:100003. doi: 10.1016/j.eswax.2019.100003

 

  1. Tandale S, Barhatte AS, Ghongade R, Dale M. Arrhythmia Classification Using Neuro Fuzzy Approach. In: 2017 3rd International Conference on Advances in Computing, Communication and Automation. 2017. p. 1-4. doi: 10.1109/ICACCAF.2017.8344712

 

  1. Rivera J, Rodriguez K, Yu XH. Cardiovascular Conditions Classification Using Adaptive Neuro-Fuzzy Inference System. In: 2019 IEEE International Conference on Fuzzy Systems. 2019. p. 1-6. doi: 10.1109/FUZZ-IEEE.2019.8858896

 

  1. Sun Z, Wang C, Zhao Y, Yan C. Multi-label ECG signal classification based on ensemble classifier. IEEE Access. 2020;8:117986-117996. doi: 10.1109/ACCESS.2020.3004908

 

  1. Kour H, Manhas J, Sharma V. Usage and implementation of neurofuzzy systems for classification and prediction in the diagnosis of different types of medical disorders. Artif Intellig Rev. 2020;53:1-56. doi: 10.1007/s10462-020-09804-x

 

  1. Hanbay K. Deep neural network based approach for ECG classification using hybrid differential features and active learning. IET Signal Process. 2019;13(2):165-175. doi: 10.1049/iet-spr.2018.5103

 

  1. Xu X, Jeong S, Li J. Interpretation of electrocardiogram (ECG) rhythm by combined CNN and BiLSTM. IEEE Access. 2020;8:125380-125388. doi: 10.1109/ACCESS.2020.3006707

 

  1. Zhai X, Tin C. Automated ECG classification using dual heartbeat coupling based on convolutional neural network. IEEE Access. 2018;6:27465-27472. doi: 10.1109/ACCESS.2018.2833841.

 

  1. Pan J, Tompkins JW. A real-time QRS detection algorithm. IEEE Trans Biomed Eng. 1985;32(3):230-236. doi: 10.1109/TBME.1985.325532

 

  1. Kuhn M, Johnson K, editors. Classification models. In: Applied Predictive Modeling. New York: Springer; 2013. doi: 10.1007/978-1-4614-6849-3

 

  1. Balbinot A, Favieiro G. A Neuro-Fuzzy system for characterization of arm movements. Sensors. 2013;13(2):2613-2630. doi: 10.3390/s130202613

 

  1. Jang JS. Adaptive-network-based Fuzzy inference system. IEEE Trans Syst Man Cybernet. 1993;23(3):665-685. doi: 10.1109/21.256541Et omnim eveliquod que et, vollabo.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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