Integrated Approach for ECG Signal Classification Using Fused Slantlet Transform and Autoregressive Features

ALI Aalsaud; Raghad Z. Yousif; 3

doi:10.25163/angiotherapy.839548

Angiogenesis, Inflammation & Therapeutics | Online ISSN 2207-872X

REVIEWS (Open Access)

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Integrated Approach for ECG Signal Classification Using Fused Slantlet Transform and Autoregressive Features

ALI Aalsaud ^1*, Raghad Z. Yousif ^{2, 3}

+ Author Affiliations

Journal of Angiotherapy 8 (3) 1-12 https://doi.org/10.25163/angiotherapy.839548

Submitted: 10 January 2024 Revised: 01 March 2024 Published: 04 March 2024

Abstract

Sudden cardiac death remains a significant concern, emphasizing the critical need for intelligent diagnostic techniques in cardiac disease detection. Cardiologic Vascular Diseases (CVD) pose a significant global health challenge, often associated with unhealthy lifestyle habits. Electrocardiogram (ECG) monitoring plays a crucial role in assessing heart function, but its interpretation can be challenging due to signal complexity. This study proposes a novel approach utilizing fused Slantlet-Spatial features alongside Auto Regression (AR) coefficients to classify electrocardiogram (ECG) signals. Leveraging the Slantlet Transform (SLT) for its superior time localization capabilities and the simplicity of AR coefficients, this methodology aims to enhance the accuracy of cardiac illness classification. Nineteen distinct features were extracted from 300 ECG signals, each comprising 512 samples, enriching the classification scheme's efficacy. The fused feature extraction technique demonstrated success in improving overall accuracy. Employing Binary Particle Swarm Optimization (BPSO), irrelevant features were pruned, resulting in a refined feature vector of only 14 relevant features. This reduction in feature dimensionality significantly influenced the classification accuracy. Subsequently, the refined feature vector was introduced to the classification stage, employing four distinct classifiers: Support Vector Machine (SVM) with various kernel functions, K-Nearest Neighbors (K-NN), Naïve Bayes (NB), and Decision Tree (DT). Simulation results revealed the feasibility of achieving an average classification accuracy of 99.67% across ECG signal categories. Notably, the polynomial kernel function-SVM classifier, with only 2-fold cross-validation, yielded optimal performance. This study emphasizes the potential of fused Slantlet-Spatial and AR features in enhancing the accuracy of cardiac disease classification. Moreover, the efficacy of BPSO in feature selection and the superior performance of the SVM classifier highlight promising avenues for further research in intelligent cardiac diagnosis systems. The proposed approach outperforms existing methods and contributes to the early detection of cardiovascular abnormalities, potentially improving patient outcomes.

Keywords: Machine Learning; ECG; Slantlet Transform; Classification; Auto regression; Particle swarm optimization

References

Abdullah, J.M. and Ahmed, T. (2019) ‘Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process’, IEEE Access, 7, pp. 43473–43486. Available at: https://doi.org/10.1109/ACCESS.2019.2907012.

Acharya, U.R. et al. (2017) ‘Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network’, Information Sciences, 405, pp. 81–90. Available at: https://doi.org/10.1016/j.ins.2017.04.012.

Chashmi, A.J. and Amirani, M.C. (2019) ‘An efficient and automatic ECG arrhythmia diagnosis system using DWT and HOS features and entropy-based feature selection procedure’, Journal of Electrical Bioimpedance, 10(1), pp. 47–54. Available at: https://doi.org/10.2478/joeb-2019-0007.

Çinar, A. and Tuncer, S.A. (2020) ‘Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks’, Computer Methods in Biomechanics and Biomedical Engineering, 0(0), pp. 1–12. Available at: https://doi.org/10.1080/10255842.2020.1821192.

Daqrouq, K. and Dobaie, A. (2016) ‘Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions’, Computational and Mathematical Methods in Medicine, 2016. Available at: https://doi.org/10.1155/2016/7359516.

De Chazal, P., O’Dwyer, M. and Reilly, R.B. (2004) ‘Automatic classification of heartbeats using ECG morphology and heartbeat interval features’, IEEE Transactions on Biomedical Engineering, 51(7), pp. 1196–1206. Available at: https://doi.org/10.1109/TBME.2004.827359.

Ge, D.F., Hou, B.P. and Xiang, X.J. (2007) ‘Study of feature extraction based on autoregressive modeling in ECG automatic diagnosis’, Zidonghua Xuebao/Acta Automatica Sinica, 33(5), pp. 462–466. Available at: https://doi.org/10.1360/aas-007-0462.

Haji, S.O. and Yousif, R.Z. (2019) ‘A novel run-length based wavelet features for screening thyroid nodule malignancy’, Brazilian Archives of Biology and Technology, 62, pp. 1–17. Available at: https://doi.org/10.1590/1678-4324-2019170821.

Hatamikia, S., Maghooli, K. and Nasrabadi, A.M. (2014) ‘The Emotion Recognition System Based on Autoregressive Model and Sequential Forward Feature Selection of Electroencephalogram Signals’, Journal of Medical Signals and Sensors, 4(3), pp. 194–201. Available at: https://doi.org/10.4103/2228-7477.137777.

Hu, T. et al. (2021) ‘Real-time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm’, Biomedical Signal Processing and Control, 68(January), p. 102764. Available at: https://doi.org/10.1016/j.bspc.2021.102764.

Hussain, L. et al. (2020) ‘Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques’, BioMed Research International, 2020. Available at: https://doi.org/10.1155/2020/4281243.

Jasim, A.M., Abd, H.M. and Abdul-Jabbar, J.M. (2020) ‘Complexity reduction of slantlet transform structure based on the multiplierless realization’, Journal of Engineering Science and Technology, 15(3), pp. 1705–1718.

Kothari, V. et al. (2012) ‘A survey on particle swarm optimization in feature selection’, in Communications in Computer and Information Science. Springer, Berlin, Heidelberg, pp. 192–201. Available at: https://doi.org/10.1007/978-3-642-29216-3_22.

Kumari, C.U. et al. (2021) ‘An automated detection of heart arrhythmias using machine learning technique: SVM’, Materials Today: Proceedings, 45(October), pp. 1393–1398. Available at: https://doi.org/10.1016/j.matpr.2020.07.088.

Li, T. and Zhou, M. (2016) ‘ECG classification usingwavelet packet entropy and random forests’, Entropy, 18(8), pp. 1–16. Available at: https://doi.org/10.3390/e18080285.

Liao, Y., Xiang, Y. and Du, D. (2020) ‘Automatic Classification of Heartbeats Using ECG Signals via Higher Order Hidden Markov Model’, IEEE International Conference on Automation Science and Engineering, 2020-Augus, pp. 69–74. Available at: https://doi.org/10.1109/CASE48305.2020.9216956.

Maitra, M. and Chatterjee, A. (2008) ‘Hybrid multiresolution Slantlet transform and fuzzy c-means clustering approach for normal-pathological brain MR image segregation’, Medical Engineering & Physics, 30(5), pp. 615–623. Available at: https://doi.org/https://doi.org/10.1016/j.medengphy.2007.06.009.

Maitra, M., Chatterjee, A. and Matsuno, F. (2008) ‘A novel scheme for feature extraction and classification of magnetic resonance brain images based on slantlet transform and support vector machine’, in Proceedings of the SICE Annual Conference, pp. 1130–1134. Available at: https://doi.org/10.1109/SICE.2008.4654828.

Mijwil, M.M. (2021) ‘Implementation of Machine Learning Techniques for the Classification of Lung X-Ray Images Used to Detect COVID-19 in Humans’, Iraqi Journal of Science, 62(6), pp. 2099–2109. Available at: https://doi.org/10.24996/ijs.2021.62.6.35.

Mohammed, B.N.S. and Yousif, R.Z. (2019) ‘Intelligent System for Screening Diabetic Retinopathy by Using Neutrosophic and Statistical Fundus Image Features.’, ZANCO JOURNAL OF PURE AND APPLIED SCIENCES, 31(6), pp. 30–39. Available at: https://doi.org/10.21271/zjpas.31.6.4.

Nahak, S. and Saha, G. (2020) ‘A fusion based classification of normal, arrhythmia and congestive heart failure in ECG’, 26th National Conference on Communications, NCC 2020, pp. 1–6. Available at: https://doi.org/10.1109/NCC48643.2020.9056095.

Olanrewaju, R.F. et al. (2021) ‘Classification of ECG signals for detection of arrhythmia and congestive heart failure based on continuous wavelet transform and deep neural networks’, Indonesian Journal of Electrical Engineering and Computer Science, 22(3), pp. 1520–1528. Available at: https://doi.org/10.11591/ijeecs.v22.i3.pp1520-1528.

Ratnaparkhi, A., Deshpande, P. and ... (2021) ‘A Framework for Segmentation and Classification of Arrhythmia using Novel Bidirectional LSTM Network’, International Journal of …, 1(1). Available at: http://journal.uob.edu.bh/handle/123456789/4221.

Reethika, A. (2020) ‘Learning Technique’, (Icoei), pp. 580–584.

Saeed, S.S. and Yousif, R.Z. (2021) ‘A Slantlet based Statistical Features Extraction for Classification of Normal, Arrhythmia, and Congestive Heart Failure in Electrocardiogram’, UHD Journal of Science and Technology, 5(1), pp. 71–81. Available at: https://doi.org/10.21928/uhdjst.v5n1y2021.pp71-81.

Sahoo, S.K. et al. (2015) ‘Feature extraction of ECG signal based on wavelet transform for arrhythmia detection’, International Conference on Electrical, Electronics, Signals, Communication and Optimization, EESCO 2015 [Preprint], (December 2018). Available at: https://doi.org/10.1109/EESCO.2015.7253954.

Sandeep, K. et al. (2019) ‘ECG Classification using Machine Learning’, 8, pp. 2277–3878. Available at: https://doi.org/10.35940/ijrte.D6989.118419.

Sangaiah, A.K., Arumugam, M. and Bian, G. Bin (2020) ‘An intelligent learning approach for improving ECG signal classification and arrhythmia analysis’, Artificial Intelligence in Medicine, 103(December 2019), p. 101788. Available at: https://doi.org/10.1016/j.artmed.2019.101788.

Sharma, A. et al. (2020) ‘Automated pre-screening of arrhythmia using hybrid combination of Fourier–Bessel expansion and LSTM’, Computers in Biology and Medicine, 120(January). Available at: https://doi.org/10.1016/j.compbiomed.2020.103753.

Thomas, M., Das, M.K. and Ari, S. (2015) ‘Automatic ECG arrhythmia classification using dual tree complex wavelet based features’, AEU - International Journal of Electronics and Communications, 69(4), pp. 715–721. Available at: https://doi.org/10.1016/j.aeue.2014.12.013.

Too, J., Abdullah, A.R. and Saad, N.M. (2019) ‘A new co-evolution binary particle swarm optimization with multiple inertia weight strategy for feature selection’, Informatics, 6(2). Available at: https://doi.org/10.3390/informatics6020021.

Tuncer, T. et al. (2019) ‘Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals’, Knowledge-Based Systems, 186(October 2020), p. 104923. Available at: https://doi.org/10.1016/j.knosys.2019.104923.

Wady, S.H., Yousif, R.Z. and Hasan, H.R. (2020) ‘A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction’, BioMed Research International, 2020. Available at: https://doi.org/10.1155/2020/8125392.

Wu, C. et al. (2021) ‘Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images’, Soft Computing, 6. Available at: https://doi.org/10.1007/s00500-021-05839-6.

Wu, M. et al. (2021) ‘A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network’, Frontiers in Computational Neuroscience, 14(January), pp. 1–10. Available at: https://doi.org/10.3389/fncom.2020.564015.

Yancy, C.W. et al. (2013) ‘2013 ACCF/AHA guideline for the management of heart failure: A report of the american college of cardiology foundation/american heart association task force on practice guidelines’, Circulation, 128(16). Available at: https://doi.org/10.1161/CIR.0b013e31829e8776.

You, A., Be, M.A.Y. and In, I. (2022) ‘Low computation heartbeat classification based on ECG using artificial neural networks’, 040018(January).

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