f Deep Learning based DWT- Bi-LSTM Classifier for Enhanced Cardiovascular Arrhythmia Classification
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Research Article |

Deep Learning based DWT- Bi-LSTM Classifier for Enhanced Cardiovascular Arrhythmia Classification

Author(s): Pinjala N Malleswari*, CRS Hanuman, Venkata Ramana Kammampati, Samanthapudi Swathi and B. Elisha Raju

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 12, Issue 3

Publisher : FOREX Publication

Published : 15 August 2024

e-ISSN : 2347-470X

Page(s) : 934-939

DOI: https://doi.org/10.37391/IJEER.120325

Abstract

Nowadays heart diseases and their diagnosis have emerged as a prominent subject in health care systems, given that the heart performs a crucial role in the human body. Several computational techniques have been explored for the recognition and classification of cardiac diseases using Electrocardiogram (ECG) signals. Deep Learning (DL) is a present focus in healthcare solicitations, particularly in the classification of heartbeats in ECG signals. Many studies have utilized dissimilar DL models, including RNN (Recurrent Neural Networks), GRU (Gated Recurrent Unit), and CNN (Convolutional Neural Networks), to classify heartbeats using the MIT-BIH arrhythmia dataset. This article presents a methodical exploration of Bi-LSTM (Bi directional Long Short-Term Memory) based DL models for heartbeat classification using various quality metrics. Proposed variants include the Bi-LSTM model, demonstrating remarkable accuracy in classifying the heartbeats into five classes: Normal (N) beat, Supraventricular (S) beat, Ventricular contraction (V), Fusion beats (F), and Unclassifiable Beat (Q). The proposed technique outperforms present classifiers with Accuracy, Sensitivity, Specificity, and F1 score values of 98%, 96.9%, 97.4%, and 97.5% respectively. The simulations are conducted using MATLAB 2020a.

Keywords: Electrocardiogram, Discrete Wavelet Transform, Deep Learning, Gated Recurrent Unit, Bi-LSTM.




Pinjala N Malleswari*, Department of Electronics and Communication Technology, Sasi Institute of Technology & Engineering, Tadepalligudem, India; Email: pinjalamalleswari@gmail.com

CRS Hanuman, Department of Electronics and Communication Engineering, Sasi Institute of Technology & Engineering, Tadepalligudem, India; Email: crshanuman@sasi.ac.in

Venkata Ramana Kammampati, Department of Electronics and Communication Engineering, Aditya College of Engineering and Technology, Surampalem, India; Email: venkat.ramana489@gmail.com

Samanthapudi Swathi, Department of Electronics and Communication Engineering, Sagi Rama Krishnam Raju Engineering College, Andhra Pradesh, India; Email: sswathiece99@gmail.com

B. Elisha Raju, Department of Electronics and Communication Engineering, Vishnu Institute of Technology, Andhra Pradesh, India; Email: dr.pavithrag.8984@gmail.com

    [1] Rahman, A.; Asif, R.N.; Sultan, K.; Abbas, S. A.; Khan, M. A.; Mosavi, A. ECG classification for detecting ECG arrhythmia empowered with deep learning approaches. Comput. Int. and neuroscience 2022.
    [2] Elgendi, M.; Menon, C. Machine learning ranks ECG as an optimal wearable biosignal for assessing driving stress. IEEE Access, 2020; Volume 8.
    [3] Acharya, U. R.; Fujita,H.; Oh, S.L.; Hagiwara, Y.; Tan, J.H.; Adam, M. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf. Sci, 2017; Volume 415-416, pp. 190–198.
    [4] Rahuja.; Valluru, S. K. A comparative analysis of deep neural network models using transfer learning for electrocardiogram signal classification. International Conference on RTEICT IEEE, 2021; pp. 285–290.
    [5] Borui, H.; Jianyong, Y.; Wang, P.; Yan, R. LSTM-based auto-encoder model for ECG arrhythmias classification. IEEE Trans on Instr and Meas, 2020; Volume 69, issue 4.
    [6] Hiriyannaiah, S.; Siddesh, G. M.; Srinivasa, K.G. A comparative study and analysis of LSTM deep neural networks for heartbeats classification. Health. Technol, 2021; Volume 11, pp. 663-671.
    [7] Huang, B.; Chen. B.; Yao.; He, W. ECG arrhythmia classification using STFT-based spectrogram and CNN. IEEE Acc. Volume 7, pp. 92871–92880.
    [8] Rizwan, A., Priyanga, P., Abualsauod, E. H., Zafrullah, S. N., Serbaya, S. H., & Halifa, A. (2022). A machine learning approach for the detection of QRS complexes in electrocardiogram (ECG) using discrete wavelet transform (DWT) algorithm. Computational Intelligence and Neuroscience, 2022(1), 9023478.
    [9] Rahul, J.; Sharma, L. D.; Bohat, V. K. Short duration vector cardiogram based inferior myocardial infarction detection: Class and subject-oriented approach. Biomed. Engg/Biomedizi. Tech. 2021; Volume 66 pp. 489–501.
    [10] Sahoo, S., Mohanty, M., & Sabut, S. (2020). Automated ECG beat classification using DWT and Hilbert transform-based PCA-SVM classifier. International Journal of Biomedical Engineering and Technology, 32(3), 287-303.
    [11] Atal, D.K.; Singh, M. Arrhythmia classification with ECG signals based on the optimization-enabled deep convolutional neural network. Comput. Methods. Programs Biomed. 2020; Volume 196, Issue 105607.
    [12] Faust, O.; Acharya, U. R. Automated classification of five arrhythmias and normal sinus rhythm based on RR interval signals. Expert Syst. Appl. 2021; Volume 181, issue 115031.
    [13] Mathunjwa, B. M.; Lin, Y. T.; Lin, C. H., Abbod, M. F.; Shieh; J. S. ECG arrhythmia classification by using a recurrence plot and convolutional neural network. Biomed. Signal Process. Control. 2021; Volume 64, issue 102262.
    [14] Acharya, U.R.; Fujita, H.; Lih, O.S.; Hagiwara, Y.; Tan, J.H.; Adam, M. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf. Sci. 2017; Volume 405, pp. 81–90.
    [15] Malleswari, P. N., Bindu, C. H., & Prasad, K. S. (2021). Cardiac severity classification using pre trained neural networks. Interdisciplinary Sciences: Computational Life Sciences, 1-8.
    [16] Malleswari, P. N.; Bindu, CH.; Prasad, K.S. An improved denoising of electrocardiogram signals based on wavelet thresholding. Jour of Biom. Biomate and Biomed. Eng. 2021; Volume 51, pp.117-129.
    [17] Murthy, S. V. S. N.; Murali, K. P.P, Adversarial transformer network for classification of lung cancer disease from CT scan images. Biomedical Signal Processing and Control. 2023; Volume 86, issue 105327.
    [18] http://www.physionet.org/physiobank/database/mitdb: The MIT-BIH Arrhythmia Database.

Pinjala N Malleswari, CRS Hanuman, Venkata Ramana Kammampati, Samanthapudi Swathi, and B. Elisha Raju (2024), Deep Learning based DWT- Bi-LSTM Classifier for Enhanced Cardiovascular Arrhythmia Classification. IJEER 12(3), 934-939. DOI: 10.37391/IJEER.120325.