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A Novel Approach for Arrhythmia Classification Using CI- 1D-LBP with LSTM, 1D-CNN and GRU Models

Year 2024, Volume: 13 Issue: 4, 1216 - 1232, 31.12.2024
https://doi.org/10.17798/bitlisfen.1542941

Abstract

Atrial arrhythmias (ARR) are known as the most encountered cardiac disorders in today's world that have direct or indirect detrimental effect on human health. Therefore, Computer-Assisted Diagnosis (CAD) systems are instrumental in the early detection and diagnosis of diseases, serving a pivotal role in the initial assessment and identification process. In this study, ECG data belonging to four different types of arrhythmias, namely ventricular beat (VB), supraventricular beat (SVB), fusion beat (FB), and an unidentified arrhythmic beat (UB), as well as ECG data showing normal sinus rhythm (NSR) of healthy individuals were classified. The ECG data were sourced from the MIT-BIH database. The Center-Independent 1-Dimensional Local Binary Pattern (CI-1D-LBP), originated from the local binary pattern (LBP) method, proposed as a new approach for deriving the essential features needed for the classification of ECG signals. With this new approach, histograms are generated from the signals, and these histogram data are used as input for classification in 1D-CNN, LSTM, and GRU deep learning methods. The CI-1D-LBP+GRU methodology exhibited superior efficacy in classifying the five-labeled dataset (VB-SVB-FB-UB-NSR) relative to the other applied methods, attaining an impressive accuracy rate of 98.59%.

Ethical Statement

The study is complied with research and publication ethics.

References

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  • M. J. Curtis et al., “The Lambeth Conventions (II): guidelines for the study of animal and human ventricular and supraventricular arrhythmias,” Pharmacol Ther, vol. 139, no. 2, pp. 213–248, 2013.
  • M. A. Arias, M. Pachón, and C. Martín‐Sierra, “A regular wide QRS complex tachycardia with fusion beats?,” J Arrhythm, vol. 36, no. 6, p. 1100, 2020.
  • S. Ayub and J. P. Saini, “ECG classification and abnormality detection using cascade forward neural network,” International Journal of Engineering, Science and Technology, vol. 3, no. 3, 2011.
  • A. Çalışkan, “A new ensemble approach for congestive heart failure and arrhythmia classification using shifted one-dimensional local binary patterns with long short-term memory,” Comput J, vol. 65, no. 9, pp. 2535–2546, 2022.
  • S. Sahoo, M. Dash, S. Behera, and S. Sabut, “Machine learning approach to detect cardiac arrhythmias in ECG signals: A survey,” Irbm, vol. 41, no. 4, pp. 185–194, 2020.
  • Y. Kaya, F. Kuncan, and R. Tekin, “A new approach for congestive heart failure and arrhythmia classification using angle transformation with LSTM,” Arab J Sci Eng, vol. 47, no. 8, pp. 10497–10513, 2022.
  • A. S. Eltrass, M. B. Tayel, and A. I. Ammar, “A new automated CNN deep learning approach for identification of ECG congestive heart failure and arrhythmia using constant-Q non-stationary Gabor transform,” Biomed Signal Process Control, vol. 65, p. 102326, 2021.
  • D. Thanapatay, C. Suwansaroj, and C. Thanawattano, “ECG beat classification method for ECG printout with Principle Components Analysis and Support Vector Machines,” in 2010 International Conference on Electronics and Information Engineering, IEEE, 2010, pp. V1-72.
  • S. Karpagachelvi, M. Arthanari, and M. Sivakumar, “Classification of electrocardiogram signals with support vector machines and extreme learning machine,” Neural Comput Appl, vol. 21, pp. 1331–1339, 2012.
  • M. Vijayavanan, V. Rathikarani, and P. Dhanalakshmi, “Automatic classification of ECG signal for heart disease diagnosis using morphological features,” International Journal of Computer Science & Engineering Technology, vol. 5, no. 4, pp. 449–455, 2014.
  • S. Hadiyoso and A. Rizal, “Electrocardiogram signal classification using higher-order complexity of hjorth descriptor,” Adv Sci Lett, vol. 23, no. 5, pp. 3972–3974, 2017.
  • J. A. Gutiérrez-Gnecchi et al., “DSP-based arrhythmia classification using wavelet transform and probabilistic neural network,” Biomed Signal Process Control, vol. 32, pp. 44–56, 2017.
  • Z. Wu et al., “A novel features learning method for ECG arrhythmias using deep belief networks,” in 2016 6th International conference on digital home (ICDH), IEEE, 2016, pp. 192–196.
  • H. M. Lynn, S. B. Pan, and P. Kim, “A deep bidirectional GRU network model for biometric electrocardiogram classification based on recurrent neural networks,” IEEE Access, vol. 7, pp. 145395–145405, 2019.
  • U. R. Acharya et al., “Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals,” Applied Intelligence, vol. 49, pp. 16–27, 2019.
  • S. Han, W. Lee, H. Eom, J. Kim, and C. Park, “Detection of arrhythmia using 1D convolution neural network with LSTM model,” IEIE Transactions on Smart Processing & Computing, vol. 9, no. 4, pp. 261–265, 2020.
  • A. Darmawahyuni, S. Nurmaini, M. Yuwandini, M. N. Rachmatullah, F. Firdaus, and B. Tutuko, “Congestive heart failure waveform classification based on short time-step analysis with recurrent network,” Inform Med Unlocked, vol. 21, p. 100441, 2020.
  • L. Zheng, Z. Wang, J. Liang, S. Luo, and S. Tian, “Effective compression and classification of ECG arrhythmia by singular value decomposition,” Biomedical Engineering Advances, vol. 2, p. 100013, 2021.
  • A. Çınar and S. A. Tuncer, “Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks,” Comput Methods Biomech Biomed Engin, vol. 24, no. 2, pp. 203–214, 2021.
  • S. C. Mohonta, M. A. Motin, and D. K. Kumar, “Electrocardiogram based arrhythmia classification using wavelet transform with deep learning model,” Sens Biosensing Res, vol. 37, p. 100502, 2022.
  • P. Madan, V. Singh, D. P. Singh, M. Diwakar, B. Pant, and A. Kishor, "A hybrid deep learning approach for ECG-based arrhythmia classification," Bioengineering, vol. 9, no. 4, p. 152, 2022.
  • E. B. Panganiban, A. C. Paglinawan, W. Y. Chung, and G. L. S. Paa, "ECG diagnostic support system (EDSS): A deep learning neural network based classification system for detecting ECG abnormal rhythms from a low-powered wearable biosensors," Sensing and Bio-Sensing Research, vol. 31, p. 100398, 2021.
  • M. Salem, S. Taheri, and J. Yuan, "ECG arrhythmia classification using transfer learning from 2-dimensional deep CNN features," in 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2018, pp. 1–4.
  • J. Rahul and L. D. Sharma, “Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and Bi-LSTM model,” Biocybern Biomed Eng, vol. 42, no. 1, pp. 312–324, 2022.
  • G. B. Moody and R. G. Mark, “The impact of the MIT-BIH arrhythmia database,” IEEE engineering in medicine and biology magazine, vol. 20, no. 3, pp. 45–50, 2001.
  • A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215–e220, 2000.
  • L. Eren, T. Ince, and S. Kiranyaz, “A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier,” J Signal Process Syst, vol. 91, pp. 179–189, 2019.
  • A. Vijayvargiya, R. Kumar, and N. Dey, “Voting-based 1D CNN model for human lower limb activity recognition using sEMG signal,” Phys Eng Sci Med, vol. 44, pp. 1297–1309, 2021.
  • T.-H. Hsieh and J.-F. Kiang, “Comparison of CNN algorithms on hyperspectral image classification in agricultural lands,” Sensors, vol. 20, no. 6, p. 1734, 2020.
  • S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput, vol. 9, no. 8, pp. 1735–1780, 1997.
  • X. Hu, S. Yuan, F. Xu, Y. Leng, K. Yuan, and Q. Yuan, “Scalp EEG classification using deep Bi-LSTM network for seizure detection,” Comput Biol Med, vol. 124, p. 103919, 2020.
  • F. Landi, L. Baraldi, M. Cornia, and R. Cucchiara, “Working memory connections for LSTM,” Neural Networks, vol. 144, pp. 334–341, 2021.
  • K. Khalil, O. Eldash, A. Kumar, and M. Bayoumi, “Economic LSTM approach for recurrent neural networks,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 66, no. 11, pp. 1885–1889, 2019.
  • K. Smagulova and A. P. James, “A survey on LSTM memristive neural network architectures and applications,” Eur Phys J Spec Top, vol. 228, no. 10, pp. 2313–2324, 2019.
  • K. Cho et al., “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014.
  • K. A. Althelaya, E.-S. M. El-Alfy, and S. Mohammed, “Stock market forecast using multivariate analysis with bidirectional and stacked (LSTM, GRU),” in 2018 21st Saudi Computer Society National Computer Conference (NCC), IEEE, 2018, pp. 1–7.
Year 2024, Volume: 13 Issue: 4, 1216 - 1232, 31.12.2024
https://doi.org/10.17798/bitlisfen.1542941

Abstract

References

  • H. E. Fürniss and B. Stiller, “Arrhythmic risk during pregnancy in patients with congenital heart disease.,” Herzschrittmacherther Elektrophysiol, vol. 32, no. 2, pp. 174–179, 2021.
  • F. A. Elhaj, N. Salim, A. R. Harris, T. T. Swee, and T. Ahmed, “Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals,” Comput Methods Programs Biomed, vol. 127, pp. 52–63, 2016.
  • H. V Huikuri, A. Castellanos, and R. J. Myerburg, “Sudden death due to cardiac arrhythmias,” New England Journal of Medicine, vol. 345, no. 20, pp. 1473–1482, 2001.
  • X. Xu, S. Jeong, and J. Li, “Interpretation of electrocardiogram (ECG) rhythm by combined CNN and BiLSTM,” Ieee Access, vol. 8, pp. 125380–125388, 2020.
  • F. Miao, B. Zhou, Z. Liu, B. Wen, Y. Li, and M. Tang, “Using noninvasive adjusted pulse transit time for tracking beat-to-beat systolic blood pressure during ventricular arrhythmia,” Hypertension Research, vol. 45, no. 3, pp. 424–435, 2022.
  • M. J. Curtis et al., “The Lambeth Conventions (II): guidelines for the study of animal and human ventricular and supraventricular arrhythmias,” Pharmacol Ther, vol. 139, no. 2, pp. 213–248, 2013.
  • M. A. Arias, M. Pachón, and C. Martín‐Sierra, “A regular wide QRS complex tachycardia with fusion beats?,” J Arrhythm, vol. 36, no. 6, p. 1100, 2020.
  • S. Ayub and J. P. Saini, “ECG classification and abnormality detection using cascade forward neural network,” International Journal of Engineering, Science and Technology, vol. 3, no. 3, 2011.
  • A. Çalışkan, “A new ensemble approach for congestive heart failure and arrhythmia classification using shifted one-dimensional local binary patterns with long short-term memory,” Comput J, vol. 65, no. 9, pp. 2535–2546, 2022.
  • S. Sahoo, M. Dash, S. Behera, and S. Sabut, “Machine learning approach to detect cardiac arrhythmias in ECG signals: A survey,” Irbm, vol. 41, no. 4, pp. 185–194, 2020.
  • Y. Kaya, F. Kuncan, and R. Tekin, “A new approach for congestive heart failure and arrhythmia classification using angle transformation with LSTM,” Arab J Sci Eng, vol. 47, no. 8, pp. 10497–10513, 2022.
  • A. S. Eltrass, M. B. Tayel, and A. I. Ammar, “A new automated CNN deep learning approach for identification of ECG congestive heart failure and arrhythmia using constant-Q non-stationary Gabor transform,” Biomed Signal Process Control, vol. 65, p. 102326, 2021.
  • D. Thanapatay, C. Suwansaroj, and C. Thanawattano, “ECG beat classification method for ECG printout with Principle Components Analysis and Support Vector Machines,” in 2010 International Conference on Electronics and Information Engineering, IEEE, 2010, pp. V1-72.
  • S. Karpagachelvi, M. Arthanari, and M. Sivakumar, “Classification of electrocardiogram signals with support vector machines and extreme learning machine,” Neural Comput Appl, vol. 21, pp. 1331–1339, 2012.
  • M. Vijayavanan, V. Rathikarani, and P. Dhanalakshmi, “Automatic classification of ECG signal for heart disease diagnosis using morphological features,” International Journal of Computer Science & Engineering Technology, vol. 5, no. 4, pp. 449–455, 2014.
  • S. Hadiyoso and A. Rizal, “Electrocardiogram signal classification using higher-order complexity of hjorth descriptor,” Adv Sci Lett, vol. 23, no. 5, pp. 3972–3974, 2017.
  • J. A. Gutiérrez-Gnecchi et al., “DSP-based arrhythmia classification using wavelet transform and probabilistic neural network,” Biomed Signal Process Control, vol. 32, pp. 44–56, 2017.
  • Z. Wu et al., “A novel features learning method for ECG arrhythmias using deep belief networks,” in 2016 6th International conference on digital home (ICDH), IEEE, 2016, pp. 192–196.
  • H. M. Lynn, S. B. Pan, and P. Kim, “A deep bidirectional GRU network model for biometric electrocardiogram classification based on recurrent neural networks,” IEEE Access, vol. 7, pp. 145395–145405, 2019.
  • U. R. Acharya et al., “Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals,” Applied Intelligence, vol. 49, pp. 16–27, 2019.
  • S. Han, W. Lee, H. Eom, J. Kim, and C. Park, “Detection of arrhythmia using 1D convolution neural network with LSTM model,” IEIE Transactions on Smart Processing & Computing, vol. 9, no. 4, pp. 261–265, 2020.
  • A. Darmawahyuni, S. Nurmaini, M. Yuwandini, M. N. Rachmatullah, F. Firdaus, and B. Tutuko, “Congestive heart failure waveform classification based on short time-step analysis with recurrent network,” Inform Med Unlocked, vol. 21, p. 100441, 2020.
  • L. Zheng, Z. Wang, J. Liang, S. Luo, and S. Tian, “Effective compression and classification of ECG arrhythmia by singular value decomposition,” Biomedical Engineering Advances, vol. 2, p. 100013, 2021.
  • A. Çınar and S. A. Tuncer, “Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks,” Comput Methods Biomech Biomed Engin, vol. 24, no. 2, pp. 203–214, 2021.
  • S. C. Mohonta, M. A. Motin, and D. K. Kumar, “Electrocardiogram based arrhythmia classification using wavelet transform with deep learning model,” Sens Biosensing Res, vol. 37, p. 100502, 2022.
  • P. Madan, V. Singh, D. P. Singh, M. Diwakar, B. Pant, and A. Kishor, "A hybrid deep learning approach for ECG-based arrhythmia classification," Bioengineering, vol. 9, no. 4, p. 152, 2022.
  • E. B. Panganiban, A. C. Paglinawan, W. Y. Chung, and G. L. S. Paa, "ECG diagnostic support system (EDSS): A deep learning neural network based classification system for detecting ECG abnormal rhythms from a low-powered wearable biosensors," Sensing and Bio-Sensing Research, vol. 31, p. 100398, 2021.
  • M. Salem, S. Taheri, and J. Yuan, "ECG arrhythmia classification using transfer learning from 2-dimensional deep CNN features," in 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2018, pp. 1–4.
  • J. Rahul and L. D. Sharma, “Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and Bi-LSTM model,” Biocybern Biomed Eng, vol. 42, no. 1, pp. 312–324, 2022.
  • G. B. Moody and R. G. Mark, “The impact of the MIT-BIH arrhythmia database,” IEEE engineering in medicine and biology magazine, vol. 20, no. 3, pp. 45–50, 2001.
  • A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215–e220, 2000.
  • L. Eren, T. Ince, and S. Kiranyaz, “A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier,” J Signal Process Syst, vol. 91, pp. 179–189, 2019.
  • A. Vijayvargiya, R. Kumar, and N. Dey, “Voting-based 1D CNN model for human lower limb activity recognition using sEMG signal,” Phys Eng Sci Med, vol. 44, pp. 1297–1309, 2021.
  • T.-H. Hsieh and J.-F. Kiang, “Comparison of CNN algorithms on hyperspectral image classification in agricultural lands,” Sensors, vol. 20, no. 6, p. 1734, 2020.
  • S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput, vol. 9, no. 8, pp. 1735–1780, 1997.
  • X. Hu, S. Yuan, F. Xu, Y. Leng, K. Yuan, and Q. Yuan, “Scalp EEG classification using deep Bi-LSTM network for seizure detection,” Comput Biol Med, vol. 124, p. 103919, 2020.
  • F. Landi, L. Baraldi, M. Cornia, and R. Cucchiara, “Working memory connections for LSTM,” Neural Networks, vol. 144, pp. 334–341, 2021.
  • K. Khalil, O. Eldash, A. Kumar, and M. Bayoumi, “Economic LSTM approach for recurrent neural networks,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 66, no. 11, pp. 1885–1889, 2019.
  • K. Smagulova and A. P. James, “A survey on LSTM memristive neural network architectures and applications,” Eur Phys J Spec Top, vol. 228, no. 10, pp. 2313–2324, 2019.
  • K. Cho et al., “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014.
  • K. A. Althelaya, E.-S. M. El-Alfy, and S. Mohammed, “Stock market forecast using multivariate analysis with bidirectional and stacked (LSTM, GRU),” in 2018 21st Saudi Computer Society National Computer Conference (NCC), IEEE, 2018, pp. 1–7.
There are 41 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence (Other), Biomechanical Engineering, Signal Processing
Journal Section Araştırma Makalesi
Authors

Hazret Tekin 0000-0002-9379-721X

Yılmaz Kaya 0000-0001-5167-1101

Early Pub Date December 30, 2024
Publication Date December 31, 2024
Submission Date September 3, 2024
Acceptance Date October 2, 2024
Published in Issue Year 2024 Volume: 13 Issue: 4

Cite

IEEE H. Tekin and Y. Kaya, “A Novel Approach for Arrhythmia Classification Using CI- 1D-LBP with LSTM, 1D-CNN and GRU Models”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 13, no. 4, pp. 1216–1232, 2024, doi: 10.17798/bitlisfen.1542941.

Bitlis Eren University
Journal of Science Editor
Bitlis Eren University Graduate Institute
Bes Minare Mah. Ahmet Eren Bulvari, Merkez Kampus, 13000 BITLIS