Heartbeat Pattern and Arrhythmia Classification: A Review
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Abstract
In today’s era, the lifestyle of people has become much more sophisticated due to the involvement of stress, anxiety, and depression in the daily routine of human beings. In such a scenario, cardiac diseases are growing rapidly in youngsters and senior citizens. It is also observed that cardiac diseases are crucial and sensitive, including life-threatening chances. So, it is essential to detect and prevent such cardiac disorders within the required time for recovery. Since there has been a lot of research in the prediction and prevention of cardiac disorders, cardiac arrhythmia is also one of the majorly occurring diseases in the bulk of the population. The electrocardiogram is the cheap and best way to diagnose the problem of cardiac arrhythmia, and a huge amount of data is collected daily in hospitals and pathological centers. Previously, various automated models were developed for detecting cardiac arrhythmia using deep learning approaches and machine learning. In this work, we have reviewed recently developed automated models and evaluated their performance based on specific parameters like deployed datasets, variation of input data, applied application, methodology, and results obtained by the developed model. The limitations of reviewed papers are also mentioned in addition to their future scope for improvement.
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2- Işin, A., Direkoğlu, C., Şah, M., “Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods”. Procedia Computer Science 102, pp. 317-324, (2016).
3- Işin, A. et al., “Monte Carlo simulation of PET/MR scanner and assessment of motion correction strategies”, JINST 12, C03089, (2017).
4- Jun, T. J. et al., “Premature ventricular contraction beat detection with deep neural networks”. 15th IEEE International Conference on Machine Learning and Applications, pp. 859-864, (2016).
5- W.S. Kim, S.H. Jin, Y.K. Park, H.M. Choi, “A study on development of multi-parametric measure of heart rate variability diagnosing cardiovascular disease”, World Congress on Medical Physics and Biomedical Engineering 2006, Volume 14 of the series IFMBE Proceedings pp. 3480– 3483, (2006).
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7- M. Karimi, R. Amirfattahi, S. Sadri, S.A. Marvasti, “Noninvasive detection and classification of coronary artery occlusions using wavelet analysis of heart sounds with neural networks”. In The 3rd IEE International Seminar on Medical Applications of Signal Processing, pp. 117–120, (2005).
8- Pan, J., Tompkins, W. J., “A Real Time QRS Detection Algorithm”. IEEE Transactions on Biomedical Engineering, 32, pp. 230-236, (1985).
9- Pourbabaee, B., Roshtkhari, M. J., Khorasani, K., “Feature Learning with Deep Convolutional Neural Networks for Screening Patients with Paraoxysmal Atrial Fibrillation”. IEEE International Joint Conference on Neural Networks, pp. 5057-5064, (2016).
10- Tajbakhsh, N. et al., “Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?”. IEEE Trans. Med. Imaging, 35, pp. 1299-1312, (2016).
11- S. Tsantis, G. C. Kagadis, K. Katsanos, D. Karnabatidis, G. Bourantas, and G. C. Nikiforidis, “Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography,” Medical physics, vol. 39, no. 1, pp. 503–513, (2011).
12- L. S. Athanasiou, C. V. Bourantas, G. A. Rigas, T. P. Exarchos, A. I. Sakellarios, P. K. Siogkas, M. I. Papafaklis, K. K. Naka, L. K. Michalis, F. Prati, et al., “Fully automated calcium detection using optical coherence tomography,” in Engineering in Medicine and Biology Society (EMBC), pp. 1430–1433, IEEE, (2013).
13- Huang, H., Liu, J., Zhu, Q., Wang, R., Hu, G., “A new hierarchical method for inter-patient heartbeat classification using random projections and rr intervals”. Biomedical engineering online 13, 90, (2014).
14- De Chazal, P., Reilly, R.B., “A patient-adapting heartbeat classifier using ecg morphology and heartbeat interval features”. IEEE transactions on biomedical engineering 53, pp. 2535–2543, (2006).
15- Park, K., Cho, B., Lee, D., Song, S., Lee, J., Chee, Y., Kim, I., Kim, S., “Hierarchical support vector machine-based heartbeat classification using higher order statistics and hermite basis function”, in: 2008 Computers in Cardiology, IEEE. pp. 229–232, (2008).
16- S Yazdani, S Fallet, J. Vesin, “A novel short-term event extraction algorithm for biomedical signals”, IEEE Transactions on Biomedical Engineering, vol. 65, no. 4, pp. 754-762, (April 2018).
17- J. Huang, B. Chen, B. Yao and W. He, “ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network”, IEEE Access, vol. 7, pp. 92871-92880, (2019).
18- P. Wang, B. Hou, S. Shao and R. Yan, “ECG Arrhythmias Detection Using Auxiliary Classifier Generative Adversarial Network and Residual Network”, IEEE Access, vol. 7, pp. 100910-100922, (2019).
19- R. He et al., “Automatic Cardiac Arrhythmia Classification Using Combination of Deep Residual Network and Bidirectional LSTM”, IEEE Access, vol. 7, pp. 102119-102135, (2019).
20- w. zhu, X. Chen, Y. Wang and L. Wang, “Arrhythmia Recognition and Classification Using ECG Morphology and Segment Feature Analysis”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 16, no. 1, pp. 131-138, (2019).
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24- S. Savalia, V. Emamian, “Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks” Bioengineering MDPI journal, 5 (2), p. 35, (2018).
25- A. Davari Dolatabadi, S. E. Z. Khadem, and B. M. Asl, “Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM”, Computer Methods and Programs in Biomedicine, vol. 138, pp. 117–126, (2017).
26- Arabasadi, Zeinab, et al., “Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm”. Computer methods and programs in biomedicine 141 (2017), pp. 19-26, (2017).
27- Ketu, Shwet, and Pramod Kumar Mishra. “Empirical analysis of machine learning algorithms on imbalance electrocardiogram-based arrhythmia dataset for heart disease detection”. Arabian Journal for Science and Engineering 47.2, pp. 1447-1469, (2022).
28- Saikumar, Kayam, Vullanki Rajesh, and Bulepe Sankara Babu. “Heart Disease Detection Based on Feature Fusion Technique with Augmented Classification Using Deep Learning Technology”. Traitement du Signal 39.1, (2022).
29- Chitra, R., et al., “Performance Evaluation of Machine Learning Algorithms in Design and Development of Heart Disease Detection”. 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS). IEEE, (2022).
30- Saleh, Basma, et al., “Analysis of weka data mining techniques for heart disease prediction system”. International journal of medical reviews 7.1, pp. 15-24, (2020).
31- Khan, Younas, et al., “Machine learning techniques for heart disease datasets: a survey”. Proceedings of the 2019 11th International Conference on Machine Learning and Computing. (2019).
32- Benhar, Houda, Ali Idri, and J. L. Fernández-Alemán. “Data preprocessing for heart disease classification: A systematic literature review”. Computer Methods and Programs in Biomedicine, 195 (2020), pp.105635, (2020).
33- Yahaya, Lamido, N. David Oye, and Etemi Joshua Garba. “A comprehensive review on heart disease prediction using data mining and machine learning techniques”. American Journal of Artificial Intelligence, 4.1, pp. 20-29, (2020).
| Files | ||
| Issue | Vol 11 No 1 (2024) | |
| Section | Literature (Narrative) Review(s) | |
| DOI | https://doi.org/10.18502/fbt.v11i1.14520 | |
| Keywords | ||
| Electrocardiogram Arrhythmia Classification Disease Detection Heartbeat Classification | ||
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How to Cite
1.
Dwivedi S, Mohammad A. Heartbeat Pattern and Arrhythmia Classification: A Review. Frontiers Biomed Technol. 2023;11(1):130-148.
