The Role of Artificial Intelligence in Diagnosing Heart Disease in Humans: A Review
DOI:
https://doi.org/10.37385/jaets.v5i1.3413Keywords:
Electrocardiogram, Waves, Heartbeats, Diagnosing, Medical, SignalAbstract
The electrical activity of the heart and the electrocardiogram (ECG) signal are fundamentally related. In the study that has been published, the ECG signal has been examined and used for a number of applications. The monitoring of heart rate and the analysis of heart rhythm patterns, the detection and diagnosis of cardiac diseases, the identification of emotional states, and the use of biometric identification methods are a few examples of applications in the field. Several various phases may be involved in the analysis of electrocardiogram (ECG) data, depending on the type of study being done. Preprocessing, feature extraction, feature selection, feature modification, and classification are frequently included in these stages. Every stage must be finished in order for the analysis to go smoothly. Additionally, accurate success measures and the creation of an acceptable ECG signal database are prerequisites for the analysis of electrocardiogram (ECG) signals. Identification and diagnosis of various cardiac illnesses depend heavily on the ECG segmentation and feature extraction procedure. Electrocardiogram (ECG) signals are frequently obtained for a variety of purposes, including the diagnosis of cardiovascular conditions, the identification of arrhythmias, the provision of physiological feedback, the detection of sleep apnea, routine patient monitoring, the prediction of sudden cardiac arrest, and the creation of systems for identifying vital signs, emotional states, and physical activities. The ECG has been widely used for the diagnosis and prognosis of a variety of heart diseases. Currently, a range of cardiac diseases can be accurately identified by computerized automated reports, which can then generate an automated report. This academic paper aims to provide an overview of the most important problems associated with using deep learning and machine learning to diagnose diseases based on electrocardiography, as well as a review of research on these techniques and methods and a discussion of the major data sets used by researchers.
Downloads
References
Abd-Alzhra, A. S., & Al-Tamimi, M. S. (2022). Image Compression Using Deep Learning: Methods and Techniques. Iraqi Journal of Science, 1299-1312.? https://doi.org/10.24996/ijs.2022.63.3.34
Abed, R. M., Abdulmalek, H. W., Yaaqoob, L. A., Altaee, M. F., & Kamona, Z. K. (2023). Genetic Polymorphism of TLR5 and TLR6 in Iraqi Patients with Heart Failure Disease. Iraqi Journal of Science, 64(4), 1662–1674. https://doi.org/10.24996/ijs.2023.64.4.9
Abood, Q. K. (2023). Predicting Age and Gender Using AlexNet. TEM Journal, 12(1).
Al Jibory, F. K., Mohammed, O. A., & Al Tamimi, M. S. H. (2022). Age Estimation Utilizing Deep Learning Convolutional Neural Network. International Journal on Technical and Physical Problems of Engineering, 14(4), 219-24.
AL-Jibory, F. K., Akram Younis, M., & Al- Tamimi, M. S. H. (2022). Preparing of ECG dataset for biometric ID identification with creative techniques. TEM Journal, 1500–1507. https://doi.org/10.18421/tem114-10.
Al-Juboori, R. A. L. (2017). Contrast Enhancement of the Mammographic Image Using Retinex with CLAHE methods. Iraqi Journal of Science, 58(1B), 327–336.
Alqahtani, N., Alam, S., Aqeel, I., Shuaib, M., Mohsen Khormi, I., Khan, S. B., & Malibari, A. A. (2023). Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification. Applied Sciences, 13(13), 7833. https://doi.org/10.3390/app13137833
Al-Tamimi, M. S. H., & Sulong, G. (2014). A Review of Snake Models in Medical MR Image Segmentation. Jurnal Teknologi (Sciences and Engineering), 69(2), 101–106. https://doi.org/10.11113/jt.v69.3116.
Al-Tamimi, M. S. H., & Sulong, G. (2014). TUMOR BRAIN DETECTION THROUGH MR IMAGES: A REVIEW OF LITERATURE. Journal of Theoretical & Applied Information Technology, 62(2).
Al-Tamimi, M. S. H., Al-Tamimi, A. S. H., & Sulong, G. (2016). A new abnormality detection approach for T1-weighted magnetic resonance imaging brain slices using three planes. Adv. Comput, 6(1), 6-27. https://doi.org/10.5923/j.ac.20160601.02
Al-Tamimi, M. S., & AL-Khafaji, R. S. (2022). Finger vein recognition based on PCA and fusion convolutional neural network. International Journal of Nonlinear Analysis and Applications, 13(1), 3667-3681.? https://doi.org/10.22075/ijnaa.2022.6145
Arooj, S., Rehman, S. ur, Imran, A., Almuhaimeed, A., Alzahrani, A. K., & Alzahrani, A. (2022). A Deep Convolutional Neural Network for the Early Detection of Heart Disease. Biomedicines, 10(11), 1–15. https://doi.org/10.3390/biomedicines10112796
Bakar, W. A. W. A., Josdi, N. L. N. B., Man, M. B., & Zuhairi, M. A. B. (2023). A Review: Heart Disease Prediction in Machine Learning & Deep Learning. 2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), 150–155. https://doi.org/10.1109/CSPA57446.2023.10087837
Bassiouni, M. M., El-Dahshan, E. S. A., Khalefa, W., & Salem, A. M. (2018). Intelligent hybrid approaches for human ECG signals identification. Signal, Image and Video Processing, 12(5), 941–949. https://doi.org/10.1007/s11760-018-1237-5
Ciccarelli, M., Giallauria, F., Carrizzo, A., Visco, V., Silverio, A., Cesaro, A., Calabrò, P., De Luca, N., Mancusi, C., & Masarone, D. (2023). Artificial intelligence in cardiovascular prevention: new ways will open new doors. Journal of Cardiovascular Medicine, 24(Supplement 2), e106–e115. https://doi.org/10.2459/JCM.0000000000001431
Cluitmans, M., Brooks, D. H., MacLeod, R., Dössel, O., Guillem, M. S., Van Dam, P. M., Svehlikova, J., He, B., Sapp, J., Wang, L., & Bear, L. (2018). Validation and opportunities of electrocardiographic imaging: From technical achievements to clinical applications. Frontiers in Physiology, 9(SEP), 1–19. https://doi.org/10.3389/fphys.2018.01305
Dai, H., Hwang, H. G., & Tseng, V. S. (2021). Convolutional neural network based automatic screening tool for cardiovascular diseases using different intervals of ECG signals. Computer Methods and Programs in Biomedicine, 203, 106035. https://doi.org/10.1016/j.cmpb.2021.106035
Dong, X., Si, W., & Huang, W. (2018). ECG-based identity recognition via deterministic learning. Biotechnology and Biotechnological Equipment, 32(3), 769–777. https://doi.org/10.1080/13102818.2018.1428500
Ganguly, B., Ghosal, A., Das, A., Das, D., Chatterjee, D., & Rakshit, D. (2020). Automated Detection and Classification of Arrhythmia from ECG Signals Using Feature-Induced Long Short-Term Memory Network. IEEE Sensors Letters, 4(8), 5–8. https://doi.org/10.1109/LSENS.2020.3006756
Geng, Q., Liu, H., Gao, T., Liu, R., Chen, C., Zhu, Q., & Shu, M. (2023). An ECG Classification Method Based on Multi-Task Learning and CoT Attention Mechanism. Healthcare (Switzerland), 11(7), 1–13. https://doi.org/10.3390/healthcare11071000
Ghadi, N. M., & Salman, N. H. (2022). Deep learning-based segmentation and classification techniques for brain tumor MRI: A review. Journal of Engineering, 28(12), 93–112. https://doi.org/10.31026/j.eng.2022.12.07
Glennie, R., Adam, T., Leos?Barajas, V., Michelot, T., Photopoulou, T., & McClintock, B. T. (2023). Hidden Markov models: Pitfalls and opportunities in ecology. Methods in Ecology and Evolution, 14(1), 43–56. https://doi.org/10.31026/j.eng.2022.12.07
Groenewegen, A., Rutten, F. H., Mosterd, A., & Hoes, A. W. (2020). Epidemiology of heart failure. European Journal of Heart Failure, 22(8), 1342–1356.
Hammad, M., Liu, Y., & Wang, K. (2019). Multimodal biometric authentication systems using convolution neural network based on different level fusion of ECG and fingerprint. IEEE Access, 7, 25527–25542. https://doi.org/10.1109/ACCESS.2018.2886573
Hassan, Z., Gilani, S. O., & Jamil, M. (2016). Review of fiducial and non-fiducial techniques of feature extraction in ECG based biometric systems. Indian Journal of Science and Technology, 9(21), 1-5. https://doi.org/10.17485/ijst/2016/v9i21/94841
Hong, S., Zhou, Y., Shang, J., Xiao, C., & Sun, J. (2020). Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review. Computers in Biology and Medicine, 122, 103801. https://doi.org/10.1016/j.compbiomed.2020.103801
Ibrahim, A. E., Abdel-Mageid, S., Nada, N., & Elshahed, M. A. (2022). Human Identification Using Electrocardiogram Signal as a Biometric Trait. International Journal of System Dynamics Applications, 11(3), 1–17. https://doi.org/10.4018/ijsda.287113
Jambukia, S. H., Dabhi, V. K., & Prajapati, H. B. (2015). Classification of ECG signals using machine learning techniques: A survey. Conference Proceeding - 2015 International Conference on Advances in Computer Engineering and Applications, ICACEA 2015, March, 714–721. https://doi.org/10.1109/ICACEA.2015.7164783
Jembula, K. K., Srinivasulu, G., & Prasad, K. S. (2013). Design of Electrocardiogram (ECG or EKG) System on FPGA. International Journal of Engineering and Science, 3(2), 21–27.
Karthik, S., Santhosh, M., Kavitha, M. S., & Paul, A. C. (2022). Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals. Computer Systems Science and Engineering, 42(1), 183–199. https://doi.org/10.32604/CSSE.2022.021698
Khajavi, H., & Rastgoo, A. (2023). Predicting the carbon dioxide emission caused by road transport using a Random Forest (RF) model combined by Meta-Heuristic Algorithms. Sustainable Cities and Society, 93, 104503. https://doi.org/10.1016/j.scs.2023.104503
Khondowe, E., Zhu, X., Rusike, S., & Mutyambizi, M. N. One-Dimensional CNN Approach for ECG Cardiovascular Disease Classification. International Research Journal of Advanced Engineering and Science, 8(2), 154- 159.
Kim, P., & Kim, P. (2017). Convolutional neural network. MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence, 121–147.
Krishnan, S., & Athavale, Y. (2018). Trends in biomedical signal feature extraction. Biomedical Signal Processing and Control, 43, 41–63. https://doi.org/10.1016/j.bspc.2018.02.008
Lastre-Dominguez, C., Shmaliy, Y. S., Ibarra-Manzano, O., & Vazquez-Olguin, M. (2019). Denoising and features extraction of ecg signals in state space using unbiased fir smoothing. IEEE Access, 7, 152166–152178. https://doi.org/10.1109/ACCESS.2019.2948067
Le, K. H., Pham, H. H., Nguyen, T. B., Nguyen, T. A., Thanh, T. N., & Do, C. D. (2023). Lightx3ecg: A lightweight and explainable deep learning system for 3-lead electrocardiogram classification. Biomedical Signal Processing and Control, 85, 104963. https://doi.org/10.1016/j.bspc.2023.104963
Liu, F., Liu, C., Zhao, L., Zhang, X., Wu, X., Xu, X., Liu, Y., Ma, C., Wei, S., He, Z., Li, J., & Yin Kwee, E. N. (2018). An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection. Journal of Medical Imaging and Health Informatics, 8(7), 1368–1373. https://doi.org/10.1166/jmihi.2018.2442
Lynn, H. M., Pan, S. B., & Kim, P. (2019). A Deep Bidirectional GRU Network Model for Biometric Electrocardiogram Classification Based on Recurrent Neural Networks. IEEE Access, 7, 145395–145405. https://doi.org/10.1109/ACCESS.2019.2939947
Maršánová, L., Nemcova, A., Smisek, R., Smital, L., & Vitek, M. (2021). Brno University of Technology ECG Signal Database with Annotations of P Wave (BUT PDB). https://doi.org/10.21203/rs.3.rs-942006/v1
Meesad, P., Boonkrong, S., & Unger, H. (2016). Electrocardiogram Identification: Use a Simple Set of Features in QRS Complex to Identify Individuals. Advances in Intelligent Systems and Computing, 463(3), v–vi. https://doi.org/10.1007/978-3-319-40415-8
Merdjanovska, E., & Rashkovska, A. (2022). Comprehensive survey of computational ECG analysis: Databases, methods and applications. Expert Systems with Applications, 117206. https://doi.org/10.1016/j.eswa.2022.117206
Moody, G. B., & Mark, R. G. (2001). The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3), 45–50. https://doi.org/10.1109/51.932724
Myles, A. J., Feudale, R. N., Liu, Y., Woody, N. A., & Brown, S. D. (2004). An introduction to decision tree modeling. Journal of Chemometrics: A Journal of the Chemometrics Society, 18(6), 275–285. https://doi.org/10.1002/cem.873.
Naz, M., Shah, J. H., Khan, M. A., Sharif, M., Raza, M., & Damaševi?ius, R. (2021). From ECG signals to images: a transformation based approach for deep learning. PeerJ Computer Science, 7, e386. https://doi.org/10.7717/peerj-cs.386
Nguyen, Q. H., Nguyen, B. P., Nguyen, T. B., Do, T. T. T., Mbinta, J. F., & Simpson, C. R. (2021). Stacking segment-based CNN with SVM for recognition of atrial fibrillation from single-lead ECG recordings. Biomedical Signal Processing and Control, 68(January), 102672. https://doi.org/10.1016/j.bspc.2021.102672
Okrainec, K., Banerjee, D. K., & Eisenberg, M. J. (2004). Coronary artery disease in the developing world. American Heart Journal, 148(1), 7–15. https://doi.org/10.1016/j.ahj.2003.11.027
Palermi, S., Serio, A., Vecchiato, M., Sirico, F., Gambardella, F., Ricci, F., Iodice, F., Radmilovic, J., Russo, V., & D’Andrea, A. (2021). Potential role of an athlete-focused echocardiogram in sports eligibility. World Journal of Cardiology, 13(8), 271. https://doi.org/10.4330%2Fwjc.v13.i8.271
Physionet (2019). PTB. Https://Physionet.Org/Content/Ptb-Xl/1.0.1/.
Pirruccello, J. P., Bick, A., Wang, M., Chaffin, M., Friedman, S., Yao, J., Guo, X., Venkatesh, B. A., Taylor, K. D., & Post, W. S. (2020). Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy. Nature Communications, 11(1), 2254. https://doi.org/10.1038/s41467-020-15823-7
Porle, R. R., Ruslan, N. S., Ghani, N. M., Arif, N. A., Ismail, S. R., Parimon, N., & Mamat, M. (2015). A survey of filter design for audio noise reduction. J. Adv. Rev. Sci. Res, 12(1), 26–44.
Qiu, J., Zhu, J., Xu, M., Huang, P., Rosenberg, M., Weber, D., Liu, E., & Zhao, D. (2022). Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation. http://arxiv.org/abs/2202.00567
Rahma, M. M.., & Salman, A. D. (2022). Heart Disease Classification–Based on the Best Machine Learning Model. Iraqi Journal of Science, 63(9), 3966–3976. https://doi.org/10.24996/ijs.2022.63.9.28
Rath, A., Mishra, D., Panda, G., & Satapathy, S. C. (2021). Heart disease detection using deep learning methods from imbalanced ECG samples. Biomedical Signal Processing and Control, 68, 102820.
S., C. V., & Ramaraj, E. (2021). A Novel Deep Learning based Gated Recurrent Unit with Extreme Learning Machine for Electrocardiogram (ECG) Signal Recognition. Biomedical Signal Processing and Control, 68(May), 102779. https://doi.org/10.1016/j.bspc.2021.102779
Sadiq, A. T., & Mahmood, N. T. (2014). A hybrid estimation system for medical diagnosis using modified full Bayesian classifier and artificial bee colony. Iraqi Journal of Science, 55(3A), 1095-1107.
Sandau, K. E., Funk, M., Auerbach, A., Barsness, G. W., Blum, K., Cvach, M., Lampert, R., May, J. L., McDaniel, G. M., Perez, M. V., Sendelbach, S., Sommargren, C. E., & Wang, P. J. (2017). Update to Practice Standards for Electrocardiographic Monitoring in Hospital Settings: A Scientific Statement From the American Heart Association. In Circulation (Vol. 136, Issue 19). https://doi.org/10.1161/CIR.0000000000000527
Satija, U., Ramkumar, B., & Sabarimalai Manikandan, M. (2018). A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment. IEEE Reviews in Biomedical Engineering, 11(c), 36–52. https://doi.org/10.1109/RBME.2018.2810957
Sekaran, K., Chandana, P., Krishna, N. M., & Kadry, S. (2020). Deep learning convolutional neural network (CNN) With Gaussian mixture model for predicting pancreatic cancer. Multimedia Tools and Applications, 79(15), 10233–10247. https://doi.org/10.1007/s11042-019-7419-5
Shkara, A. A., & Hussain, Y. (2018). Heartbeat Amplification and ECG Drawing from Video (Black and White or Colored Videos). Iraqi Journal of Science, 408-419. https://doi.org/10.24996/ijs.2018.59.1B.21
Smith, S. W., Walsh, B., Grauer, K., Wang, K., Rapin, J., Li, J., Fennell, W., & Taboulet, P. (2019). A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation. Journal of Electrocardiology, 52, 88–95. https://doi.org/10.1016/j.jelectrocard.2018.11.013
Todorov, T., Bogdanova, G., Noev, N., & Sabev, N. (2019). Data management in a holter monitoring system. TEM Journal, 8(3), 801–805. https://dx.doi.org/10.18421/TEM83-15
Ueda, D., Matsumoto, T., Ehara, S., Yamamoto, A., Walston, S. L., Ito, A., Shimono, T., Shiba, M., Takeshita, T., & Fukuda, D. (2023). Artificial intelligence-based model to classify cardiac functions from chest radiographs: a multi-institutional, retrospective model development and validation study. Lancet Digital Health S2589-7500 (23) 00107–3. https://doi.org/10.1016/S2589-7500(23)00107-3
Vasconcellos, M. M. E., Ferreira, B. G., Leandro, J. S., Neto, B. F. S., Cordeiro, F. R., Cestari, I. A., Gutierrez, M. A., Sobrinho, Á., & Cordeiro, T. D. (2023). Siamese Convolutional Neural Network for Heartbeat Classification Using Limited 12-Lead ECG Datasets. IEEE Access, 11, 5365–5376. https://doi.org/10.1109/ACCESS.2023.3236189
Wang, M., Rahardja, S., Fränti, P., & Rahardja, S. (2023). Single-lead ECG recordings modeling for end-to-end recognition of atrial fibrillation with dual-path RNN. Biomedical Signal Processing and Control, 79, 104067. https://doi.org/10.1016/j.bspc.2022.104067
CITEDNESS IN SCOPUS
CITEDNESS IN WOS
