Intelligent Diagnosis System of ECG Signal Based on Deep Learning
Abstract
This study introduces an intelligent diagnosis method based on an improved Transformer, which introduces a multi-scale attention mechanism into the fine feature extraction of the ECG signal, further optimizes the classification model, enhances the loss function, and improves the diagnosis accuracy. This project intends to use the MIT-BIH arrhythmia database as the research object. It divides it into training set, validation set, and test set according to 7:2:1. Experiments show that the accuracy of arrhythmia classification of the method proposed in this paper reaches 98.6%, the recall rate is 98.2%, and the F1 value is 98.4%. Compared with the traditional model, its accuracy is improved by 3.2%, 2.8%, and 3.0%, respectively. Compared with other mainstream deep learning algorithms such as ResNet and Dense Net, the performance indicators of this algorithm have been greatly improved. The research results of this project will provide an efficient and accurate solution for the intelligent diagnosis of ECG signals. It has important scientific significance and practical value.
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Goud, P. S., Sastry, P. N., & Sekhar, P. C. (2024). A novel intelligent deep optimized framework for heart disease prediction and classification using ECG signals. Multimedia Tools and Applications, 83(12), 34715–34731. https://doi.org/10.1007/s11042-023-16850-4
Saini, S. K., & Gupta, R. (2022). Artificial intelligence methods for analysis of electrocardiogram signals for cardiac abnormalities: State-of-the-art and future challenges. Artificial Intelligence Review, 55(2), 1519–1565. https://doi.org/10.1007/s10462-021-09999-7
Refaee, E. A., & Shamsudheen, S. (2022). A computing system that integrates deep learning and the internet of things for effective disease diagnosis in smart health care systems. Journal of Supercomputing, 78(7), 9285–9306. https://doi.org/10.1007/s11227-021-04263-9
Abubaker, M. B., & Babayiğit, B. (2022). Detection of cardiovascular diseases in ECG images using machine learning and deep learning methods. IEEE Transactions on Artificial Intelligence, 4(2), 373–382. https://doi.org/10.1109/TAI.2022.3159505
Zhuang, J., Sun, J., & Yuan, G. (2023). Arrhythmia diagnosis of young martial arts athletes based on deep learning for smart medical care. Neural Computing and Applications, 35(20), 14641–14652. https://doi.org/10.1007/s00521-021-06159-4
Liu, J., et al. (2022). A review of arrhythmia detection based on electrocardiogram with artificial intelligence. Expert Review of Medical Devices, 19(7), 549–560. https://doi.org/10.1080/17434440.2022.2115887
Pandey, S. K., & Janghel, R. R. (2021). Classification of electrocardiogram signal using an ensemble of deep learning models. Data Technologies and Applications, 55(3), 446–460. https://doi.org/10.1108/DTA-05-2020-0108
Sevcan Emek, Şebnem Bora, Vedat Evren, İbrahim Çakirlar, Behavioural Representation of the Aorta by Utilizing Windkessel and Agent-Based Modelling, Informatica 32(2021), no. 3, 499-516, https://doi.org/10.15388/21-INFOR456
Dugonik, J., Bošković, B., Brest, J., & Sepesy Maučec, M. (2019). Improving Statistical Machine Translation Quality Using Differential Evolution. Informatica, 30(4), 629-645. https://doi.org/10.15388/Informatica.2019.222
Karasan, A., Zavadskas, E. K., Kahraman, C., & Keshavarz-Ghorabaee, M. (2019). Residential Construction Site Selection Through Interval-Valued Hesitant Fuzzy CODAS Method. Informatica, 30(4), 689-710. https://doi.org/10.15388/Informatica.2019.225
Khafaga, D. S., et al. (2023). Dipper Throated Algorithm for feature selection and classification in electrocardiogram. Computer Systems Science and Engineering, 45(2), 1469–1482. https://doi.org/10.32604/csse.2023.031943
Joy, S. I., et al. (2023). Review on advent of artificial intelligence in electrocardiogram for the detection of extra-cardiac and cardiovascular disease. IEEE Canadian Journal of Electrical and Computer Engineering, 46(2), 99–106. https://doi.org/10.1109/ICJECE.2022.3228588
Ukil, A., Marin, L., Mukhopadhyay, S. C., & Jara, A. J. (2022). AFSense-ECG: Atrial fibrillation condition sensing from single lead electrocardiogram (ECG) signals. IEEE Sensors Journal, 22(12), 12269–12277. https://doi.org/10.1109/JSEN.2022.3162691
Prakash, A. J., et al. (2023). A new approach of transparent and explainable artificial intelligence technique for patient-specific ECG beat classification. IEEE Sensors Letters, 7(5), 1–4. https://doi.org/10.1109/LSENS.2023.3268677
Singhal, S., & Kumar, M. (2023). A systematic review on artificial intelligence-based techniques for diagnosis of cardiovascular arrhythmia diseases: Challenges and opportunities. Archives of Computational Methods in Engineering, 30(2), 865–888. https://doi.org/10.1007/s11831-022-09823-7
Silva, B. V., Marques, J., Menezes, M. N., Oliveira, A. L., & Pinto, F. J. (2023). Artificial intelligence-based diagnosis of acute pulmonary embolism: Development of a machine learning model using 12-lead electrocardiogram. Revista Portuguesa de Cardiologia, 42(7), 643–651. https://doi.org/10.1016/j.repc.2023.03.016
Zhang, X., et al. (2022). Detection of atrial fibrillation from variable-duration ECG signal based on time-adaptive densely network and feature enhancement strategy. IEEE Journal of Biomedical and Health Informatics, 27(2), 944–955. https://doi.org/10.1109/JBHI.2022.3221464
DOI: https://doi.org/10.31449/inf.v49i8.9297
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