Abstract
Arrhythmia categorization is an exciting research in the early prevention and detection of cardiovascular illnesses, using Electrocardiogram (ECG). In the case of ECG signals, time series data are obtained by changing the time. This type of signal has the drawback of requiring repeated acquisition of comparison data with the same size as the registration data. Resolving the issue of inconsistent data size is accomplished by the use of an additional classifier-based adversarial neural networks. Adversarial data synthesis using Generative Adversarial Networks (GANs) and the generation of additional training examples solves the basic problem of insufficient data labelling. Recent studies have used the GAN architecture to create synthetic adversarial ECG signals in order to boost the amount of training data already available. The arrhythmia detection system, on the other hand, has a fragmented Convolution Neural Network (CNN) classification architecture. No flexible structural design has yet been suggested that can simultaneously discover and order abnormalities. An exceptional Prioritized Feature Subset Vector-Associated Generative Adversarial Network (PFSV-AGAN) is proposed in this research in order at a time produce ECG indications for multiple classes and sense heart-related problems. Furthermore, the model is based on class-specific ECG signals in order to generate realistic adversarial cases. This research presents a framework for ECG signal abnormality identification that has an unbalanced distribution among classes and achieves high accuracy in abnormalities categorization. After training on datasets, the classification model reliably identifies abnormalities in the proposed model. The proposed model when compared to the traditional model exhibits better performance levels.
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References
Al’Aref SJ, Anchouche K, Singh G, et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J. 2019;40(24):1975–86.
Fritsch P, Dalla Pozza R, Ehringer-Schetitska D, et al. Cardiovascular pre-participation screening in young athletes: recommendations of the association of European paediatric cardiology. Cardiol Young. 2017;27(9):1655–60.
Yang Y, Zhang E, Zhang J, et al. Relationship between occupational noise exposure and the risk factors of cardiovascular disease in China: a meta-analysis. Medicine (Baltimore). 2018;97(30): e11720.
Bhatia RS, Dorian P. Screening for cardiovascular disease risk with electrocardiography. JAMA Intern Med. 2018;178(9):1163–4.
Asif IM, Drezner JA. Cardiovascular screening in young athletes: evidence for the electrocardiogram. Curr Sports Med Rep. 2016;15(2):76–80.
Falter M, Budts W, Goetschalckx K, Cornelissen V, Buys R. Accuracy of apple watch measurements for heart rate and energy expenditure in patients with cardiovascular disease: cross-sectional study. JMIR Mhealth Uhealth. 2019;7(3): e11889.
Tracer H, Jadotte YT. Screening for cardiovascular disease risk with electrocardiography. Am Fam Phys. 2018;98(6):375–6.
Xu X, Wei S, Ma C, et al. Atrial fibrillation beat identification using the combination of modified frequency slice wavelet transform and convolutional neural networks. J Healthc Eng. 2018;2102918:8.
Acharya UR, Oh SL, Hagiwara Y, et al. A deep convolutional neural network model to classify heartbeats. Comput Biol Med. 2017;89:389–96.
Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. The Lancet. 2019;394(10201):861–7.
Sodmann P, Vollmer M, Nath N, Kaderali L. A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms. Physiol Meas. 2018;39(10):104005.
Carrara M, Carozzi L, Moss TJ, et al. Classification of cardiac rhythm using heart rate dynamical measures: validation in MIT-BIH databases. J Electrocardiol. 2015;48(6):943–6.
Marsili IA, Biasiolli L, Masè M, et al. Implementation and validation of real-time algorithms for atrial fibrillation detection on a wearable ECG device. Comput Biol Med. 2020;116:103540.
Zhou F-Y, Jin L-P, Dong J. Premature ventricular contraction detection combining deep neural networks and rules inference. Artif Intell Med. 2017;79:42–51.
Asatryan B, Servatius H. Revisiting the approach to diagnosis of arrhythmogenic cardiomyopathy: stick to the arrhythmia criterion! Circ Genom Precis Med. 2019;12(10):455–7.
May RW, Seibert GS, Sanchez-Gonzalez MA, Fincham FD. School burnout and heart rate variability: risk of cardiovascular disease and hypertension in young adult females. Stress. 2018;21(3):211–6.
Jonas DE, Reddy S, Middleton JC, et al. Screening for cardiovascular disease risk with resting or exercise electrocardiography. JAMA. 2018;319(22):2315–28.
Zhang J, Yao R, Ge W, Gao J. Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG. Comput Methods Progr Biomed. 2020;183:105089.
Übeyli ED. Combining recurrent neural networks with eigenvector methods for classification of ECG beats. Digit Signal Process. 2009;19(2):320–9.
Froese T, Hadjiloucas S, Galvão RKH, et al. Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms. Pattern Recognit Lett. 2006;27(5):393–407.
Kanani P, Padole M. ecg heartbeat arrhythmia classification using time-series augmented signals and deep learning approach. Proc Comput Sci. 2020;171:524–31.
Latif G, Al Anezi FY, Zikria M, et al. EEG-ECG Signals classification for arrhythmia detection using decision trees. In: Proceedingsof the Fourth International Conference on Inventive Systems and Control (ICISC 2020). Coimbatore, 2020; pp. 192–6.
Wu J, Li F, Chen Z, et al. Patient-specific ECG classification with integrated long short-term memory and convolutional neuralnetworks. IEICE Trans Inform Syst. 2020;E103D(5):1153–63.
Zhang J, Tian J, Cao Y, et al. Deep time–frequency representation and progressive decision fusion for ECG classification. Knowl-Based Syst. 2020;190: 105402.
Shankar MG, Babu CG. An exploration of ECG signal feature selection and classification using machine learning techniques. Int J Innov Technol Explor Eng. 2020;9(3):797–804.
Diker A, Avci E, Tanyildizi E, et al. A novel ECG signal classification method using DEA-ELM. Med Hypotheses. 2020;136: 109515.
Rida I, Al-Maadeed N, Al-Maadeed S, et al. A comprehensive overview of feature representation for biometric recognition. Multimed Tools Appl. 2020;79:4867–90.
Fei L, Zhang B, Xu Y, Tian C, Rida I, Zhang D. Jointly heterogeneous palmprint discriminant feature learning. IEEE Trans Neural Netw Learn Syst. 2022;33(9):4979–90.
Boubchir RL, Al-Maadeed N, Al-Maadeed S., Robust model-free gait recognition by statistical dependency feature selection and Globality-Locality Preserving Projections. In: 2016 39th International Conference on Telecommunications and Signal Processing (TSP), Vienna, Austria, 2016; pp. 652–55.
Rida,I. Temporal signals classification. (Classification de signaux temporels) (Doctoral dissertation, Normandy University, France). 2017.
Imad Rida SA, Bouridane A. Gait recognition based on modified phase-only correlation. Signal, Image Video Process. 2016;10:463–70.
Anwar S, Rida I. Data augmentation-based novel deep learning method for deep faked images detection. ACM Trans Multimed Comput Commun Appl. 2023.
Rida I. Feature extraction for temporal signal recognition: an overview. In: Audio and speech processing, arXiv:1812.01780, https://doi.org/10.48550/arXiv.1812.01780.
Parashar A, Ding W, Rida I. Intra-class variations with deep learning-based gait analysis: A comprehensive survey of covariates and methods. Neurocomputing. 2022;505:315–38 (ISSN 0925-2312).
Acknowledgements
Shaik Janbhasha thanks to Dr Naga Kishore Bhavanam for his complete technical advice and guidance on the preparation of this article, and the anonymous reviewers for their valuable feedback.
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SNKB suggested and guided complete research work, according to his suggestion and guidance SJ did this work. We would like to confirm that all authors were fully involved in the study and preparation of the manuscript.
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Shaik, J., Bhavanam, S.N. Arrhythmia Detection Using ECG-Based Classification with Prioritized Feature Subset Vector-Associated Generative Adversarial Network. SN COMPUT. SCI. 4, 519 (2023). https://doi.org/10.1007/s42979-023-01970-3
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DOI: https://doi.org/10.1007/s42979-023-01970-3