Abstract
Cardiac Arrhythmia has grouped into cardiovascular disease, and it occurs when the electrical signals that coordinate the heart’s beats didn’t work properly. Moreover, it can cause the body to have abnormalities ranging from mild to death. Cardiac Arrhythmia is among the leading death rate in the world. It can occur from several reasons such as sleep deprivation, eating fatty foods, and lack of exercise. From the structure of the heart system, it was found that there are 4 functional electrical pathways, e.g., SA node, AV node, bundle branches, and Purkinje fiber. These electrical signals can be read by EKG. In this experiment, 10,000 images of EKG from PhysioBank ATM were used and divided into 4 classes as abnormal SA + AV node, abnormal bundle branches, abnormal Purkinje fiber, and normal condition to train and compare the result of using two CNN models: transfer learning MobileNetV2 model and transfer learning Xception model. Then, these models were used to detect Cardiac Arrhythmia. As a result, the transfer learning MobileNetV2 model has an accuracy of 98.58%. Besides, the transfer learning Xception model coved an accuracy of 94.51%. It can be concluded that the transfer learning MobileNetV2 has higher accuracy than the transfer learning Xception at 4.34%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
WHO the Top 10 Causes of Death (2017–2018)
Seidelmann, S.B., et al.: Retinal vessel calibers in predicting long-term cardiovascular outcomes: the atherosclerosis risk in communities study. Circulation 134, 1328–1338 (2016)
D’Agostino, R.B., Sr., et al.: General cardiovascular risk profile for use in primary care: the Framingham heart study. Circulation 117, 743–753 (2008)
Wilson, P.W., et al.: Prediction of coronary heart disease using risk factor categories. Circulation 97, 1837–1847 (1998)
Conroy, R.M., et al.: Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur. Heart J. 24, 987–1003 (2003)
Goff, D.C., et al.: 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association task force on practice guidelines. Circulation 129, S49–S73 (2014)
Yeboah, J., et al.: Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals. JAMA 308, 788–795 (2012)
Cooney, M.T., et al.: How much does HDL cholesterol add to risk estimation? A report from the SCORE investigators. Eur. J. Cardiovasc. Prev. Rehabil. 16, 304–314 (2009)
Cardiovascular disease (10-Year Risk) (Framingham Heart Study). Accessed 21 June 2017. https://www.framinghamheartstudy.org/risk-functions/cardiovascular-disease/10-year-risk.php
Dudina, A., et al.: Relationships between body mass index, cardiovascular mortality, and risk factors: a report from the SCORE investigators. Eur. J. Cardiovasc. Prev. Rehabil. 18, 731–742 (2011)
Wang, J.J., et al.: Retinal vascular calibre and the risk of coronary heart disease-related death. Heart 92, 1583–1587 (2006)
Wong, T.Y., et al.: Quantitative retinal venular caliber and risk of cardiovascular disease in older persons: the cardiovascular health study. Arch. Intern. Med. 166, 2388–2394 (2006)
McGeechan, K., et al.: Prediction of incident stroke events based on retinal vessel caliber: a systematic review and individual-participant meta-analysis. Am. J. Epidemiol. 170, 1323–1332 (2009)
McGeechan, K., et al.: Meta-analysis: retinal vessel caliber and risk for coronary heart disease. Ann. Intern. Med. 151, 404–413 (2009)
Wong, T.Y., et al.: Retinal arteriolar narrowing and risk of coronary heart disease in men and women. The Atherosclerosis Risk in Communities Study. JAMA 287, 1153–1159 (2002)
Wong, T.Y., et al.: Retinal microvascular abnormalities and 10-year cardiovascular mortality: a population-based case-control study. Ophthalmology 110, 933–940 (2003)
Cheung, C.Y.-L., et al.: Retinal microvascular changes and risk of stroke: the Singapore Malay eye study. Stroke 44, 2402–2408 (2013)
Cheung, C.Y., et al.: Retinal vascular fractal dimension and its relationship with cardiovascular and ocular risk factors. Am. J. Ophthalmol. 154, 663-674.e1 (2012)
Xu, K., et al.: Show, attend, and tell: neural image caption generation with visual attention. Preprint at https://arxiv.org/abs/1502.03044 (2015)
Kawasaki, R., et al.: Fractal dimension of the retinal vasculature and risk of stroke: a nested case-control study. Neurology 76, 1766–1767 (2011)
Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016)
Angermueller, C., Pärnamaa, T., Parts, L., Stegle, O.: Deep learning for computational biology. Mol. Syst. Biol. 12, 878 (2016)
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualizing image classification models and saliency maps. Preprint at https://arxiv.org/abs/1312.6034 (2013)
Ting, D.S.W., et al.: Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318, 2211–2223 (2017)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
Cho, K., Courville, A., Bengio, Y.: Describing multimedia content using attention-based encoder-decoder networks. IEEE Trans. Multimed. 17, 1875–1886 (2015)
Roy Chowdhury, S., Koozekanani, D.D., Parhi, K.K.: DREAM: diabetic retinopathy analysis using machine learning. IEEE J. Biomed. Health Inform. 18, 1717–1728 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., et al. (eds.) Proceedings of 25th Conference on Advances in Neural Information Processing Systems 1097–1105 (Neural Information Processing Systems, 2012)
Cohen, J.: Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit. Psychol. Bull. 70(4), 213 (1968)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. Preprint at https://arxiv.org/abs/1502.03167 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Akarajaka, T., Leksakul, K., Suedumrong, C., Charoenchai, N. (2023). Application of Deep Convolutional Neural Networks MobileNetV2 and Xception for Detecting Cardiac Arrhythmia. In: Nagar, A.K., Singh Jat, D., Mishra, D.K., Joshi, A. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 578. Springer, Singapore. https://doi.org/10.1007/978-981-19-7660-5_51
Download citation
DOI: https://doi.org/10.1007/978-981-19-7660-5_51
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-7659-9
Online ISBN: 978-981-19-7660-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)