Sudden Cardiac Arrest Detection Based on Temporal Features of ECG Using Support Vector Machine Classifier-Reference-Cited by-同舟云学术

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Sudden Cardiac Arrest Detection Based on Temporal Features of ECG Using Support Vector Machine Classifier

Published:2022 Issue: Volume: Page:201-208
ISSN:1876-1100
Container-title:Proceedings of International Conference on Industrial Instrumentation and Control
language:
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Author:

Banerjee Prakash,Bhattacherjee Saptak,Dasgupta Kousik

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-7011-4_20

Reference19 articles.

1. Caesarendra, W., Ismail, R., Kurniawan, D., Karwiky, G., Ahmad, C.: Sudden cardiac death predictor based on spatial QRS-T angle feature and support vector machine case study for cardiac disease detection in Indonesia. In: 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 186–192. IEEE (2016)

2. Vanitha, L., Suresh, G., JenefarSheela, C.: Sudden cardiac death prediction system using hybrid classifier. In: 2014 International Conference on Electronics and Communication Systems (ICECS), pp. 1–5. IEEE (2014)

3. Devi, R., Tyagi, H.K., Kumar, D.: Early stage prediction of sudden cardiac death. In: 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). pp. 2005–2008. IEEE (2017)

4. Riasi, A., Mohebbi, M.: Prediction of ventricular tachycardia using morphological features of ECG signal. In: 2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP), pp. 170–175. IEEE (2015)

5. Vijjaya, V., Rao, K.K., Sahrudai, P.: Identification of sudden cardiac arrest using the pan-tompkins algorithm. In: 2012 UKSim 14th International Conference on Computer Modeling and Simulation, pp. 97–100. IEEE (2012)

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1. Prediction of Cardiac Arrest using Artificial Intelligence - A Systematic Review;2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N);2022-12-16

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