Affiliation:
1. St.Joseph’s College of Arts and Science for Women, Hosur, Tamil Nadu, India.
Abstract
Machine learning has become increasingly useful in various medical applications. One such case is the automatic categorization of ECG voltage data. A method of categorization is proposed that works in real time to provide fast and accurate classifications of heart beats. This proposed method uses machine learning principles to allow for results to be determined based on a training dataset. The goal of this project is to develop a method of automatically classifying heartbeats that can be done on a low level and run on portable hardware.
Subject
General Mathematics,General Physics and Astronomy,General Agricultural and Biological Sciences,General Environmental Science,General Medicine,Multidisciplinary,Nutrition and Dietetics,Medicine (miscellaneous),Insect Science,Physiology,Ecology, Evolution, Behavior and Systematics,Insect Science,Ecology, Evolution, Behavior and Systematics,General Physics and Astronomy,General Engineering,General Mathematics,General Agricultural and Biological Sciences,General Environmental Science,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine
Reference9 articles.
1. “MIT-BIH Arrhythmia Database Directory”, Physionet.org, 2019. [Online]. Available: https://physionet.org/physiobank/database/html/mitdbdir/mitdbdir.htm.
2. Zhang, D. Zhou and X. Zeng,” Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals”, BioMedical Engineering OnLine, vol. 16, no. 1, 2017. Available: 10.1186/s12938-017-0317-z.
3. Mahmud et al.” SensoRing: An Integrated Wearable System for Continuous Measurement of Physiological Biomarkers”, Presented at the 2018 IEEE International Conference on Communications (ICC), MO, USA, 2018.
4. Mahmud, H. Fang, H. Wang, S. Carreiro, E. Boyer,” Automatic Detection of Opioid Intake Using Wearable Biosensor”, IEEE International Conference on Computing, Networking and Communications (ICNC), Maui, Hawaii, 2018.
5. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G.S. Corrado, A. Davis, J. Dean, M. Devin, et al., Tensorflow: large-scale machine learning on heterogeneous distributed systems 2016, pp. 1–19 arXiv preprint arXiv: 1603.04467.