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2(1), (2023):21-26 DOI: https://doi.org/10.46632/jdaai/2/1/4
N. Bindhu, N. Mageswari, B. U. Archana, V. Niranjana.
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.
“MIT-BIH Arrhythmia Database Directory”, Physionet.org, 2019. [Online]. Available: https://physionet.org/physiobank/database/html/mitdbdir/mitdbdir.htm.
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.
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.
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.
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.
Kachuee, S. Fazeli and M. Sarrafzadeh,” ECG Heartbeat Classification: A Deep Transferable Representation”, CoRR, vol. 180500794, 2018. Available: http://arxiv.org/abs/1805.00794.
Sannino and G. De Pietro,” A deep learning approach for ECGbased heartbeat classification for arrhythmia detection”, Future Generation Computer Systems, vol. 86, pp. 446-455, 2018. Available: 10.1016/j.future.2018.03.057.
Fandango, Mastering TensorFlow 1.x. [S.l.]: Packt Publishing, 2018.
Hanyu and C. Xiaohui,” Motion artifact detection and reduction in PPG signals based on statistics analysis,” 2017 29th Chinese Control and Decision Conference.
N. Bindhu, N. Mageswari, B. U. Archana, V. Niranjana. “Robust Classification of Cardiac Arrhythmia Using a Deep Neural Network.” REST Journal on Data Analytics and Artificial Intelligence 2(1), (2023):21-26.