AI-Powered Real-Time ECG Monitoring and Analysis
Keywords:
Health care, Cardiovascular, ECG, Internet of Things.Abstract
This research focuses on an artificial intelligence-based electrocardiogram (ECG) monitoring system with the purpose of improving patient care. The monitoring and diagnosis of cardiac diseases is accomplished via the use of machine learning algorithms, and it supplies hospitals and medical professionals with correct medical data. For the purpose of identifying aberrant beats, detecting arrhythmias, and measuring heart rate variability, the system makes use of sophisticated methods such as convolutional neural networks and recurrent neural networks. A user interface that is straightforward to understand is also provided, making it possible to obtain ECG data with ease. For the purpose of continually monitoring and diagnosing cardiac problems, this system is intended to offer a solution that is both dependable and economical.
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References
Wong CH, Siah KW, Lo AW. Estimation of Clinical Trial Success Rates and Related Parameters. Biostatistics 2019;20(2): 273–86.h_ps://doi.org/10.1093/biostatistics/kxx069
Askin S, Burkhalter D, Calado G, El Dakrouni S. Arti_cial Intelligence Applied to Clinical Trials: Opportunities and Challenges. Health and Technology 2023;13(2): 203–13. h_ps://doi.org/10.1007/s12553-023-00738-2.
Mathew A, Amudha P, Sivakumari S. Deep Learning Techniques: An Overview. In Advanced Machine Learning Technologies and Applications, edited by Hassanien AE, Bhatnagar R, Darwish A, 1141:599–608. Singapore: Springer Singapore, 2021.
Badilini F, Sarapa N. Implications of Methodological Di_erences in Digital Electrocardiogram Interval Measurement. Journal of Electrocardiology 2006;39(4): S152–6h_ps://doi.org/10.1016/j.
jelectrocard.2006.05.030.
He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pa_ern Recognition (CVPR), 770–8. Las Vegas, NV: IEEE, 2016. h_ps://doi.org/10.1109/CVPR.2016.90.
Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J. Clinical Development Success Rates for Investigational Drugs. Nature Biotechnology 2014;32(1): 40–51. h_ps://doi.org/10.1038/nbt.2786.
ICH E14. “Questions & Answers (R3),” December 10, 2015. h_ps://database.ich.org/sites/default/_les/E14_Q%26As_R3_Q%26As.pdf.
Darpo B, Borin M, Ferber G, Galluppi GR, Hopkins SC, Landry I, et al. ECG Evaluation as Part of the Clinical Pharmacology Strategy in the Development of New Drugs: A Review of Current Practices and Opportunities Based on _ve Case Studies. The Journal of Clinical Pharmacology 2022;62(12): 1480–500. h_ps://doi.org/10.1002/jcph.2095.
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