Efficient ECG classification based on the probabilistic Kullback-Leibler divergence
Informatics in Medicine Unlocked, ISSN: 2352-9148, Vol: 47, Page: 101510
2024
- 8Citations
- 2Captures
- 1Mentions
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Study Findings on Health Information Technology Published by Researchers at University of Al-Qadisiyah (Efficient ECG classification based on the probabilistic Kullback-Leibler divergence)
2024 JUN 05 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Daily -- New study results on health information technology have
Article Description
Diagnostic systems of cardiac arrhythmias face early and accurate detection challenges due to the overlap of electrocardiogram (ECG) patterns. Additionally, these systems must manage a huge number of features. This paper proposes a new classifier Kullback-Leibler classifier (KLC) that combines feature optimization and probabilistic Kullback-Leibler (KL) divergence. Particle swarm optimization (PSO) is used for optimizing the features of ECG data, while KL divergence counts the variance between training and testing probability distributions. The proposed framework led the new classifier to distinguish normal and abnormal rhythms accurately. MIT-BIH Standard Arrhythmia Dataset (MIT-BIH) is used to test the validity of the proposed model. The experimental results show the proposed classifier achieves results in precision (86.67%), recall (86.67%), and F1_Score (86.5%).
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2352914824000662; http://dx.doi.org/10.1016/j.imu.2024.101510; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85191971899&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S2352914824000662; https://dx.doi.org/10.1016/j.imu.2024.101510
Elsevier BV
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