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Cloud-based ECG monitoring using event-driven ECG acquisition and machine learning techniques

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Abstract

An approach is proposed for the detection of chronic heart disorders from the electrocardiogram (ECG) signals. It utilizes an intelligent event-driven ECG signal acquisition system to achieve a real-time compression and effective signal processing and transmission. The experimental results show that grace of event-driven nature an overall 2.6 times compression and bandwidth utilization gain is attained by the suggested solution compared to the counter classical methods. It results in a significant reduction in the complexity and execution time of the post denoising, features extraction and classification processes. The overall system precision is studied in terms of the classification accuracy, the F-measure, the area under the ROC curve (AUC) and the Kappa statistics. The best classification accuracy of 94.07% is attained. It confirms that the designed event-driven solution realizes a computationally efficient automatic diagnosis of the cardiac arrhythmia while achieving a high precision decision support for cloud-based mobile health monitoring.

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Acknowledgements

Authors are thankful to anonymous reviewers for their valuable feedback.

Funding

This project is funded by the Effat University with the decision number UC#7/28Feb 2018/10.2-44 g.

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  1. College of Engineering, Effat University, Jeddah, 21478, Saudi Arabia

    Saeed Mian Qaisar & Abdulhamit Subasi

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  1. Saeed Mian Qaisar
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  2. Abdulhamit Subasi
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Correspondence to Saeed Mian Qaisar.

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Mian Qaisar, S., Subasi, A. Cloud-based ECG monitoring using event-driven ECG acquisition and machine learning techniques. Phys Eng Sci Med 43, 623–634 (2020). https://doi.org/10.1007/s13246-020-00863-6

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  • DOI: https://doi.org/10.1007/s13246-020-00863-6

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