Real-time classification technique for early detection and prevention of myocardial infarction on wearable devices, 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)

Continuous monitoring of patients suffering from cardiovascular diseases and, in particular, myocardial infarction (MI) places a considerable burden on health-care systems and government budgets. The rise of wearable devices alleviates this burden, allowing for long-term patient monitoring in ambulatory settings. One of the major challenges in this area is to design ultra-low energy wearable devices for long-term monitoring of patients’ vital signs. In this work, we present a real-time event-driven classification technique, based on support vector machines (SVM) and statistical outlier detection. The main goal of this technique is to maintain a high classification accuracy while reducing the complexity of the classification algorithm. This technique leads to a reduction in energy consumption and thus battery lifetime extension. We validate our approach on a well-established and complete myocardial infarction (MI) database (Physiobank, PTB Diagnostic ECG database [1]). Our experimental evaluation demonstrates that our real-time classification scheme outperforms the existing approaches in terms of energy consumption and battery lifetime by a factor of 3, while maintaining the classification accuracy at a medically-acceptable level of 90%.

Published in:
Proceedings of the 2017 IEEE Biomedical Circuits & Systems Conference (BioCAS), 1, 1, 1-4
Presented at:
13th IEEE Biomedical Circuits and Systems Conference, Turin, Italy, October 19-21, 2017
2017 IEEE Biomedical Circuits & Systems Conference (BioCAS), Turin, Italy, October 19-21, 2017
New York, IEEE
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 Record created 2017-08-28, last modified 2018-04-28

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