Abstract
Myocardial Infarction (MI) is a fatal heart disease that is a leading cause of death. The silent and recurrent nature of MI requires real-time monitoring on a daily basis through wearable devices. Real-time MI detection on wearable devices requires a fast and energy-efficient solution to enable long term monitoring. In this paper, we propose an MI detection methodology using Binary Convolutional Neural Network (BCNN) that is fast, energy-efficient and outperforms the state-of-the- art work on wearable devices. We validate the performance of our methodology on the well known PTB diagnostic ECG database from PhysioNet. Evaluation on real hardware shows that our BCNN is faster and achieves up to 12x energy efficiency compared to the state-of-the-art work.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
