Abstract
In this paper, we propose a demonstrable prototype of an on-device cardiac monitoring system comprising bio-sensor module and a low-powered microcontroller. Apart from measuring physiological vitals, the proposed system can classify abnormal heart rhythms on the microcontroller itself for low-cost 24\(\,\times \,\)7 unobtrusive monitoring. A Convolutional Neural network (CNN) is duly optimized to run on the constrained hardware platform for identification of normal, Atrial Fibrillation (AF) and other abnormal rhythms from single-lead electrocardiogram (ECG) signals. The system is successfully verified on offline dataset. It also reports promising accuracy when deployed for real-time health monitoring.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Max86150 sensor. https://datasheets.maximintegrated.com/en/ds/MAX86150.pdf. Accessed 30 Apr 2022
Clifford, G.D., et al.: Af classification from a short single lead ecg recording: the physionet/computing in cardiology challenge 2017. In: 2017 Computing in Cardiology (CinC), pp. 1–4. IEEE (2017)
Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. ar**v preprint ar**v:1704.04861 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Banerjee, R., Ghose, A. (2023). A Light Weight Cardiac Monitoring System for On-device ECG Analysis. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13718. Springer, Cham. https://doi.org/10.1007/978-3-031-26422-1_49
Download citation
DOI: https://doi.org/10.1007/978-3-031-26422-1_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26421-4
Online ISBN: 978-3-031-26422-1
eBook Packages: Computer ScienceComputer Science (R0)
