Energy Efficient ECG Classifier Using Smart Lead Switching and Apache TVM

International Conference on Machine Learning and Applications, vol. 11, pp. 188–193

Abstract

The classification of Electrocardiograms (ECG) sig-nals is vital for diagnosing and monitoring cardiovascular diseases. Recently, there has been increasing interest in leveraging efficient machine learning models for ECG classification. Op-timizing computational resources in this domain is crucial for real-time analysis and reducing power consumption. This paper explores two approaches to enhance the efficiency of ECG classification on edge devices. Firstly, a smart lead-switching mechanism intelligently switches between a high-power model and a low- power model. Secondly, Apache Tensor Virtual Machine (TVM) is employed to optimize the implementation of the developed models on the Jetson Nano single-board computer. Experimental results demonstrate noticeable achievements, including up to 96% reduction in FLOPS and 95% reduction in inference time. Our results hold significant promise for improving ECG classification on edge devices, enabling early detection of abnormalities, and enhancing patient care.

Authors 5

1. Ahmad Ayad
   Affiliation as printed

   Chair of Information Theory and Data Analytics

2. Mahdi Barhoush
   Affiliation as printed

   Chair of Information Theory and Data Analytics

3. Benedikt Völker
   Affiliation as printed

   Chair of Information Theory and Data Analytics

4. Steffen K. Leonhardt Aachen Chair or Medical Information Technology

   RWTH Aachen University

   Affiliation as printed

   Chair or Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University,Aachen,Germany,D-52074

5. Anke Schmeink
   Affiliation as printed

   Chair of Information Theory and Data Analytics