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Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT.

Tariq Sadad1, Mejdl Safran2, Inayat Khan1

  • 1Department of Computer Science, University of Engineering & Technology, Mardan 23200, Pakistan.

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This study presents an IoT system for early cardiac disorder detection using deep learning on ECG images. The novel approach achieves 98.39% accuracy, aiding in cardiovascular disease management.

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Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Artificial Intelligence

Background:

  • Cardiovascular diseases (CVDs) are a major global health concern, necessitating advanced diagnostic and preventative strategies.
  • Electrocardiogram (ECG) is a vital tool for assessing cardiac health, but widespread accessibility remains a challenge.
  • Telemedicine solutions offer a promising avenue for accessible and cost-effective CVD management.

Purpose of the Study:

  • To develop an integrated Internet of Things (IoT) based system for real-time monitoring and detection of cardiac disorders.
  • To enhance the accuracy and efficiency of ECG analysis through advanced deep learning techniques.
  • To combine efficient data routing protocols with sophisticated image classification for improved cardiovascular diagnostics.

Main Methods:

  • A two-stage approach was implemented: Stage 1 utilized a hybrid routing protocol (REL with DSR) for efficient data collection on an IoT healthcare platform.
  • Stage 2 involved ECG image classification using a lightweight Convolutional Neural Network (CNN) for automatic feature extraction.
  • An attention module was employed to optimize the extracted deep features, enhancing classification performance.

Main Results:

  • The developed system achieved a high classification accuracy of 98.39% in identifying cardiac abnormalities from 12-lead ECG images.
  • The system successfully differentiated between normal ECGs, abnormal heartbeats, myocardial infarction (MI), and previous history of MI.
  • The integration of IoT, deep learning, and optimized routing protocols demonstrated robust performance in ECG analysis.

Conclusions:

  • The proposed IoT-based system offers a highly accurate and efficient method for the early detection and management of cardiac disorders.
  • This approach holds significant potential for improving the diagnosis and remote monitoring of cardiovascular diseases.
  • The study highlights the synergy between IoT, deep learning, and efficient data handling in modern healthcare applications.