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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.
Sensors (Basel, Switzerland)
|September 28, 2023
Summary
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.
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.
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