Deep-Learning-Based Arrhythmia Detection Using ECG Signals: A Comparative Study and Performance Evaluation

Area of Science:

• Cardiology
• Biomedical Engineering
• Artificial Intelligence

Background:

• Heart disease is a leading global cause of mortality.
• Arrhythmia presents a significant health risk, necessitating early detection.
• Electrocardiogram (ECG) signals are crucial for identifying cardiac irregularities.

Purpose of the Study:

• To investigate deep learning methods for automated arrhythmia identification from ECG data.
• To evaluate the performance of a novel small Convolutional Neural Network (CNN).
• To compare the proposed CNN against established pretrained models like GoogLeNet.

Main Methods:

• Utilized deep learning techniques for pattern recognition in ECG signals.
• Developed and trained a small Convolutional Neural Network (CNN).
• Benchmarked the CNN's performance against GoogLeNet using metrics like accuracy, specificity, precision, and F1 score.

Main Results:

• Deep learning models demonstrated high efficacy in identifying arrhythmia from ECG.
• The proposed small CNN achieved competitive performance.
• Comparative analysis highlighted the potential of deep learning for arrhythmia detection.

Conclusions:

• Deep learning-based approaches show significant promise for accurate arrhythmia identification using ECG.
• Early and precise detection of arrhythmia can lead to timely medical intervention.
• The developed CNN offers a viable tool for clinical application in cardiovascular health.