Detection and Classification of Unhealthy Heartbeats Using Deep Learning Techniques.

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Medicine
• Cardiology

Background:

• Arrhythmias are common cardiac disorders requiring early detection for better outcomes.
• Manual interpretation of electrocardiograms (ECGs) for arrhythmias can be inconsistent.
• Automated classification of ECG signals is needed to improve diagnostic accuracy.

Purpose of the Study:

• To investigate the effectiveness of machine learning and deep learning for automated arrhythmia classification.
• To compare traditional machine learning models with a hybrid deep learning approach.
• To optimize a deep learning model using metaheuristic optimization for enhanced performance.

Main Methods:

• Utilized the MIT-BIH dataset for ECG signal analysis.
• Compared Gradient Boosting Machine (GBM) and Multilayer Perceptron (MLP) models.
• Developed and evaluated a hybrid 1D-CNN-LSTM deep learning model.
• Employed Grey Wolf Optimizer (GWO) for hyperparameter tuning of the 1D-CNN-LSTM model.

Main Results:

• The proposed 1D-CNN-LSTM model achieved a high accuracy of 97%.
• The hybrid deep learning model significantly outperformed classical machine learning models (GBM, MLP).
• Classification reports and confusion matrices demonstrated the model's robustness in identifying diverse arrhythmia types.

Conclusions:

• Hybrid deep learning models, optimized with metaheuristic algorithms like GWO, show significant promise for automated arrhythmia detection.
• This approach offers a more accurate and consistent alternative to manual ECG interpretation.
• Integrating advanced AI techniques can substantially improve clinical outcomes in cardiac disorder management.