I had the privilege to work with and supervise the following talented postgraduate students (annually updated):
MSc in Computer Science Thesis Title (2025): Detection and Classification of Unhealthy Heartbeats Using Deep Learning Techniques.
Supervision Team: Dr. Abdullah M. Albarrak (Main) & Dr. Ibrahim Abdualmonam Ibrahim (Co-supervisor)
Abstract: Arrhythmias are a common and potentially life-threatening category of cardiac disorders, making accurate and early detection crucial for improving clinical outcomes. Electrocardiograms are widely used to monitor heart rhythms, yet their manual interpretation remains prone to inconsistencies due to the complexity of the signals.
This thesis investigates the effectiveness of machine learning and deep learning techniques for automated arrhythmia classification using ECG signals from the MIT-BIH dataset. Specifically, this work proposes a hybrid deep learning model combining 1D-CNN and LSTM networks for arrhythmia classification. This model is further enhanced by incorporating a preprocessing step to remove noise from ECG signals. Furthermore, the Grey Wolf Optimizer (GWO), a metaheuristic optimization algorithm, automatically adjusts the model's hyperparameters to enhance its classification performance.
By comparing with traditional methods such as GBM and MLP, experimental results indicate that the proposed 1D-CNN-LSTM model achieved the highest accuracy of 97%, outperforming both classical machine learning and other deep learning baselines. The classification report and confusion matrix confirm the model’s robustness in identifying various arrhythmia types. These findings emphasize the possible benefits of integrating metaheuristic optimization with hybrid deep learning.