The following projects are inactive; however, there are things to be done on both projects, but my priority of pursuing these projects is lower than the other projects. If you are interested, please contact me as well.

Problem-based learning in bioinstrumentation lectures

In our redesigned bioinstrumentation lectures, students are encouraged to participate real-life problem solving. We use a bedside electromyography (EMG)-based communication device as the theme of the redesigned bioinstrumentation course. In the course, students are taught relevant knowledge in sensors, conditioning, and digitization to recreate the themed device as a real-life design exercise, in addition to the lab modules they complete.

Better Detection of Atrial Fibrillation with Generated Electrocardiogram Signals

Training machine learning-based medical devices is difficult. The prevalence of most medical conditions is low, which results in class imbalance collected in real-life data sets. We aim to use generative neural networks to generate atrial fibrillation-like electrocardiogram (ECG) signals to improve the accuracy of detection in deep learning-based atrial fibrillation detectors.

Our AFE-GAN successfully generates ECG signals with atrial fibrillation characteristics that can trick existing atrial fibrillation detectors. Our next stage would be to demonstrate improvements to existing ECG detection systems.