Research & Teaching

Research Interest

1.  Wearable Healthcare Technology

2.  Sleep Quality and Disorder Monitoring 

3.  Monitoring Neurological Disorders

4.  Wearable-based Stress Monitoring 

5. Tiny Machine Learning, XAI

6. Non-Linear Signal Decomposition

PG Thesis Supervision

1. "Automatic Detection of Paroxysmal Atrial Fibrillation Using Machine Learning Technique" by Md Mayenul Islam (MSc, completed May 2024).

2. "Monitoring Mental Fatigue Using EEG Signal" By Md. Shohanur Rahaman (MSc Ongoing).

3. "Automated Sleep Stage Monitoring Using a Wrist-Worn Wearable Device" by Ragib Rowshon (MSc Ongoing).

4. "Automated Classification of Atrial Fibrillation from Electrocardiogram Signals Using Machine Learning Techniques" by Md. Ali Hasan (MSc Ongoing).

5. "Automated Sleep Apnea Detection Using Electrocardiogram: An Interpretable Ensemble Deep Learning Approach" by Mahsumul Alam Rusho (MSc Ongoing).

Selected UG Thesis Supervision

1. "Stress Assessment Using Wrist-Worn Wearable Device", Md Santo Ali (19 Series).

2.  "Deep Learning Based Motor Imagery Intention Classification Using Electroencephalogram Signal" By Muhammad Sudipto Siam Dip (18 Series).

3. "Monitoring Sleep Apnea using Single Lead Electrocardiogram Signal" By Partha Pratim Das Turja (16 Series).

4. "Screening Sleep Disordered Breathing Using A Phone Oximeter" By Nafis Shahriar (16 Series).

5. "Mental Workload Assessment Using ECG and PPG Signal" By Dipta Kumar Ghoshe (16 Series).

Number of UG Theses Completed: 03 (08 Series) + 03 (09 Series) + 03 (16 Series) + 03 (17 Series)+ 06 (18 Series) + 06 (19 Series) = 24

Number of UG Theses Ongoing: 07 (20 Series) + 07 (21 Series) 

Post Graduate Level

1.  Introduction to Machine Learning

2.  Artificial Neural Network

3. Wearable Signal Processing and Machine Learning for Healthcare

4.  Statistical Signal Processing

5.  Statistical Machine Learning

Under Graduate Level

1. Introduction to Machine Learning

2. Digital Signal Processing

3. Signals and Systems

4. Biomedical Engineering