The mHealth Research group aims at developing biomedical signal processing and machine learning algorithms for remote health monitoring, and prototyping innovative mHealth devices for improving people’s health, overall well-being and quality of life.
OUR TEAM:
Istiaq Ansari
Research Assistant
mHealth Research Group
Mohammad Zunaed Rafi
Research Assistant
mHealth Research Group
Dr. Nawsabah Noor
[MBBS, MRCP(UK), ECFMG cert.]
Clinical Researcher
mHealth Research Group
Meemnur Rashid
Research Assistant
mHealth Research Group
Kaisar Alman
Research Assistant
mHealth Research Group
Nusrat Binta Nizam
Lecturer
BME, BUET
Shoyad Nuhash
Lecturer
BME, BUET
Samiul Based Shuvo
Undergraduate Research Student
mHealth Research Group
Shams Nafisa Ali
Undergraduate Research Student
mHealth Research Group
Md. Iqbal Hossain
Undergraduate Research Student
mHealth Research Group
S. M. Jawwad Hossain
Undergraduate Research Student
mHealth Research Group
Tanzila Binti Alam
Undergraduate Research Student
mHealth Research Group
Alfaj Uddin Ahmed
Undergraduate Research Student
mHealth Research Group
Prithwiraj Das
Undergraduate Research Student
mHealth Research Group
Manzil-E Akbar Khan
Undergraduate Research Student
mHealth Research Group
Fardeen Ahmed
Undergraduate Research Student
mHealth Research Group
ADVISORS:
PROJECTS:
PUBLICATIONS:
[1] Mohammad Mostafiz, Rifat Jahan Azad, Nawsabah Noor, Shajib Ghosh, Haris Sair, Paul Nagy, Taufiq Hasan, “A Web-based Assistive Tool for Emergency Physicians in Diagnosing Intracranial Hemorrhage Subtypes from 2D Brain CT Images”, Society for Imaging Informatics in Medicine (SIIM) 2020 Annual Meeting, June 2020, Austin, Texas, USA (abstract).
[2] Nusrat Binta Nizam, Shoyad Nuhash, Ahmed Imtiaz Humayun, Tausif Amim Shadly, Ashraf Ur Rahman, Mohammad Enamul Hakim, AKM Monoarul Islam, Taufiq Hasan, “Cardiac Anomaly Screening Using Machine Learning Enabled Digital Stethoscopes”, Bangladesh Cardiac Society Conference, Dec. 2019, Dhaka, Bangladesh (abstract).
[3] Partho Ghosh, Md Istiak, Nayeeb Rashid, Ahsan Habib Akash, Ridwan Abrar, Ankan Ghosh Dastider, Asif Shahriyar Sushmit, and Taufiq Hasan. “Privacy-Aware Activity Classification from First Person Office Videos.” arXiv preprint arXiv:2006.06246 (2020). [PDF]
[4] Nurul Huda, Sadia Khan, Ragib Abid, Samiul Basir, Maheen Labib and Taufiq Hasan, “A Low-cost, Low-energy Wearable ECG System with Cloud-Based Arrhythmia Detection.” IEEE TenSymp 2020, Dhaka, Bangladesh.
[5] Ahmed Imtiaz Humayun, Shabnam Ghaffarzadegan, Md. Istiaq Ansari, Zhe Feng, and Taufiq Hasan, “Towards Domain Invariant Heart Sound Abnormality Detection using Learnable Filterbanks.” IEEE Journal of Biomedical and Health Informatics, 2020. [PDF]
[6] Ahmed Imtiaz Humayun, Asif Shahriyar, Taufiq Hasan, Mohammed Imamul Hassan Bhuiyan, “End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets", IEEE BHI 2019, Chicago, USA [PDF]
[7] Asif Shahriyar, Shakib Uz Zaman, Ahmed Imtiaz Humayun, Taufiq Hasan, Mohammed Imamul Hassan Bhuiyan, “X-Ray Image Compression Using Convolutional Recurrent Neural Networks", IEEE BHI 2019, Chicago, USA [PDF]
[8] Ahmed Imtiaz Humayun, Md. Tauhiduzzaman Khan, Shabnam Ghaffarzadegan, Zhe Feng and Taufiq Hasan, “An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification”, INTERSPEECH 2018, Hyderabad, India [PDF]
[9] Ahmed Imtiaz Humayun, Shabnam Ghaffarzadegan, Zhe Feng and Taufiq Hasan, “Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection”, EMBC 2018, Hawaii, USA [PDF]
[10] Md. Tauhiduzzaman Khan, Shabnam Ghaffarzadegan, Zhe Feng and Taufiq Hasan, “A Low-cost Wearable Neckband for Robust Diet Activity Monitoring”, BHI 2018, Las Vegas, USA (Poster) [PDF]
Low-cost, Wearable ECG System with Cloud-Based Arrhythmia Detection
Low-cost, Wearable ECG System with Cloud-Based Arrhythmia Detection
Continuous ECG monitoring is an essential tool for Cardiovascular Disease patients but in low resource countries, continuous monitoring of ECG become a challenge due to limited availability of ECG system, high prices and lack of skilled physicians. We present a low-cost, low-power and wireless Flexible fabric-based design of ECG monitoring system with cloud-based automatic arrhythmia detection using Convolutional Neural Network.
Cardiovascular Screening with Heart Sounds
Cardiovascular Screening with Heart Sounds
Cardiovascular diseases cause 31% of global deaths. Machine learning enabled stethoscopes can help in early diagnosis by detecting abnormal heart sounds. Proposed end-to-end Deep Learning framework consists of a Littmann stethoscope that connects to the server via an Android smartphone for real-time diagnosis.
Wearable Smart Neckband for Diet Monitoring
Wearable Smart Neckband for Diet Monitoring
Diet monitoring is crucial for diseases such as: diabetes, obesity, eating disorders, hypertension. Automatic monitoring is more effective and accurate than manual count of intake. Developed a fabric-based low-cost ($5) wearable neckband that monitors user’s eating and drinking activities.
RadAssist: An AI-Assisted Tele-Radiology Platform
RadAssist: An AI-Assisted Tele-Radiology Platform
Radiological imaging diagnosis plays an important role in clinical patient management. We aim to develop advanced deep learning models that can detect clinically important abnormalities from chest X-ray images. Developed system can be integrated within a tele-radiology framework for assisting a radiologist in making more efficient and effective diagnosis.
End-to-End Sleep Staging with EEG
End-to-End Sleep Staging with EEG
Sleep disorders are becoming a global health problem and more prominent in clinical practice as global population and life expectancy increases. Diagnosis of sleep disorders depend largely on the classification accuracy of sleep stages. We developed an end-to-end deep learning system for automatic sleep staging using a single channel EEG.
OxyJet: A Jet Mixing Principle Based Low-Cost CPAP
OxyJet: A Jet Mixing Principle Based Low-Cost CPAP
The COVID-19 pandemic has affected more than 20 million people worldwide with a death toll of over 700 thousand. Although HFNO and NIPPV are effective in treating severe COVID-19 patients, high-cost of these devices make it impossible to procure sufficient amounts of such ventilation devices in low and middle income countries. We propose OxyJet CPAP, a low-cost NIPPV system that is easy to use, manufacture, and implement within an LMIC healthcare infrastructure.
NEWS:
COLLABORATORS & SUPPORTERS:
May 2020: mHealth Lab spin-off project RadAssist wins the 1st Runner-up prize in the “Act COVID-19 National Call” challenge organized by the ICT Ministry, Government of Bangladesh!
April 2020: mHealth Lab team wins the 2nd Runner-up prize in the “Design For Life | Ventilators for ALL” competition organized by Edge, The Foundation!
Dec 2019: mHealth Lab team as an R&D partner of Sonavi Labs (Baltimore, USA) wins the MIT Solve - Tiger Challenge 2019!
Oct 2019: Dr. Taufiq Hasan is invited to Technology Innovation Center, Johns Hopkins School of Medicine as a visiting scholar.
Sep 2019: Dr. Taufiq Hasan is invited to serve as a mentor in the 2019 African Biomedical Engineering Consortium (ABEC) design school to be held in Kampala, Uganda.
Aug 2019: Former Research Assistant Ahmed Imtiaz is starting his PhD at RICE University.
May 2019: Dr. Taufiq Hasan is appointed as a chair of the Biomedical Signal Processing Informatics session at IEEE BHI 2019.
May 2019: Dr. Paul Nagy, Deputy Director of Johns Hopkins Medicine Technology Innovation Center and Associate Professor of Radiology and Radiological Science, joins mHealth Research Group as an Advisor.
April 2019: Congratulations to Anirban Chakraborty and Nusrat Binta Nizam on their success at the Johns Hopkins Design Competition 2019!
April 2019: Paper on Heart Sound Classification and X-Ray Image Compression accepted at IEEE BHI.
October 2018: Dr. Alain Bernard Larbrique, Associate Professor, Johns Hopkins Bloomberg School of Public Health, joined mHealth Research Group as an Advisor.
September 2018: Congratulations to Ahmed Imtiaz Humayun for receiving ISCA Student and Young Scientist Travel grant for attending INTERSPEECH 2018 in Hyderabad, India.
July 2018: Paper on Heart Sound Classification system accepted in INTERSPEECH 2018 to be held in Hyderabad, India.
July 2018: Congratulations to BME undergraduate students Farhana Islam and Mehnaz Urbee for winning 3rd place in the UBORA design competition 2018, Pisa, Italy.
April 2018: Paper on learnable filterbanks for cardiac anomaly detection accepted in IEEE Engineering in Medicine and Biology Conference (EMBC) 2018 to be held in Hawaii, USA.
Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka 1000
© mHealth Lab, BME, BUET, 2019
