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:

Asif Shahriyar

Research Assistant

mHealth Research Group

Istiaq Ansari

Research Assistant

mHealth Research Group

Dr. Nawsabah Noor

[MBBS, MRCP(UK), ECFMG cert.]

Clinical Researcher

mHealth Research Group

Farhat Binte Azam

Research Assistant

mHealth Research Group

Sadi Mohammad

Research Assistant

mHealth Research Group

Uday Kamal

Research Assistant

mHealth Research Group

Mehnaz Urbee

Undergraduate Research Student

mHealth Research Group

Anirban Chakraborty

Undergraduate Research Student

mHealth Research Group

Meer Maheen Labib

Undergraduate Research Student

mHealth Research Group

Nurul Huda

Undergraduate Research Student

mHealth Research Group

Farhana Islam

Undergraduate Research Student

mHealth Research Group

Kaisar Alman

Undergraduate Research Student

mHealth Research Group

Nusrat Binta Nizam

Undergraduate Research Student

mHealth Research Group

Meemnur Rashid

Undergraduate Research Student

mHealth Research Group

Sadia Khan

Undergraduate Research Student

mHealth Research Group

Shoyad Nuhash

Undergraduate Research Student

mHealth Research Group

ADVISORS:

PROJECTS:

PUBLICATIONS:

 

 

 

 

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.

 

 

 

CONTACT US:

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Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka 1000

 

© mHealth Lab, BME, BUET, 2019