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 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

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.

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

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.

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.

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.