OMNI
Motivation
Modern medical diagnostic tools like ECG and pulse oximetry have saved innumerable lives of infants around the world. However, infant mortality has not been adequately addressed by medical device manufacturers in impoverished countries. Respiratory ailments like pneumonia and bronchitis are a leading cause of infant deaths in such countries. The conventional medical care model, which allows for monitoring in primary healthcare centers, is often inaccessible to such communities. The advent of low cost open-source biosensing hardware like the OpenBCI coupled with deep learning models on edge devices like the raspberry pi opens a new avenue for delivering in-situ medical monitoring for a fraction of the cost of traditional diagnostic equipment.OMNI provides a deep learning algorithm to robustly extract the Heart Rate (HR) and Breathing Rate (BR) from a single lead ECG. Furthermore, the algorithm is realized on a Raspberry Pi on ECGs obtained real-time from the OpenBCI Ganglion board.
Access the code here.