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A digital stethoscope that auscultates and detects abnormalities in the respiratory system using tinyML at the edge.

As the 2nd wave of covid unfolds throughout the world, sustaining through the pandemic has become an everyday challenge. Due to the steep rise in cases, the healthcare industry continues to evolve and is adopting telemedicine to facilitate the accessibility of remote health care services. But using telemedicine we can only diagnose common diseases as no physical examination is possible, which may increase the number of wrong diagnoses. A quite common symptom of covid-19 in the Indian population is the development of respiratory abnormalities, for which Physical inspection is a mandatory process for proper diagnosis.

What if the stethoscope could reveal insights on our health condition just by listen and analyzing the lung sounds at the edge !? The initial inspiration for this project came from the “digital stethoscope” project by Peter Ma.

Proposed Solution
Hence the idea is to develop a solution that combines any normal stethoscope paired with a microphone and recording the sound of the respiratory system and performing audio classification at the edge on a microcontroller. In order to get accurate acoustic data, we have to make sure that the data to be processed is free of an anomaly due to power line interference, motion artifacts, etc. Hence we need to include a filtering system consisting of low pass, high pass filters along with an adjustable gain. The filtered signal is then pre-processed using the AudioMFE processing block which extracts time and frequency features from a signal and fed into the neural network.

The model then classifies the recording into one of the 4 classes: Ideal (Stethoscope not being used), Normal ( No abnormalities in lungs), covid ( Lungs sound closely matching to that of a covid patient), wheezing ( Symptom of chronic obstruction or breathing problems. The goal is to build with low-cost off-shelf components that are readily available in the market so that even a non-technical person could easily build this kit by himself. The ability to detect the critical health issue at your disposal beforehand in a lockdown situation is inestimable.”

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