Data Science

Onera’s Bio-Impedance Patch detect sleep apnea by using machine learning efficiently

Clinical specialists at Onera Health have made a bioimpedance fix that utilizes AI to help analyze sleep apnea, a serious rest condition where breathing routinely starts and eases back, and may prompt cardiovascular infection, memory issue, and other wellbeing related issues. Onera’s patch could make it possible to diagnose sleep apnea in one’s own household, enabling more patients to be screened for long-term care. The bio-impedance patch, which is placed on the chest, uses a slight current at a specified frequency and monitors the resultant voltage as it travels through the body at a different spot.

How it works fro sleep apnea?

The observations are then processed using a two-phase LSTM (Long Short-Term Memory) deep learning method to identify sleep apnea cases. The bioimpedance patch is focused on a tool (known as Robin) from IMEC and Ghent University researchers who wondered whether it might be used to differentiate breathing habits from people that have sleep apnea. Researchers at Onera Health checked the tool using synchronized measurements and polysomnography (recording brain waves, breathing habits, pulse rate, etc.) of 25 patients and considered their method to be 73 percent effective in the identification of sleep disturbance.

Many products in the market use elastic bands or MEMS sensors to track breathing movements indirectly from outside the body, but Onera does so centrally inside the body, rendering it more accurate.

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