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bioRxiv · 10.1101/080515

A Probabilistic Approach to the Detection and Diagnosis of Obstructive Sleep Apnea and Its Environmental Factors Using Chest Movement Analysis, Recursive Noise Reduction and Machine Learning Algorithms.

Ramnani, R.·Tripathi, A.
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Abstract

1In a novel approach to diagnose Obstructive Sleep Apnea, electronic components, such as an Arduino Mega, a Bluetooth Transceiver, an accelerometer, and a air-quality sensor, were put together to create a wearable that would detect the frequency of apnea events, detect and diagnose the disorder, and sound an alarm when necessary. A primary consideration was to make the mechanism accessible and affordable, and in doing so, lower the cases of Obstructive Sleep Apnea that go undiagnosed due to the cost and inconvenience associated with the traditional diagnosis method--a Polysomnogram. Bluetooth capability was an additional consideration so that the device would transmit data directly to an android smartphone, eliminating the need for an additional output mechanism. The total cost of the device, quite surprisingly, did not exceed $30, and therein rendered the device an accessible, affordable mechanism for diagnosis. Tests of the device on diagnosed patients yielded data consistent with the diagnosis, with a few false positives as a result of the excessive sensitivity of the sensors.

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Ramnani, R., Tripathi, A.. 2016-10-17. A Probabilistic Approach to the Detection and Diagnosis of Obstructive Sleep Apnea and Its Environmental Factors Using Chest Movement Analysis, Recursive Noise Reduction and Machine Learning Algorithms.. https://doi.org/10.1101/080515

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