Sleep apnea is a serious disorder in which breathing stops during sleep, which causes fatigue, heart disease, cognitive impairment, or even death. Shockingly, 80% of all people with sleep apnea are left undiagnosed. In response, we developed Sleep Apnea Guardian to offer an affordable and non-invasive solution to detect sleep apnea. We used Brainflow to obtain EEG signals from the Muse 2 BCI headset, numpy to preprocess the data, matplotlib to graph the data, and PyQt5 to create an intuitive GUI. To detect sleep apnea in a patient’s brain signals, we convert the EEG signal into the frequency domain using Fast Fourier Transformation, and use a bandstop filter around 60Hz to remove interference from power lines. We then split it into 5 bands: delta (0.5 - 4Hz), theta (4 - 8Hz), alpha (8 - 13Hz) and beta ( > 13Hz). The most significant indicator for sleep apnea is the rapid decrease in the ratio between the delta and beta bands, denoted ∂-β. Sleep Apnea Guardian can monitor ∂-β ratio changes during sleep and provide instant feedback. We also record all the apnea events throughout the night to create a detailed sleep quality report in the morning. With accessibility in mind, we developed a voice assistant using speech recognition to enable full voice control and customization. Affordable, non-invasive, and user-friendly, Sleep Apnea Guardian has great potential to serve high risk populations, relieve stress on the healthcare system, and help everyone get better sleep.
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