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Sleep Apnea Detection Using Wearable Devices

Rahimi, Hamid Reza | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56395 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Fotowat Ahmady, Ali; Akbar, Fatemeh
  7. Abstract:
  8. Approximately 1.36 billion people worldwide suffer from sleep apnea, necessitating accurate diagnosis and treatment. Traditional sleep clinics rely on Polysomnography (PSG)-based monitoring devices. However, these devices are not only voluminous but also prohibitively expensive, requiring numerous sensors and wires that disrupt sleep and cause discomfort for patients. To address these challenges, we have developed a wireless wearable system. This system comprises three sensor blocks, each capable of capturing vital signs from the body, resulting in a total of eight signals. The first two sensor blocks capture signals from the body, which are subsequently modulated and transmitted to a user's smartphone via low-energy Bluetooth. Additionally, the third block records the user's voice during sleep using the smartphone's built-in microphone. Upon data transfer to MATLAB, each signal undergoes thorough processing to establish its relationship with vital signs. This comprehensive analysis ultimately leads to the diagnosis of sleep apnea and the determination of its severity through signal processing algorithms. Notably, the primary objective of this system was to detect the exact timing of apnea and hypopnea events. Impressively, the system exhibits a remarkable accuracy of 90.2% in pinpointing the precise occurrence times of these events. Furthermore, when employing sensor fusion, the system's accuracy in determining the timing of total apnea and hypopnea events substantially improves to 95.1%. Moreover, the Apnea-Hypopnea Index (AHI) boasts an outstanding accuracy level of approximately 98%, while the system consistently demonstrates an average reliability of about 93%
  9. Keywords:
  10. Sleep Disorder Breathing (SDB) ; Wearable Electronics ; Signal Processing ; Polysomnography (PSG) ; Sleep Apnea ; Sleep Monitoring

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