title: A system for portable sleep apnea diagnosis using an embedded data capturing module.
abstract: Sleep apnea (SA) is a very common disease with serious health consequences, yet is very under-diagnosed, partially because of the high cost and limited accessibility of in-laboratory polysomnography (PSG). The purpose of this work is to introduce a newly developed portable system for the diagnosis of SA at home that is both reliable and easy to use. The system includes personal devices for recording breath sounds and airflow during sleep and diagnostic algorithms to process the recorded data. The data capturing device consists of a wearable face frame with an embedded electronic module featuring a unidirectional microphone, a differential microphone preamplifier, a microcontroller with an onboard differential analogue to digital converter, and a microSD memory card. The device provides continuous data capturing for 8 h. Upon completion of the recording session, the memory card is returned to a location for acoustic analysis. We recruited 49 subjects who used the device independently at home, after which each subject answered a usability questionnaire. Random data samples were selected to measure the signal-to-noise ratio (SNR) as a gauge of hardware functionality. A subset of 11 subjects used the device on 2 different nights and their results were compared to examine diagnostic reproducibility. Independent of those, system's performance was evaluated against PSG in the lab environment in 32 subject. The overall success rate of applying the device in un-attended settings was 94 % and the overall rating for ease-of-use was 'excellent'. Signal examination showed excellent capturing of breath sounds with an average SNR of 31.7 dB. Nine of the 11 (82 %) subjects had equivalent results on both nights, which is consistent with reported inter-night variability. The system showed 96 % correlation with simultaneously performed in-lab PSG.
  
  
  CONCLUSIONS
  Our results suggest excellent usability and performance of this system and provide a strong rationale to further improve it and test its robustness in a larger study.
authors: Sleep and Upper Airway Team & Technology Team, Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, Toronto, ON M5G 2A2, Canada. hisham.alshaer@uhn.ca
authors: Hisham
authors: H
authors: Alshaer
authors: Alexander
authors: A
authors: Levchenko
authors: T Douglas
authors: TD
authors: Bradley
authors: Steven
authors: S
authors: Pong
authors: Wen-Hou
authors: WH
authors: Tseng
authors: Geoff R
authors: GR
authors: Fernie
mesh_terms: D000162:Acoustics; D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation / Q000706:statistics & numerical data; D004867:Equipment Design; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000706:statistics & numerical data; D017286:Polysomnography / Q000295:instrumentation* / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D012135:Respiratory Sounds; D059629:Signal-To-Noise Ratio; D012891:Sleep Apnea Syndromes / Q000175:diagnosis*