A system for portable sleep apnea diagnosis using an embedded data capturing module.

UNLABELLED: 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. CONCLUSION: 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.

Comparison of in-laboratory and home diagnosis of sleep apnea using a cordless portable acoustic device.

BACKGROUND AND OBJECTIVES: Sleep apnea (SA) is a common, serious, but underdiagnosed condition. There is a need for more accessible and economic means of diagnosing SA in the home. The aim of this study was to test the validity of a cordless acoustic portable device (BresoDx™) for home diagnosis of SA compared with standard polysomnography (PSG). METHODS: A total of 135 subjects underwent full overnight PSG and simultaneous recording of breath sounds by BresoDx in the sleep laboratory. Acoustic data extracted from BresoDx were analyzed using validated computer acoustic algorithms. The PSG-derived apnea-hypopnea index (AHI-p) and the acoustic AHI (AHI-a) were calculated and compared. A subset of 100 subjects used the device in a subsequent night in their home from which home AHI (AHI-h) was determined. RESULTS: The correlation between AHI-a and simultaneous AHI-p was 95.2% and diagnostic accuracy of BresoDx ranged between 88.9% and 93.3% around AHI cutoffs of 5-15. In the home, AHI-h did not differ significantly from AHI-p (p = 0.60). Using an AHI-p cutoff ≥ 10 BresoDx's accuracy was 81%. Of the 100 subjects, 81 (81%) had low inter-night variability measured by a difference between home AHI-h and PSG AHI-p < 10 event/h, while 19% had higher inter-night variability. CONCLUSION: AHI determined using BresoDx was in excellent agreement with simultaneous AHI-p. The majority of patients had a consistent AHI in their subsequent home study with very good overall diagnostic accuracy. We conclude that BresoDx is a reliable device for diagnosing SA that can be used by subjects, unattended in their own homes.

Classification of vibratory patterns of the upper airway during sleep.

Upper airway (UA) narrowing and collapse during sleep results in obstructive sleep apnea (OSA). We hypothesize that vibratory patterns of snoring can distinguish simple snorers from those with OSA. Samples of breath sounds were collected from 7 snorers without OSA and 5 with OSA. Snoring pitch (F0) contours were found using the robust algorithm for pitch tracking (RAPT). The OSA snoring contours showed fluctuating patterns as compared to the smoother patterns of simple snorers. This suggests that snoring reveals the underlying instabilities of UA tissue in OSA. Conditional random fields, a statistical sequence classifier, gave 75% accuracy in distinguishing the 2 groups.