Med Biol Eng Comput - Sleep apnea monitoring and diagnosis based on pulse oximetry and tracheal sound signals.

Tópicos

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Resumo

Sleep apnea is a common respiratory disorder during sleep, which is described as a cessation of airflow to the lungs that lasts at least for 10 s and is associated with at least 4% drop in blood's oxygen saturation level (S(a)O(2)). The current gold standard method for sleep apnea assessment is full-night polysomnography (PSG). However, its high cost, inconvenience for patients, and immobility have persuaded researchers to seek simple and portable devices to detect sleep apnea. In this article, we report on developing a new method for sleep apnea detection and monitoring, which only requires two data channels: tracheal breathing sounds and the pulse oximetry (S(a)O(2) signal). It includes an automated method that uses the energy of breathing sounds signals to segment the signals into sound and silent segments. Then, the sound segments are classified into breath, snore, and noise segments. The S(a)O(2) signal is analyzed automatically to find its rises and drops. Finally, a weighted average of different features extracted from breath segments, snore segments and S(a)O(2) signal are used to detect apnea and hypopnea events. The performance of the proposed approach was evaluated on the data of 66 patients recorded simultaneously with their full-night PSG study, and the results were compared with those of the PSG. The results show high correlation (0.96, P < 0.0001) between the outcomes of our system and those of the PSG. Also, the proposed method has been found to have sensitivity and specificity values of more than 91% in differentiating simple snorers from obstructive sleep apnea patients.

Resumo Limpo

sleep apnea common respiratori disord sleep describ cessat airflow lung last least s associ least drop blood oxygen satur level sao current gold standard method sleep apnea assess fullnight polysomnographi psg howev high cost inconveni patient immobl persuad research seek simpl portabl devic detect sleep apnea articl report develop new method sleep apnea detect monitor requir two data channel tracheal breath sound puls oximetri sao signal includ autom method use energi breath sound signal segment signal sound silent segment sound segment classifi breath snore nois segment sao signal analyz automat find rise drop final weight averag differ featur extract breath segment snore segment sao signal use detect apnea hypopnea event perform propos approach evalu data patient record simultan fullnight psg studi result compar psg result show high correl p outcom system psg also propos method found sensit specif valu differenti simpl snorer obstruct sleep apnea patient

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