Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0037315 (sleep apnea)
 8,000 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Simple ambulatory monitoring methods can be used in step-by-step diagnosis of sleep apnoea syndrome to differentiate between high-risk and low-risk patients, or to exclude the syndrome for achieving more efficient utilisation of sleep laboratory facilities. The question was whether a new method using a thermistor sensor measuring respiration-conditioned thermal convection at the mouth and nose can reliably record the frequency of apnoea (ambulatory thermistor method = ATM). The study was subdivided into two sections. In phase I the respiratory impulses measured via ATM were polygraphically recorded in 20 patients simultaneously with conventional cardiopulmonary data of nocturnal polygraphy (heart rate, oxygen saturation, thorax and abdominal excursions and oronasal respiratory flow). 40 patients participated in phase II. During a first night the patients slept under ATM in their patient rooms. In the 2nd night nocturnal polygraphy was conducted with the parameters mentioned above; the results of both nights were then compared. Taking 35 phases of apnoea in one night as threshold or baseline value, a sensitivity of 100% and a specificity of 84.6% were attained in phase I, the simultaneous comparison of ATM and nocturnal polygraphy, in the recording of an enhanced nocturnal apnoea frequency by ATM. In phase 2 (1st night ATM, 2nd night polygraphy) ATM also yielded a sensitivity of 100% and a specificity of 76%. When measuring with a borderline value of 70 nocturnal phases of apnoea, ATM yielded a specificity and sensitivity of 100%, in phase 2 a sensitivity of 80.7% and a specificity of 88.5%.(ABSTRACT TRUNCATED AT 250 WORDS)
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PMID:[Ambulatory monitoring of patients with suspected sleep apnea syndrome using a thermistor sensor in comparison with nocturnal polysomnography]. 849 62

Obstructive sleep apnea (OSA) is a common sleep disorder that often remains undiagnosed, leading to an increased risk of developing cardiovascular diseases. Polysomnogram (PSG) is currently used as a golden standard for screening OSA. However, because it is time consuming, expensive and causes discomfort, alternative techniques based on a reduced set of physiological signals are proposed to solve this problem. This study proposes a convenient non-parametric kernel density-based approach for detection of OSA using single-lead electrocardiogram (ECG) recordings. Selected physiologically interpretable features are extracted from segmented RR intervals, which are obtained from ECG signals. These features are fed into the kernel density classifier to detect apnea event and bandwidths for density of each class (normal or apnea) are automatically chosen through an iterative bandwidth selection algorithm. To validate the proposed approach, RR intervals are extracted from ECG signals of 35 subjects obtained from a sleep apnea database ( http://physionet.org/cgi-bin/atm/ATM ). The results indicate that the kernel density classifier, with two features for apnea event detection, achieves a mean accuracy of 82.07 %, with mean sensitivity of 83.23 % and mean specificity of 80.24 %. Compared with other existing methods, the proposed kernel density approach achieves a comparably good performance but by using fewer features without significantly losing discriminant power, which indicates that it could be widely used for home-based screening or diagnosis of OSA.
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PMID:An obstructive sleep apnea detection approach using kernel density classification based on single-lead electrocardiogram. 2573 75