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Query: UMLS:C0004135 (ATM)
 13,001 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The importance of the cerebellum in sleep disorders, and vice versa, is only beginning to be understood. Advanced neuroimaging modalities have revealed cerebellar changes in both common and rare sleep disorders. Sleep disorders in those with genetic cerebellar disease, such as spinocerebellar ataxia, Friedreich ataxia, Joubert syndrome, and ataxia-telangiectasia, include excessive daytime sleepiness, restless legs syndrome, periodic limb movements of sleep, obstructive apnea, central apnea, and rapid eye movement behavior disorder. Sleep medicine is an important and under-recognized part of the neurologic evaluation in those with cerebellar disease.
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PMID:The cerebellum and sleep. 2543 87

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