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Query: UMLS:C0037315 (sleep apnea)
 8,000 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Periodic increases in blood pressure (BP) can occur in the sleep apnea syndrome (SAS) during recurrent apneas. To investigate the mechanisms causing this periodic hypertension, we simulated SAS by imposing a matching breathing pattern on seven healthy awake male volunteers. Continuous finger arterial BP, electrocardiogram, arterial O2 saturation (SaO2), end-tidal CO2, and tidal volume were measured. The role of hypoxia was studied by comparing apneas during depletion of O2 in the spirometer with those during 100% O2 breathing. In all subjects, BP periodically reached values greater than 150/95 mmHg in the hypoxic series. During the hyperoxic apnea series, however, BP remained stable. End-apneic mean BP was shown to be inversely correlated to SaO2 in six subjects in the SaO2 range from 60 to 100%. Although the hypoxic BP pattern closely mimicked that in SAS, the heart rate pattern in four of our subjects remained distinct from that in patients. Atropine could not prevent large BP swings in the hypoxic series. We conclude that SaO2 is a major determinant of periodic hypertension in recurrent apneas. Its effect probably results from chemoreflex modulation of peripheral resistance.
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PMID:Repetitive apneas induce periodic hypertension in normal subjects through hypoxia. 156 78

This paper examines the feasibility of accurate state classification of autonomic nervous activity (ANA) based on the power spectral pattern of the heart rate fluctuations (HRFs). Some attempts have been made to utilize artificial neural networks (ANNs) to classify HRFs for clinical diagnoses such as ischemic cardiomyopathy, arrhythmia or sleep apnea. To establish the firm bases for making such clinical diagnoses, it may be important to examine the classification accuracy for the data in physiologically well defined conditions by e.g. application of autonomic blocking agents. In this paper the three layered perceptron has been trained by the heart rate data in variety of ANS states yielded by the application of Atropine and Propranolol to 14 healthy male subjects. Six state (control, atropine and propranolol for each of the spine and upright posture) classification based on power spectrum showed average sensitivity of 67.2% and specificity 91.2%. Four state (control, atropine, propranolol and double block for either spine or upright posture) resulted in the average classification sensitivity of 75.7% and specificity 95.5%. The paper revealed that entropy bandwidth and indices originated from characteristic oscillations of blood pressure change improve the classification accuracy.
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PMID:State classification of heart rate variability by an artificial neural network in frequency domain. 2109 42