Relationship between delta power and the electrocardiogram-derived cardiopulmonary spectrogram: possible implications for assessing the effectiveness of sleep.

Objectives: The physiologic relationship between slow-wave activity (SWA) (0-4 Hz) on the electroencephalogram (EEG) and high-frequency (0.1-0.4 Hz) cardiopulmonary coupling (CPC) derived from electrocardiogram (ECG) sleep spectrograms is not known.

Mapping sleep using coupled biological oscillations.

Sleep and wake state have different influences on a variety of recordable signals that make up the polysomnogram. Conventional sleep stages are dependent on analysis of electroencephalogram (EEG) waveforms. Non-EEG approaches can provide a different view of sleep.

Probabilistic sleep architecture models in patients with and without sleep apnea.

Sleep fragmentation of any cause is disruptive to the rejuvenating value of sleep. However, methods to quantify sleep architecture remain limited. We have previously shown that human sleep-wake stage distributions exhibit multi-exponential dynamics, which are fragmented by obstructive sleep apnea (OSA), suggesting that Markov models may be a useful method to quantify architecture in health and disease.

Effect of mild, asymptomatic obstructive sleep apnea on daytime heart rate variability and impedance cardiography measurements.

Dysregulation of autonomic nervous system dynamics is important in the pathophysiology of cardiovascular risk in obstructive sleep apnea (OSA). Heart rate variability (HRV) and impedance cardiography measures can estimate autonomic activity but have not gained traction clinically. The hypothesis of this study was that even in a cohort of patients with mild, asymptomatic OSA without overt cardiovascular disease, daytime HRV metrics and impedance cardiography measurements of preejection period would demonstrate increased sympathetic and decreased parasympathetic modulation compared to matched controls.

Classification algorithms for predicting sleepiness and sleep apnea severity.

Identifying predictors of subjective sleepiness and severity of sleep apnea are important yet challenging goals in sleep medicine. Classification algorithms may provide insights, especially when large data sets are available. We analyzed polysomnography and clinical features available from the Sleep Heart Health Study.