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Sleep is characterized by dynamic coupling between central (CNS) and peripheral autonomic (ANS) nervous systems. However, further research is needed to better understand the multiple interactions occurring among electroencephalographic (EEG) features and respiratory and cardiovascular (CV) outputs modulated by the ANS during sleep. Here, we developed new methods to study EEG slow-wave activity (SWA) during non-rapid eye movement (NREM) sleep with respect to the phases of peripheral oscillations. EEG, respiration, and continuous blood pressure signals recorded from 20 participants were analyzed. Digital filters, designed to decompose the signals into different frequency bands, and the Hilbert transform were applied to estimate the instantaneous phases and frequencies of the peripheral oscillations. The peripheral oscillations were categorized into four phases representing up and down states. EEG delta power (synchronized SWA) was computed and compared across these phases during NREM sleep. Results show that EEG delta power is higher during down phases of slow and respiratory frequency components of blood pressure and during up phases of respiration, suggestive of CNS-ANS coupling during NREM sleep. The developed techniques provide the preliminary framework to further analyze and interpret complex interactions between cortical and cardiac oscillations and their synchrony.

Impedance cardiography (ICG) is a noninvasive technique for evaluation of cardiac hemodynamic parameters such as cardiac output and pre-ejection period. However, the sensitivity of the technique to motion artifact, electrode displacement, and cardiovascular pathologies can severely impact the accuracy of hemodynamic parameter estimates. In this paper, we proposed a new algorithm for the automatic detection and exclusion of corrupted ICG cardiac cycles by defining a pulse similarity index that quantifies the level of pulse corruption and its diversion from a typical-shaped pulse. The index considers different features (activity, structure, shape, and pattern) of the ICG cardiac cycles. The algorithm is compared on sleep data collected from 20 participants against expert identified corrupted cycles. The artifact rejection algorithm achieved a high accuracy of 96% in detection of expert-identified corrupted ICG cycles, including those with normal amplitude as well as out-of-range values, and was robust to different types and levels of artifact. The algorithm shows promise toward applications requiring accurate and reliable automatic measurement of cardiac hemodynamic parameters from prolonged data sets.

Hot flashes (HF) are intense, transient feelings of heat usually accompanied with flushed skin and sweating that are experienced by women around the time of menopause. HFs are associated with poor quality of life and increased cardiovascular risk. Automatic detection of HF occurrence and precise timing of HF onset could provide unique insight into the physiology of the HF and its effect on the cardiovascular system. A novel automatic algorithm is proposed for the detection of HFs occurrence and timing from the sternal skin conductance signal that is robust to noise and artifacts. The method is based on the gold standard rule (2μS rise in skin conductance within 30 s) and considers several conditions based on the skin conductance level and its derivative to reject unwanted events. ECG-derived heart rate pattern variations are studied prior to the detected HF onset. The algorithm is validated against expert detected HFs over 200 hours of sleep data collected from 12 perimenopausal women. It achieved a total accuracy of 93% and a total error of 3% in HF detection. It was observed that heart rate increased before the onset of 80% of the HFs occurring in undisturbed sleep. Application of this algorithm along with fusion of other simultaneously recorded physiological measures has the potential to advance understanding of the HF.