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Objective:Cardiorespiratory interactions have been widely investigated in different physiological states and conditions. Various types of coupling characteristics have been observed in the cardiorespiratory system; however, it is difficult to identify and quantify details of their interaction. In this study, we investigate directional coupling of the cardiorespiratory system in different physiological states (sleep stages) and conditions, i.e., severity of obstructive sleep apnea (OSA).Methods:Directionality analysis is performed using the evolution map approach with heartbeats acquired from electrocardiogram and abdominal respiratory effort measured from the polysomnographic data of 39 healthy individuals and 24 mild, 21 moderate, and 23 severe patients with OSA. The mean phase coherence is used to confirm the weak and strong coupling of cardiorespiratory system.Results:We find that unidirectional coupling from the respiratory to the cardiac system increases during wakefulness (average value of −0.61) and rapid eye movement sleep (−0.55). Furthermore, unidirectional coupling between the two systems significantly decreases during light (−0.52) and deep sleep, which is further decreased in deep sleep (−0.46), approaching bidirectional coupling. In addition, unidirectional coupling from the respiratory to the cardiac system also significantly increases according to the severity of OSA.Conclusion:These coupling characteristics in different states and conditions are believed to be linked with autonomic nervous modulation.Significance:Our approach could provide an opportunity to understand how integrated systems cooperate for physiological functions under internal and external environmental changes, and how abnormality in one physiological system could develop to increase the risk of other systemic dysfunctions and/or disorders. [ABSTRACT FROM AUTHOR]

Objective: Filtering power line interference (PLI) from electrocardiogram (ECG) recordings can lead to significant distortions of the ECG and mask clinically relevant features in ECG waveform morphology. The objective of this study is to filter PLI from ECG recordings with minimal distortion of the ECG waveform. Methods : In this paper, we propose a fixed-lag Kalman smoother with adaptive noise estimation. The performance of this Kalman smoother in filtering PLI is compared to that of a fixed-bandwidth notch filter and several adaptive PLI filters that have been proposed in the literature. To evaluate the performance, we corrupted clean neonatal ECG recordings with various simulated PLI. Furthermore, examples are shown of filtering real PLI from an adult and a fetal ECG recording. Results: The fixed-lag Kalman smoother outperforms other PLI filters in terms of step response settling time (improvements that range from 0.1 to 1 s) and signal-to-noise ratio (improvements that range from 17 to 23 dB). Our fixed-lag Kalman smoother can be used for semi real-time applications with a limited delay of 0.4 s. Conclusion and Significance: The fixed-lag Kalman smoother presented in this study outperforms other methods for filtering PLI and leads to minimal distortion of the ECG waveform. [ABSTRACT FROM PUBLISHER]

In magnetoencephalography (MEG) and electroencephalography (EEG), independent component analysis is widely applied to separate brain signals from artifact components. A number of different methods have been proposed for the automatic or semiautomatic identification of artifact components. Most of the proposed methods are based on amplitude statistics of the decomposed MEGIEEG signal. We present a fully automated approach based on amplitude and phase statistics of decomposed MEG signals for the isolation of biological artifacts such as ocular, muscle, and cardiac artifacts (CAs). The performance of different artifact identification measures was investigated. In particular, we show that phase statistics is a robust and highly sensitive measure to identify strong and weak components that can be attributed to cardiac activity, whereas a combination of different measures is needed for the identification of artifacts caused by ocular and muscle activity. With the introduction of a rejection performance parameter, we are able to quantify the rejection quality for eye blinks and CAs. We demonstrate in a set of MEG data the good performance of the fully automated procedure for the removal of cardiac, ocular, and muscle artifacts. The new approach allows routine application to clinical measurements with small effect on the brain signal. [ABSTRACT FROM AUTHOR]