Cardiovascular coupling analysis with high-resolution joint symbolic dynamics in patients suffering from acute schizophrenia.

Besides the well-known cardiac risk factors for schizophrenia, increasing concerns have been raised regarding the cardiac side-effects of antipsychotic medications. A bivariate analysis of autonomic regulation, based on cardiovascular coupling, can provide additional information about heart rate (HR) and blood pressure regulatory patterns within the complex interactions of the cardiovascular system. We introduce a new high-resolution coupling analysis method (HRJSD) based on joint symbolic dynamics (JSD), which is characterized by three symbols, a threshold (individual dynamic variability, physiological) for time series transformation and eight coupling pattern families. This is based on a redundancy reduction strategy used to quantify and characterize cardiovascular couplings. In this study, short-term (30 min) HR and systolic blood pressure (SP) time series of 42 unmedicated (UNMED) and 42 medicated patients (MED) suffering from acute schizophrenia were analysed to establish the suitability of the new method for quantifying the effects of antipsychotics on cardiovascular couplings. We were able to demonstrate that HRJSD, applying the threshold based on spontaneous baroreflex sensitivity (BRS) estimation, revealed eight significant pattern families that were able to quantify the anti-cholinergic effects of antipsychotics and the related changes of cardiovascular regulation (coupling) in MED in comparison to UNMED. This was in contrast to the simple JSD, BRS (sequence method) and only partly to standard linear HR variability indices. HRJSD provides strong evidence that autonomic regulation in MED seems to be, to some extent, predominated by invariable HR responses in combination with alternating SP values in contrast to UNMED, indicating an impairment of the baroreflex control feedback loop in MED. Surrogate data analysis was applied to test for the significance and nonlinearity of cardiovascular couplings in the original data due to medical treatment with antipsychotic drugs in MED. In conclusion, the application of HRJSD revealed detailed information about short-term nonlinear cardiovascular couplings and cardiovascular physiological regulatory mechanisms (patterns) of autonomic function due to the anti-cholinergic effects of antipsychotics in patients with acute schizophrenia.

Auto-mutual information function of the EEG as a measure of depth of anesthesia.

Monitoring the depth of anesthesia (DOA) is necessary in order to decrease the incident of awareness in anesthesia and to prevent delays in the recovery phase. In the last decades a number of noninvasive methods have been proposed for the analysis of the electroencephalogram (EEG) for monitoring DOA. The objective of this work was to apply auto mutual information function (AMIF) to EEGs of patients under anesthesia in order to find variables able to characterize the following 4 states: awake, sedated, anesthetized and burst suppression episodes. The results show that the single and combined AMIF parameters were able to correctly classify the states in the range 72.2%-94.1% and 61.1%-100%, respectively.

Assessment of the depth of anesthesia based on symbolic dynamics of the EEG.

Methodologies based on symbolic dynamics have successfully demonstrated to reflect the nonlinear behavior of biological signals. In the present study, symbolic dynamics was applied to the electroencephalogram (EEG) in order to describe the level of depth of anesthesia. The EEG was transformed to symbol sequences. Words of three symbols were built from this symbolic series. The results obtained from the EEGs of 36 patients undergoing anesthesia showed that the probabilities of the word types were able to reflect the depth of anesthesia in a similar way to the auditory evoked potential index AAI, a commercial index.