LP were detected in a significant proportion of dialysis patients, probably related to underlying CAD with left ventricular dysfunction. Prolongation of fQRS after dialysis could be explained by the acute reduction in serum potassium levels.

With the aim of better understanding the dynamic changes in sympatho-vagal tone occurring during the night, human heart rate variability (HRV) during the various sleep stages was evaluated by means of autoregressive spectral analysis. Each recording consisted of an electroencephalogram, an electrooculogram, and electromyogram, and electrocardiogram, and a spirometry trace. All of the data were sampled and stored in digital form. Sleep was analysed visually, but HRV was analysed off-line by means of original software using Burg's algorithm to calculate the LF/HF ratio (LF: 0.04-0.12 Hz; HF: 0.15-0.35 Hz) for each sleep stage. Seven healthy subjects (four males; mean age 35 years) were enrolled in the study. Our findings show a progressive and significant reduction in the LF/HF ratio through sleep stages S1-S4, as a result of an increase in the HF component; this indicates the prevalence of parasympathetic activity during slow-wave sleep. During wakefulness, S1 and REM, the LF/HF values were similar and close to 1.

No abstract

A nonlinear analysis of the underlying dynamics of a biomedical time series is proposed by means of a multi-dimensional testing of nonlinear Markovian hypotheses in the observed time series. The observed dynamics of the original N-dimensional biomedical time series is tested against a hierarchy of null hypotheses corresponding to N-dimensional nonlinear Markov processes of increasing order, whose conditional probability densities are estimated using neural networks. For each of the N time series, a measure based on higher order cumulants quantifies the independence between the past of the N-dimensional time series, and its value r steps ahead. This cumulant-based measure is used as a discriminating statistic for testing the null hypotheses. Experiments performed on artificial and real world examples, including autoregressive models, noisy chaos, and nonchaotic nonlinear processes, show the effectiveness of the proposed approach in modeling multivariate systems, predicting multidimensional time series, and characterizing the structure of biological systems. Electroencephalogram (EEG) time series and heart rate variability trends are tested as biomedical signal examples.

A study of the 24-h heart rate variability's (HRV) hidden dynamic is performed hour by hour, in order to investigate the evolution of the nonlinear structure of the underlying nervous system. A hierarchy of null hypotheses of nonlinear Markov models with increasing order n is tested against the hidden dynamic of the HRV time series. The minimum accepted Markov order supplies information about the nonlinearity of the HRV's hidden dynamic and consequently of the underlying nervous system. The Markov model with minimum order is detected for each hour of the RR time series extracted from seven 24-h electrocardiogram records of patients in different pathophysiological conditions, some including ventricular tachycardia episodes. Heart rate, pNN30, and LF/HF index plots are reported to serve as a reference for the description of the patient's cardiovascular frame during each examined hour. The minimum Markov order shows to be a promising index for quantifying the average nonlinearity of the autonomic nervous system's activity.