OBJECTIVE In certain physiologic systems, disease is associated with a loss of complexity in system's output. We test the hypothesis that, in critically ill patients, there is an inverse relation between the complexity of the temperature curve and the clinical status. We also consider whether complexity analysis of the temperature curve may have prognostic value. DESIGN Prospective, observati...

We study the notion of approximate entropy within the framework of network theory. Approximate entropy is an uncertainty measure originally proposed in the context of dynamical systems and time series. We first define a purely structural entropy obtained by computing the approximate entropy of the so-called slide sequence. This is a surrogate of the degree sequence and it is suggested by the fr...

Introduction Non-linear analysis techniques have been reported as a way of characterizing Alzheimer’s disease (AD) from electroencephalograms (EEG), which could be utilised for early and more accurate detection of the disease. Among them, entropy has been used increasingly for EEG studies since the introduction of Approximate Entropy (ApEn) and Sample Entropy (SampEn). A newly developed entropy...

The aim of the present study is to show the utility of non-linear methods to analyse the electroencephalogram (EEG) and magnetoencephalogram (MEG) in patients with Alzheimer’s disease (AD). The following non-linear methods have been applied to study the EEG and MEG background activity in AD patients and control subjects: approximate entropy, sample entropy, multiscale entropy, auto-mutual infor...

Heart Rate Variability (HRV) is defined as the variations between consecutive instantaneous heart rates that occur in the heart as a consequence of a complex internal dynamic balance. Nonlinear analysis of HRV is helpful to assess the cardiac health noninvasively. Approximate Entropy and Sample Entropy are mathematical algorithms to measure the predictability or repeatability with in a time ser...

The application of non-linear metrics to physiological signals is a valuable tool because ‘‘hidden information’’ related to underlying mechanisms can be obtained. In this respect, approximate entropy (ApEn) is themost popular non-linear regularity index that has been applied to physiological time series. However, ApEn presents some shortcomings, such as bias, relative inconsistency and dependen...

BACKGROUND Approximate entropy, a measure of signal complexity and regularity, quantifies electroencephalogram changes during anesthesia. With increasing doses of anesthetics, burst-suppression patterns occur. Because of the high-frequency bursts, spectrally based parameters such as median electroencephalogram frequency and spectral edge frequency 95 do not decrease, incorrectly suggesting ligh...