Different techniques originated in information theory and tools from nonlinear systems theory have been applied to the analysis of electro-physiological time series. Several clinically relevant results have emerged from the use of concepts, such as entropy, chaos and complexity, in analyzing electrocardiograms and electroencephalographic (EEG) records. In this work, we develop a method based on permutation entropy (PE) to characterize EEG records from different stages in the treatment of a chronic epileptic patient. Our results show that the PE is useful for clearly quantifying the evolution of the patient along a certain lapse of time and allows visualizing in a very convenient way the effects of the pharmacotherapy.publishedVersionFil: Ma...
In nonlinear dynamics, and to a lesser extent in other fields, a widely used measure of complexity i...
Biomedical signals are measurable time series that describe a physiological state of a biological sy...
Measuring complexity from non-stationary time series provides an important clue to the understanding...
Different techniques originated in information theory and tools from nonlinear systems theory have b...
Different techniques originated in information theory and tools from nonlinear systems theory have b...
In the clinical electrophysiological practice, reading and comparing electroencephalographic (EEG) r...
In the clinical electrophysiological practice, reading and comparing electroencephalographic (EEG) r...
In the clinical electrophysiological practice, reading and comparing electroencephalographic (EEG) r...
A foundation of medical research is time series analysis—the behavior of variables of interest with ...
Objective: Our goal is to use existing and to propose new time-frequency entropy measures that objec...
Measuring complexity from noisy time series provides a crucial insight into the understanding and mo...
Measuring complexity from noisy time series provides a crucial insight into the understanding and mo...
Permutation entropy (PeEn) is a complexity measure that originated from dynamical systems theory. Sp...
Permutation entropy (PeEn) is a complexity measure that originated from dynamical systems theory. Sp...
Biomedical signals are measurable time series that describe a physiological state of a biological sy...
In nonlinear dynamics, and to a lesser extent in other fields, a widely used measure of complexity i...
Biomedical signals are measurable time series that describe a physiological state of a biological sy...
Measuring complexity from non-stationary time series provides an important clue to the understanding...
Different techniques originated in information theory and tools from nonlinear systems theory have b...
Different techniques originated in information theory and tools from nonlinear systems theory have b...
In the clinical electrophysiological practice, reading and comparing electroencephalographic (EEG) r...
In the clinical electrophysiological practice, reading and comparing electroencephalographic (EEG) r...
In the clinical electrophysiological practice, reading and comparing electroencephalographic (EEG) r...
A foundation of medical research is time series analysis—the behavior of variables of interest with ...
Objective: Our goal is to use existing and to propose new time-frequency entropy measures that objec...
Measuring complexity from noisy time series provides a crucial insight into the understanding and mo...
Measuring complexity from noisy time series provides a crucial insight into the understanding and mo...
Permutation entropy (PeEn) is a complexity measure that originated from dynamical systems theory. Sp...
Permutation entropy (PeEn) is a complexity measure that originated from dynamical systems theory. Sp...
Biomedical signals are measurable time series that describe a physiological state of a biological sy...
In nonlinear dynamics, and to a lesser extent in other fields, a widely used measure of complexity i...
Biomedical signals are measurable time series that describe a physiological state of a biological sy...
Measuring complexity from non-stationary time series provides an important clue to the understanding...