Analyzing brain signals of the patients suffering from the state of depression may lead to interesting observations in the signal parameters that is quite different from a normal control. The present study adopts two different methods: Time frequency domain and nonlinear method for the analysis of EEG signals acquired from depression patients and age and sex matched normal controls. The time fr...

Entropy estimation metrics have become a widely used method to identify subtle changes or hidden features in biomedical records. These methods have been more effective than conventional linear techniques in a number of signal classification applications, specially the healthy–pathological segmentation dichotomy. Nevertheless, a thorough characterization of these measures, namely, how to match m...

Multiscale entropy (MSE) analysis [1, 2] is a new method of measuring the complexity of finite length time series. This computational tool can be applied both to physical and physiologic data sets, and can be used with a variety of measures of entropy. We have developed and applied MSE for the analysis of physiologic time series, for which we prefer to estimate entropy using the sample entropy ...

The wakefulness state of healthy subjects tends to be an early drowsy state because of the prolonged times required for electroencephalography (EEG) measurements. In such cases, clinicians should consider accurate diagnosis of the wakefulness state of the subjects in order to interpret EEG signals accurately. The aim of the present study was to quantitatively evaluate the wakefulness state (ear...

True random number generators (TRNGs) are essential for cryptographic systems, and they are usually evaluated by the concept of entropy. In general, the entropy of a TRNG is estimated from its stochastic model, and reflected in the statistical results of the generated raw bits. Oscillator-based TRNGs are widely used in practical cryptographic systems due to its elegant structure, and its stocha...

Multiscale cross-approximate entropy (MC-ApEn) between two different physiological signals could evaluate cardiovascular health in diabetes. Whether MC-ApEn analysis between two similar signals such as photoplethysmographic (PPG) pulse amplitudes of bilateral fingertips can reflect diabetes status is unknown. From a middle-to-old-aged population free of prior cardiovascular disease, we selected...

Dong Liu, Miao Yu, Nan Sun, Ying Qi (School of Water Conservancy & Civil Engineering,Northeast Agricultural University, Harbin Heilongjiang 150030,China) Abstract:In the course of analyzing and forecasting regional groundwater depth, its variation process complexity was ignored in the past, so that it was difficult to realize scientific management of groundwater resources. Aiming at the aforesa...

Non-linear analysis of the electroencephalogram (EEG) background activity can help to obtain a better understanding of abnormal dynamics in the brain. The aim of this study was to analyze the regularity of the EEG time series of Alzheimer’s disease (AD) patients to test the hypothesis that the irregularity of the AD patients’ EEG is lower than that of age-matched controls. We recorded the EEG f...

Walking stability of human beings varies with age and reduces in the elderly people. Lesser walking stability causes many falls in the elderly people. In this paper, we have investigated the walking stability of young and elderly subjects using approximate entropy, a non-linear time series analysis method. Our emphasis in this paper is to investigate the walking stability using a portable instr...

In the present work we intend to investigate how to detect the behaviour of the immune system reaction to an external stimulus in terms of phase transitions. The immune model considered follows Jerne’s idiotypic network theory. We considered two graph complexity measures the connectivity entropy and the approximate von Neumann entropy and one entropy for topological spaces, the so-called persis...