Physiological signal processing techniques are commonly used in emotion recognition. Heart rate variability (HRV) is an important tool in disease diagnosis and psychological investigations. Because of the chaotic nature of HRV, customary methods may not be proficient. Taking the advantage of geometrically based algorithms can lead to the uncomplicated and better representation of heart rate dynamics. The aim of this study was to test whether a simple HRV measure, based on triangle phase space mapping and polynomial fitting, provides a useful emotion recognition technique. HRV of women (n = 12) aged 19-25 years were compared to that of 12 matched aged men, while subjects were induced by four emotional stimuli: happy, sad, afraid, and relax. Kruskal-Wallis test was applied to show the level of significance of the features. The results confirm that emotional responses to sad, afraid and relax stimuli can be differentiated by the proposed indices. In addition, they are significantly different in both genders' physiological reactions. It seems that the suggested simple quantifiers are most promising in offering new insight into the dynamics assessments of HRV signals in different emotional states.

The human brain is one of the most complex physiological systems. Therefore, electroencephalogram (EEG) signal modeling is important to achieve a better understanding of the physical mechanisms generating these signals. The aim of this study is to investigate the application of Kalman filter and the state space model for estimation of electroencephalogram signals in a specific pathological state. For this purpose, two types of EEG signals (normal and partial epilepsy) were analyzed. The estimation performance of the proposed method on EEG signals is evaluated using the root mean square (RMS) measurement. The result of the present study shows that this model is appropriate for the analysis of EEG recordings. In fact, this model is capable of predicting changes in EEG time series with phenomena such as epileptic spikes and seizures.

Scaling behavior is an indicator of the lack of characteristic time scale, and the existence of long-range correlations related to physiological constancy preservation. To investigate the fluctuations of the sleep electroencephalogram (EEG) over various time scales during different sleep stages detrended fluctuation analysis (DFA) is studied. The sleep EEG signals for analysis were obtained from the Sleep-EDF Database available online at the PhysioBank. The DFA computations were performed in different sleep stages. The scaling behavior of these time series was investigated with detrended fluctuation analysis (window size: 50 to 500). The results show that the mean values of scaling exponents were lower in subjects during stage 4 and standard deviation of scaling exponents of stage 4 was larger than that of the other stages. In contrast, the mean value of scaling exponents of stage 2 was larger, while a small variation of scaling exponent is observed at this stage. Therefore, DFA has a more stable behavior in stage 2, whereas the random variability and unpredictable behavior of DFA can be observed in the stage 4. In conclusion, scaling exponent indices are efficacious in quantifying EEG signals in different sleep stages.

The objective of this article was to study the effects of Chi meditation on heart rate variability (HRV). For this purpose, the statistical and spectral measures of HRV from the RR intervals were analyzed. In addition, it is concerned with finding adequate Auto-Regressive Moving Average (ARMA) model orders for spectral analysis of the time series formed from RR intervals. Therefore, Akaike's Final Prediction Error (FPE) was taken as the base for choosing the model order. The results showed that overall the model order chosen most frequently for FPE was p = 8 for before meditation and p = 5 for during meditation. The results suggested that variety of orders in HRV models upon different psychological states could be due to some differences in intrinsic properties of the system.

Meditation refers to a family of self-regulation practices that focus on training attention and awareness in order to bring mental processes under greater voluntary control. The aim of this study is to evaluate the efficiency of two different classifiers, k-Nearest Neighbor (k-NN) and Radial Basis Function (RBF), on the heart rate signals in a specific psychological state. For this purpose, two types of heart rate time series (before, and during meditation) of 25 healthy women are collected in the meditation clinic in Mashhad. The spectral parameters are used to gain insight into the autonomic nervous system (ANS) response induced by meditation. Therefore, very low frequency, low frequency, high frequency, the LF/HF ratio and frequency of the highest spectral peak of heart rate signals are extracted and used as inputs of the classifiers. To evaluate performance of the classifiers, the classification accuracies and mean square error (MSE) of the classifiers were examined. The classification results of this study denote that the RBF classifier trained on spectral features obtains higher accuracy than that of k-NN classifier. The total classification accuracy of the RBF classifier is 92.3% with 0.026 classification error. However, k-Nearest Neighbor classifier gives encouraging results (86.5%). Experimental results verify that radial basis function is an efficient classifier for classifying heart rate signals in a specific psychological state.

Nonlinear dynamics has been introduced to the analysis of biological data and increasingly recognized to be functionally relevant. The aim of this study is to evaluate nonlinear and chaotic dynamics of gait signals. For this purpose, we analyzed gait data in ten healthy subjects who walked for an hour at their usual, slow and fast paces. Poincare plots, Hurst Exponents and the Lyapunov Exponents of gait signals were calculated. The results show that the Hurst Exponents are significantly increased during slow and fast paces. For all subjects, the Lyapunov Exponents are increased during normal gait, which indicates that signals are more chaotic. This can be due to decreased nonlinear interaction of variables in slow and fast paces. The finite values of Hurst Exponents and positive values of Lyapunov Exponents suggest that all of gait signals have low dimensional chaos. In addition, the complexity of signals is decreased during slow and fast gait. Results are useful for the early diagnosis of common gait pathologies.

Meditation is a practice of concentrated focus upon the breath in order to still the mind. In this paper we have investigated an algorithm to classify rest and meditation, by processing of electroencephalogram (EEG) signals through the Wavelet and nonlinear methods. For this purpose, two types of EEG time series (before, and during meditation) of 25 healthy women are collected in the meditation clinic in Mashhad. Correlation dimension and Wavelet coefficients at the forth decomposition level of EEG signals in Fz, Cz and Pz are extracted and used as an input of different classifiers. In order to evaluate performance of the classifiers, the classification accuracies and mean square error (MSE) of the classifiers were examined. The results show that the Fisher discriminant and Parzen classifier trained on both composite features obtain higher accuracy than that of the others. The total classification accuracy of the Fisher discriminant and Parzen classifier applying Wavelet coefficients was 85.02% and 84.75%, respectively which is raised to 92.37% in both classifiers using Correlation dimensions.

Nonlinear dynamics has been introduced to the analysis of biological data and increasingly recognized to be functionally relevant. The aim of this study is to quantify and compare the contribution of nonlinear and chaotic dynamics of human heart rate variability during two forms of meditation: (i) Chinese Chi (or Qigong) meditation and (ii) Kundalini Yoga meditation. For this purpose, Poincare plots, Lyapunov exponents and Hurst exponents of heart rate variability signals were analyzed. In this study, we examined the different behavior of heart rate signals during two specific meditation techniques. The results show that heart rate signals became more periodic and their chaotic behavior was decreased in both techniques of meditation. Therefore, nonlinear chaotic indices may serve as a quantitative measure for psychophysiological states such as meditation. In addition, different forms of meditation appear to differentially alter specific components of heart rate signals.

The current study analyses the dynamics of the heart rate signals during specific psychological states in order to obtain a detailed understanding of the heart rate patterns during meditation. In the proposed approach, heart rate time series available in Physionet database are used. The dynamics of the signals are then analyzed before and during meditation by examining the attractors in the phase space and recurrence quantification analysis. In general, the results reveal that the heart rate signals transit from a chaotic, highly-complex behavior before meditation to a low dimensional chaotic (and quasi-periodic) motion during meditation. This can be due to decreased nonlinear interaction of variables in meditation states and may be related to increased parasympathetic activity and increase of relaxation state. The results suggest that nonlinear chaotic indices may serve as a quantitative measure for psychophysiological states.

Meditation is commonly perceived as an alternative medicine method of psychological diseases management tool that assist in alleviating depression and anxiety disorders. The purpose of this study is to evaluate the accuracy of different classifiers on the heart rate signals in a specific psychological state. Two types of heart rate time series (before, and during meditation) of 25 healthy women are collected in the meditation clinic in Mashhad. Nonlinear features such as Lyapunov Exponents and Entropy were extracted. To evaluate performance of the classifiers, the classification accuracies and mean square error (MSE) of the classifiers were examined. Different classifiers were tested and the studies confirmed that for the heart rate signals, Quadratic classifier trained on Lyapunov Exponents and Entropy results in higher classification accuracy. The classification accuracy of the Quadratic classifier is 92.31%. However, the accuracies of Fisher and k-Nearest Neighbor (k-NN) classifiers are encouraging. The classification results demonstrate that the dynamical measures are useful parameters which contain comprehensive information about signals and the Quadratic classifier using nonlinear features can be useful in analyzing the heart rate signals in a specific psychological state.