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.

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.

Distinct sleep phases are related to different dynamical patterns in electroencephalogram (EEG) signals. In this article, the relationship between the sleep stages and nonlinear behavior of sleep EEG is explored. In particular, analysis of approximate entropy (ApEn) and the largest Lyapunov exponent is evaluated in patients with sleep apnea, which is defined as respiratory flow that is suspended or decreased for more than 10 s. The pathological sleep EEG signals for analysis were obtained from the MIT-BIH polysomnography database available online at the PhysioBank. The results show that for the both normal and apneic sleep epochs, ApEn decreased significantly as the sleep goes into deeper stages. Therefore, it indicated that as sleep becomes deeper, the brain function becomes less activated. Compared with normal sleep, the mean value of largest lyapunov exponents was also significantly lower than that of normal epochs during deep sleep stages. The results also show that the average largest lyapunov exponents of EEG signals increased in the REM state. Because during this stage of sleep, the cortex becomes more active and more neurons incorporate in the information processing. In conclusion, the nonlinear dynamical measures obtained from the nonlinear dynamical analysis such as the approximate entropy and largest lyapunov exponents can be useful for characterizing the physiological or pathological states of the brain.