Application of Permutation Entropy and Permutation Min-Entropy in Multiple Emotional States Analysis of RRI Time Series

This study’s aim was to apply permutation entropy (PE) and permutation min-entropy (PME) over an RR interval time series to quantify the changes in cardiac activity among multiple emotional states. Electrocardiogram (ECG) signals were recorded under six emotional states (neutral, happiness, sadness, anger, fear, and disgust) in 60 healthy subjects at a rate of 1000 Hz. For each emotional state, ECGs were recorded for 5 min and the RR interval time series was extracted from these ECGs. The obtained results confirm that PE and PME increase significantly during the emotional states of happiness, sadness, anger, and disgust. Both symbolic quantifiers also increase but not in a significant way for the emotional state of fear. Moreover, it is found that PME is more sensitive than PE for discriminating non-neutral from neutral emotional states.

[1]  O. Rioul,et al.  Wavelets and signal processing , 1991, IEEE Signal Processing Magazine.

[2]  A. Porta,et al.  Progressive decrease of heart period variability entropy-based complexity during graded head-up tilt. , 2007, Journal of applied physiology.

[3]  José Manuel Pastor,et al.  Symbolic Analysis of Brain Dynamics Detects Negative Stress , 2017, Entropy.

[4]  F. B. Reguig,et al.  Emotion recognition from physiological signals , 2011, Journal of medical engineering & technology.

[5]  Matthias Weippert,et al.  Sample Entropy and Traditional Measures of Heart Rate Dynamics Reveal Different Modes of Cardiovascular Control During Low Intensity Exercise , 2014, Entropy.

[6]  M. Arif,et al.  Multiscale Permutation Entropy of Physiological Time Series , 2005, 2005 Pakistan Section Multitopic Conference.

[7]  K. H. Kim,et al.  Emotion recognition system using short-term monitoring of physiological signals , 2004, Medical and Biological Engineering and Computing.

[8]  Luciano Zunino,et al.  Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions , 2017 .

[9]  O. Rosso,et al.  Permutation min-entropy: An improved quantifier for unveiling subtle temporal correlations , 2015 .

[10]  Arcangelo Pellegrino,et al.  Studying the influence of cognitive load on driver's performances by a Fuzzy analysis of Lane Keeping in a drive simulation. , 2013 .

[11]  Hyo Jong Lee,et al.  A novel feature extraction method based on late positive potential for emotion recognition in human brain signal patterns , 2016, Comput. Electr. Eng..

[12]  Chuan-Yu Chang,et al.  Physiological emotion analysis using support vector regression , 2013, Neurocomputing.

[13]  Atefeh Goshvarpour,et al.  Indices from lagged poincare plots of heart rate variability: an efficient nonlinear tool for emotion discrimination , 2017, Australasian Physical & Engineering Sciences in Medicine.

[14]  A. Porta,et al.  K-nearest-neighbor conditional entropy approach for the assessment of the short-term complexity of cardiovascular control , 2013, Physiological measurement.

[15]  Enzo Pasquale Scilingo,et al.  The Role of Nonlinear Dynamics in Affective Valence and Arousal Recognition , 2012, IEEE Transactions on Affective Computing.

[16]  Byoung-Jun Park,et al.  Analysis of physiological signals for recognition of boredom, pain, and surprise emotions , 2015, Journal of Physiological Anthropology.

[17]  N. Nicolaou,et al.  The Use of Permutation Entropy to Characterize Sleep Electroencephalograms , 2011, Clinical EEG and neuroscience.

[18]  E. Kochs,et al.  Linear and non-linear heart rate metrics for the assessment of anaesthetists' workload during general anaesthesia. , 2016, British journal of anaesthesia.

[19]  Arcangelo Pellegrino,et al.  Entropic Measure of Epistemic Uncertainties in Multibody System Models by Axiomatic Design , 2017, Entropy.

[20]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[21]  E. Scilingo,et al.  Dominant Lyapunov exponent and approximate entropy in heart rate variability during emotional visual elicitation , 2012, Front. Neuroeng..

[22]  Jing Li,et al.  Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures , 2014, Entropy.

[23]  Rafael A. Calvo,et al.  Detecting Naturalistic Expressions of Nonbasic Affect Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.

[24]  Danuta Makowiec,et al.  Entropy Measures in the Assessment of Heart Rate Variability in Patients with Cardiodepressive Vasovagal Syncope , 2015, Entropy.

[25]  Danuta Makowiec,et al.  Ordinal pattern statistics for the assessment of heart rate variability , 2013 .

[26]  Chien-Hung Lin,et al.  Heart Rate Variability Signal Features for Emotion Recognition by Using Principal Component Analysis and Support Vectors Machine , 2016, 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE).

[27]  Guang-yuan Liu,et al.  Feature Extraction, Feature Selection and Classification from Electrocardiography to Emotions , 2009, 2009 International Conference on Computational Intelligence and Natural Computing.

[28]  Arcangelo Pellegrino,et al.  Evaluation of Uncertainties in the Design Process of Complex Mechanical Systems , 2017, Entropy.

[29]  Luca Citi,et al.  Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics , 2014, Scientific Reports.

[30]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[31]  Francesco Villecco,et al.  Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis , 2017, Entropy.

[32]  Feng Wang,et al.  An emotional analysis method based on heart rate variability , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.

[33]  Francesco Carlo Morabito,et al.  Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer's Disease EEG , 2012, Entropy.