Combination of Empirical Mode Decomposition Components of HRV Signals for Discriminating Emotional States

Introduction Automatic human emotion recognition is one of the most interesting topics in the field of affective computing. However, development of a reliable approach with a reasonable recognition rate is a challenging task. The main objective of the present study was to propose a robust method for discrimination of emotional responses thorough examination of heart rate variability (HRV). In the present study, considering the non-stationary and non-linear characteristics of HRV, empirical mode decomposition technique was utilized as a feature extraction approach. Materials and Methods In order to induce the emotional states, images indicating four emotional states, i.e., happiness, peacefulness, sadness, and fearfulness were presented. Simultaneously, HRV was recorded in 47 college students. The signals were decomposed into some intrinsic mode functions (IMFs). For each IMF and different IMF combinations, 17 standard and non-linear parameters were extracted. Wilcoxon test was conducted to assess the difference between IMF parameters in different emotional states. Afterwards, a probabilistic neural network was used to classify the features into emotional classes. Results Based on the findings, maximum classification rates were achieved when all IMFs were fed into the classifier. Under such circumstances, the proposed algorithm could discriminate the affective states with sensitivity, specificity, and correct classification rate of 99.01%, 100%, and 99.09%, respectively. In contrast, the lowest discrimination rates were attained by IMF1 frequency and its combinations. Conclusion The high performance of the present approach indicated that the proposed method is applicable for automatic emotion recognition.

[1]  Sazali Yaacob,et al.  Emotion Detection from QRS Complex of ECG Signals Using Hurst Exponent for Different Age Groups , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[2]  Zhizhong Wang,et al.  Mean frequency derived via Hilbert-Huang transform with application to fatigue EMG signal analysis , 2006, Comput. Methods Programs Biomed..

[3]  Enzo Pasquale Scilingo,et al.  Recognizing Emotions Induced by Affective Sounds through Heart Rate Variability , 2015, IEEE Transactions on Affective Computing.

[4]  M. Murugappan,et al.  Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst , 2013, BioMedical Engineering OnLine.

[5]  Woontack Woo,et al.  Physiological Sensing and Feature Extraction for Emotion Recognition by Exploiting Acupuncture Spots , 2005, ACII.

[6]  Jason Williams,et al.  Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System , 2004, ADS.

[7]  Ram Bilas Pachori,et al.  Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition , 2011, Comput. Methods Programs Biomed..

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

[9]  M Matteucci,et al.  Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis , 2010, Physiological measurement.

[10]  Ying Sun,et al.  Assessment of Chaotic Parameters in Nonstationary Electrocardiograms by Use of Empirical Mode Decomposition , 2004, Annals of Biomedical Engineering.

[11]  Ki H. Chon,et al.  Automatic Motion and Noise Artifact Detection in Holter ECG Data Using Empirical Mode Decomposition and Statistical Approaches , 2012, IEEE Transactions on Biomedical Engineering.

[12]  Mohamed Chetouani,et al.  Hilbert-Huang transform based physiological signals analysis for emotion recognition , 2009, 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[13]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[14]  E P Souza Neto,et al.  Assessment of Cardiovascular Autonomic Control by the Empirical Mode Decomposition , 2004, Methods of Information in Medicine.

[15]  Lan Li,et al.  Emotion Recognition Using Physiological Signals from Multiple Subjects , 2006, 2006 International Conference on Intelligent Information Hiding and Multimedia.

[16]  Paulo Gonçalves,et al.  Empirical Mode Decompositions as Data-Driven Wavelet-like Expansions , 2004, Int. J. Wavelets Multiresolution Inf. Process..

[17]  Changchun Liu,et al.  An empirical study of machine learning techniques for affect recognition in human–robot interaction , 2006, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Atefeh Goshvarpour,et al.  Affective Visual Stimuli: Characterization of the Picture Sequences Impacts by Means of Nonlinear Approaches , 2015, Basic and clinical neuroscience.

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

[20]  A. Barreto,et al.  Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  Kang-Ming Chang,et al.  Gaussian Noise Filtering from ECG by Wiener Filter and Ensemble Empirical Mode Decomposition , 2011, J. Signal Process. Syst..

[22]  Sun Kook Yoo,et al.  Neural Network Based Emotion Estimation Using Heart Rate Variability and Skin Resistance , 2005, ICNC.

[23]  M. O. A. Omar,et al.  Application of the empirical mode decomposition to ECG and HRV signals for congestive heart failure classification , 2011, 2011 1st Middle East Conference on Biomedical Engineering.

[24]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

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

[26]  A. Damasio,et al.  Basic emotions are associated with distinct patterns of cardiorespiratory activity. , 2006, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[27]  N. Izeboudjen,et al.  APPLICATION OF A PROBABILISTIC NEURAL NETWORK FOR CLASSIFICATION OF CARDIAC ARRHYTHMIAS , 2009 .

[28]  Ching-Haur Chang,et al.  Denoise of ECG Based on Weighted Sum of Intrinsic Mode Functions , 2013, 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[29]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Yodchanan Wongsawat,et al.  The preliminary study of EEG and ECG for epileptic seizure prediction based on Hilbert Huang Transform , 2014, The 7th 2014 Biomedical Engineering International Conference.

[31]  Christos D. Katsis,et al.  Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[32]  Qiang Chen,et al.  Research on genetic algorithm based on emotion recognition using physiological signals , 2011, 2011 International Conference on Computational Problem-Solving (ICCP).

[33]  Ya Xu,et al.  A Method of Emotion Recognition Based on ECG Signal , 2009, 2009 International Conference on Computational Intelligence and Natural Computing.

[34]  Pablo F. Diez,et al.  Application of the empirical mode decomposition to the extraction of features from EEG signals for mental task classification , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[35]  Sazali Yaacob,et al.  Electrocardiogram‐based emotion recognition system using empirical mode decomposition and discrete Fourier transform , 2014, Expert Syst. J. Knowl. Eng..

[36]  Christos D. Katsis,et al.  An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders , 2011, Biomed. Signal Process. Control..

[37]  Georgios N. Yannakakis,et al.  Entertainment modeling through physiology in physical play , 2008, Int. J. Hum. Comput. Stud..

[38]  P. Lang,et al.  International Affective Picture System (IAPS): Instruction Manual and Affective Ratings (Tech. Rep. No. A-4) , 1999 .

[39]  Maysam F. Abbod,et al.  Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia , 2013, Entropy.

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

[41]  Abraham Lempel,et al.  On the Complexity of Finite Sequences , 1976, IEEE Trans. Inf. Theory.

[42]  Enzo Pasquale Scilingo,et al.  Electrodermal Activity in Bipolar Patients during Affective Elicitation , 2014, IEEE Journal of Biomedical and Health Informatics.

[43]  R. Boostani,et al.  ECG-Based Personal Identification Using Empirical Mode Decomposition and Hilbert Transform , 2012, The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012).

[44]  Chuan-Yu Chang,et al.  Based on Support Vector Regression for emotion recognition using physiological signals , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[45]  Vahid Abootalebi,et al.  Selection of an efficient feature space for EEG-based mental task discrimination , 2014 .

[46]  Wanhui Wen,et al.  Analysis of affective ECG signals toward emotion recognition , 2010 .

[47]  Dermot Diamond,et al.  Employing ensemble empirical mode decomposition for artifact removal: Extracting accurate respiration rates from ECG data during ambulatory activity , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

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

[49]  Sridhar Krishnan,et al.  Application of a variation of empirical mode decomposition and teager energy operator to EEG signals for mental task classification , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[50]  Changchun Liu,et al.  Physiology-based affect recognition for computer-assisted intervention of children with Autism Spectrum Disorder , 2008, Int. J. Hum. Comput. Stud..

[51]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.