Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

Abstract

BackgroundIdentifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.MethodsEmotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature ‘Hurst’ was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers – Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm.ResultsAnalysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively.ConclusionsThe results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system.

Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers

“…Regarding automatic emotion identification, audio-visual stimuli have also been applied [4]. In contrast with alternative stimuli methods, audio-visual production of emotional states has been found to be superior and more pervasively adopted [13,15,16]. Therefore, brief audio-visual film excerpts were adopted to elicit emotion in this research.…”

“…Emotional changes would be elicited using different physiological signals such as galvanic skin response (GSR) [15], electrodermal activity (EDA) [17], blood volume pressure (BVP) [18], and skin temperature (ST) [19], evoked potentials (EP) [20], electrocardiogram (ECG) [21], electromyogram (EMG) [22], and electroencephalogram (EEG) [23][24][25][26][27][28][29][30]. Clinically, EEG signals have been widely used as useful indicators of different mental states such as epilepsy, Alzheimer's disease (AD) and vascular dementia (VaD) [31][32][33][34][35].…”

“…Studies on EEG signal processing have been conducted to identify the brain activity patterns involved in cognitive science, neuropsychological research, clinical assessments, and consciousness research [42][43][44][45][46][47]. Recently, EEG has been widely used to assess and evaluate the human emotional states with excellent time resolution [3,15,[28][29][30]48]. EEG can provide useful information of emotional states that have been described as a potential biomarker to evaluate different emotional responses from multi-channel EEG datasets over the brain regions [38].…”

Low Cost Wearable Sensor for Human Emotion Recognition Using Skin Conductance Response

“…Post hoc tests using the Bonferroni correction showed an increase in the PSD value in the happy emotional state (i.e., −2.251 ± 0.92384 W/Hz) compared with that in the recovery state (i.e., −2.802 ± 0.62080 W/Hz), which was statistically significant (p = 0. 19). An increment in the SCR PSD value was also observed in the anger emotional state (i.e., −2.248 ± 0.92384 W/Hz) compared with that in the recovery state (i.e., −2.802 ± 0.62080 W/Hz), which was also statistically significantly different from the recovery state (p = 0.16).…”

“…The happy stimulus was arranged at the beginning of the elicitation process and followed by the negative emotion [19]. Neutral and recovery sessions were utilized as control.…”