Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human Emotional State Classification Using Discrete Wavelet Transform (DWT)

Abstract

[Purpose] Intelligent emotion assessment systems have been highly successful in a variety of applications, such as e-learning, psychology, and psycho-physiology. This study aimed to assess five different human emotions (happiness, disgust, fear, sadness, and neutral) using heart rate variability (HRV) signals derived from an electrocardiogram (ECG). [Subjects] Twenty healthy university students (10 males and 10 females) with a mean age of 23 years participated in this experiment. [Methods] All five emotions were induced by audio-visual stimuli (video clips). ECG signals were acquired using 3 electrodes and were preprocessed using a Butterworth 3rd order filter to remove noise and baseline wander. The Pan-Tompkins algorithm was used to derive the HRV signals from ECG. Discrete wavelet transform (DWT) was used to extract statistical features from the HRV signals using four wavelet functions: Daubechies6 (db6), Daubechies7 (db7), Symmlet8 (sym8), and Coiflet5 (coif5). The k-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used to map the statistical features into corresponding emotions. [Results] KNN provided the maximum average emotion classification rate compared to LDA for five emotions (sadness − 50.28%; happiness − 79.03%; fear − 77.78%; disgust − 88.69%; and neutral − 78.34%). [Conclusion] The results of this study indicate that HRV may be a reliable indicator of changes in the emotional state of subjects and provides an approach to the development of a real-time emotion assessment system with a higher reliability than other systems.

The Psychological and Physiological Part of Emotions: Multimodal Approximation for Valence Classification

“…However, if the PNS and SNS are linked with the positive/approach and negative/withdrawal responses [55], respectively, is more diffuse. As for HRV measures, both our results and those of other authors, showed differences in their response to either emotional dimensions [56], [20] and discrete emotions [57], [58], [21]. These differences are present in all levels of the HRV analysis, the time domain, the frequency domain and the Poincare plot.…”

Heart rate spectrum analysis for sleep quality detection

“…Seventy-nine percent of the actual and estimated REM was quite similar and 80% of the test subjects felt better when woken up during REM sleep instead of NREM sleep. Murugappan [8] developed an algorithm to assess five human emotions (sadness, happiness, fear, disgust, neutral) from the heart rate variability (HRV). They achieved an average agreement of 74.82%.…”

Linking numbers to perceptions and experiences: Why we need transdisciplinary mixed-methods combining neurophysiological and qualitative data

“…It is mostly a measure of vagal nerve activity, and it correlates highly with actual activity of this nerve (Kuo et al, 2005). According to a recent review, HRV may be a reliable indicator of changes in the emotional state of subjects and provides an approach to a real-time emotional assessment system with a higher reliability than other systems (Murugappan et al, 2013). A reduced HRV was found to be associated with emotional strain and elevated stress (Chandola et al, 2010).…”

“…Sensors for heart rate: Blood Volume Pulse sensor (attached to a finger) or electrocardiography (three electrodes attached on the chest) For heart rate coherence: also a sensor for respiration Stress, emotional state (well-being), pain (McCraty et al, 2009;Murugappan et al, 2013) Cortical brain waves (EEG) Electroencephalography (EEG): min. three sensors (but depending on the application often more: 19 for the standard 10/20 system, more than 100 for high-density EEG) Individually: psychological pain (Meerwijk et al, 2015) concentration versus focus on the external world (Harmony, 2013) emotional state (Du and Lee, 2014;Guntekin and Basar, 2014;Jatupaiboon et al, 2013) Between two people (inter-brain synchronisation; Baess et al, 2012;Liu and Pelowski, 2014;McCraty, 2003;Yun, 2013;Yun et al, 2012): (un)successful communication evaluation of uncertainty and risk competition/cooperation lobe is correlated with withdrawal emotions such as anxiety and depressive feelings (Jesulola et al, 2015;Morins, 2011).…”