Journal of Applied Physiology, 75(5), 2310–2317.įurlan, R., Guzzetti, S., Crivellaro, W., Dassi, S., Tinelli, M., Baselli, G., et al. Important influence of respiration on human RR interval power spectra is largely ignored. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology.
Physiological emotion analysis using support vector regression. IEEE Transactions on Affective Computing, 3(4), 482–495. Representative segment-based emotion analysis and classification with automatic respiration signal segmentation. Journal of Physiological Anthropology, 34(1), 25. Analysis of physiological signals for recognition of boredom, pain, and surprise emotions. IEEE Transactions on Biomedical Engineering, 59(12), 3498–3510. Toward an EEG-based recognition of music liking using time-frequency analysis. Autonomic nervous system activity distinguishes among emotions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), 1175–1191. Toward machine emotional intelligence: analysis of affective physiological state.
Sad emotion series#
It concluded that HRV indices showed significant differences between happiness and sadness emotion states and the findings could help to better understand the inherent differences of cardiovascular time series between different emotion states in clinical practice. In addition, all indices (time-domain, frequency-domain and nonlinear) showed no MAP-related changes. Four time-domain indices decreased with the increase of HR (all P < 0.01), while frequency-domain and nonlinear indices demonstrated no HR-related changes for each emotional state. All indices, except for SampEn, had significant positive correlations (all P < 0.01) for the two emotion states. The key result was that among all nine HRV indices, six indices were identified having significant differences between happiness and sadness emotion states: MEAN ( P = 0.028), SDNN ( P = 0.002), three frequency-domain indices (all P < 0.0001) and FuzzyMEn ( P = 0.047), whereas RMSSD, PNN50 and SampEn had no significant differences between the two emotion states.
The results showed that experimental order had no significant effect on all HRV indices from both happiness and sadness emotions (all P > 0.05). In addition, the effects of heart rate (HR) and mean artery pressure (MAP) on the aforementioned HRV indices were analyzed for both emotion states.
RR interval (RRI) time series were extracted from ECGs and multiple HRV indices, including time-domain (MEAN, SDNN, RMSSD and PNN50), frequency-domain (LFn, HFn and LF/HF) and nonlinear indices (SampEn and FuzzyMEn) were calculated. Electrocardiography (ECG) signals were recorded under both emotion states with a random measurement order (first happiness emotion measurement then sadness or reverse). Forty-eight healthy volunteers were enrolled for this study. This pilot study investigated the differences of heart rate variability (HRV) indices between two opposite emotion states: happiness and sadness, to reveal the differences of autonomic nervous system activity under different emotional states.
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