Differences of Heart Rate Variability Between Happiness and Sadness Emotion States: A Pilot Study

Shi, Hongyu; Yang, Licai; Zhao, Lulu; Su, Zhonghua; Mao, Xinrui; Zhang, Li; Liu, Chengyu

doi:10.1007/s40846-017-0238-0

Cited by 62 publications

(40 citation statements)

References 31 publications

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“…While breathing is a peripheral rhythm with a special relationship with the mind, other physiological measures have emotional expression. These include cardiovascular indices, temperature, electrodermal, blood oxygenation, photoplethysmographic, and electromyographic measures (Shi et al, 2017). Although potentially invasive, biomarkers such as cortisol can also provide insight into emotional state (Strahler et al, 2017).…”

Section: Other Responses To Emotionmentioning

confidence: 99%

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Respiratory Rhythm, Autonomic Modulation, and the Spectrum of Emotions: The Future of Emotion Recognition and Modulation

Jerath¹,

Beveridge²

2020

Front. Psychol.

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Pulmonary ventilation and respiration are considered to be primarily involved in oxygenation of blood for oxygen delivery to cells throughout the body for metabolic purposes. Other pulmonary physiological observations, such as respiratory sinus arrhythmia, Hering Brewer reflex, cardiorespiratory synchronization, and the heart rate variability (HRV) relationship with breathing rhythm, lack complete explanations of physiological/functional significance. The spectrum of waveforms of breathing activity correlate to anxiety, depression, anger, stress, and other positive and negative emotions. Respiratory pattern has been thought not only to be influenced by emotion but to itself influence emotion in a bi-directional relationship between the body and the mind. In order to show how filling in gaps in understanding could lead to certain future developments in mind-body medicine, biofeedback, and personal health monitoring, we review and discuss empirical work and tracings to express the vital role of bodily rhythms in influencing emotion, autonomic nervous system activity, and even general neural activity. Future developments in measurement and psychophysiological understanding of the pattern of breathing in combination with other parameters such as HRV, cardiorespiratory synchronization, and skin conductivity may allow for biometric monitoring systems to one day accurately predict affective state and even affective disorders such as anxiety. Better affective prediction based on recent research when incorporated into personal health monitoring devices could greatly improve public mental health by providing at-home biofeedback for greater understanding of one's mental state and for mind-body affective treatments such as breathing exercises.

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Section: Other Responses To Emotionmentioning

confidence: 99%

“…Heart sounds can even be used in emotion recognition (Xiefeng et al, 2019). Heart rate variability (HRV) is the tiny variations in sinus heartbeats (Shi et al, 2017). Although some have criticized the idea, HRV is widely thought to differentiate a parasympathetic versus a sympathetic state (Balzarotti et al, 2017).…”

Section: Other Responses To Emotionmentioning

confidence: 99%

Respiratory Rhythm, Autonomic Modulation, and the Spectrum of Emotions: The Future of Emotion Recognition and Modulation

Jerath¹,

Beveridge²

2020

Front. Psychol.

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“…On the other hand, changed HRV, heart rate complexity and baroreflex sensitivity have been described in studies (Koschke et al, 2009; Schulz et al, 2010; Voss et al, 2011), indicating a reduction of vagal function in relation to the central autonomic dysregulation. Specifically, HRV has emerged as a physiological marker for emotional regulation (Shi et al, 2017), psychological well-being (Chalmers et al, 2014), and general cardiovascular health (Kemp and Quintana, 2013). Among the time-domain, frequency-domain and non-linear parameters, entropy is regarded as a valuable tool to quantify the regularity and inherent complexity of physiological time series and can provide important insights for understanding the underlying mechanisms of the cardiovascular and respiration systems (Zhao et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Cardiorespiratory Coupling Analysis Based on Entropy and Cross-Entropy in Distinguishing Different Depression Stages

Zhao

Yang

et al. 2019

Front. Physiol.

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Aims This study used entropy- and cross entropy-based methods to explore the cardiorespiratory coupling of depressive patients, and thus to assess the values of those entropy methods for identifying depression patients with different disease severities. Methods Electrocardiogram (ECG) and respiration signals from 69 depression patients were recorded simultaneously for 5 min. Patients were classified into three groups according to the Hamilton Depression Rating Scale (HDRS) scores: group Non-De (HDRS 0–7), Mid-De (HDRS 8–17), and Con-De (HDRS >17). Sample entropy (SEn), fuzzy measure entropy (FMEn) and high-frequency power (HF) were computed on the original RR interval time series and breath-to-breath interval time series. Cross sample entropy (CSEn) and cross fuzzy measure entropy (CFMEn) were computed on interval time series resampled at 2 Hz and 4 Hz, respectively. The difference among three patient groups and correlation between entropy values and HDRS scores were analyzed by statistical analysis. Surrogate data were also employed to confirm the validation of entropy measures in this study. Results A consistent increasing trend has been found among most entropy measures from Non-De, to Mid-De, and to Con-De groups, and a significant ( p < 0.05) difference in FMEn of RR intervals exists between Non-De and Mid-De or Con-De groups. Significant differences have been also found in all cross entropies, between Non-De and Con-De groups and between Mid-De and Con-De groups. Furthermore, significant correlations also exist between HDRS scores and FMEn of RR intervals ( R = 0.24, p < 0.05), CSEn at 4 Hz ( R = 0.26, p < 0.05) or 2 Hz ( R = 0.28, p < 0.05) resampling, and CFMEn at 4 Hz ( R = 0.31, p < 0.01) or 2 Hz ( R = 0.30, p < 0.05) resampling. A significant difference of cardiorespiratory coupling parameters between different depression stages and significant correlations between entropy measures and depression severity both indicate central autonomic dysregulation in depression patients and reflect varying degrees of vagal modulation reduction among different depression levels. Analysis based on surrogate data confirms that the non-linear properties of the physiological signals played a major role in depression recognition. Conclusion The current study demonstrates the potential of cardiorespiratory coupling in the auxiliary diagnosis of depression based on the entropy method.

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“…情绪对个体身心健康、主观幸福感有十分重要的影响 (Diener & Tay, 2017;Shi et al, 2017 (Lang et al, 1990(Lang et al, , 1996。相似地，与中性刺激相比，高接近动机的积极刺激使注意范围变窄，而低接近动机的积极情绪使注意范围拓宽 (Gable & Harmon-Jones, 2008 但是探测不到 T2，出现注意暂时性的脱离，即注意瞬脱 (Raymond et al, 1992;Shapiro et al, 1994Shapiro et al, , 1997 …”

Section: 引言unclassified

The Influence of Different Emotional States on External Emotional Stimuli

Zhang¹

2018

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Emotion has an important influence on attention. The present study mainly investigates the influences of emotional state on humans' attention to emotional stimuli. In this study, forty-two college students were tested. Prior to the experimental task, subjects were required to watch a positive or negative video to induce corresponding emotional state. Subsequently, subjects were asked to complete an attentional blink task, during which subjects were firstly asked to judge if T1 was a flower or an animal, and then to classify T2 according to their emotional valence (positive, neutral or negative). It was found that, regardless of individual emotional state, negative stimuli were the most difficult to be detected, and there were no differences in the detection of positive and neutral stimuli. Meanwhile, compared to those in the positive mood state, subjects in negative mood state found it more difficult to detect negative stimuli. The study showed that the emotional state of the individual had an impact on attention devotion to emotional stimuli, especially on the attention to negative stimuli. And this effect may be related to avoidance motivation, devoting more cognitive resources, and to the narrowing of the attention breadth during negative stimulation.

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Differences of Heart Rate Variability Between Happiness and Sadness Emotion States: A Pilot Study

Cited by 62 publications

References 31 publications

Respiratory Rhythm, Autonomic Modulation, and the Spectrum of Emotions: The Future of Emotion Recognition and Modulation

Respiratory Rhythm, Autonomic Modulation, and the Spectrum of Emotions: The Future of Emotion Recognition and Modulation

Cardiorespiratory Coupling Analysis Based on Entropy and Cross-Entropy in Distinguishing Different Depression Stages

The Influence of Different Emotional States on External Emotional Stimuli

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