Breathing rhythms and emotions

Homma, Ikuo; Masaoka, Yuri

doi:10.1113/expphysiol.2008.042424

Cited by 340 publications

(275 citation statements)

References 64 publications

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“…2.13) as well as their complexity we found, in accordance to previous findings [102,105,106]. Homma et al [107] stated that the final respiratory output involves a complex interaction between the brainstem and higher centers, including the limbic system and cortical structures. Respiration is primarily regulated for metabolic and homeostatic purposes in the brainstem and also changes in response to changes in emotions, such as sadness, happiness, anxiety or fear.…”

Section: Schizophrenia: Cardiorespiratory Systemsupporting

confidence: 81%

Complex and Nonlinear Analysis of Heart Rate Variability in the Assessment of Fetal and Neonatal Wellbeing

Signorini

Magenes

Ferrario

et al. 2017

Complexity and Nonlinearity in Cardiovascular Signals

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Methods from nonlinear dynamics have shown new insights into alterations of the cardiovascular system under various physiological and pathological conditions, and thus providing additional prognostic information. In this chapter prominent complexity methods of non-linear dynamics as symbolic dynamics, Poincaré plot analyses, and compression entropy are introduced and their algorithmic implementations and application examples in clinical trials are provided. Especially, we will give their basic theoretical background, their main features and demonstrate their usefulness in different applications in the field of cardiovascular and cardiorespiratory time series analyses. IntroductionLinear time and frequency domain measures are often not sufficient enough to quantify the complex dynamics of physiological systems and their related time series. Therefore, various efforts have been made to apply nonlinear complexity measures to analyze, e.g. the heart rate variability (HRV) [1]. These approaches differ from the traditional time-and frequency domain HRV analyses because they quantify the signal properties instead of assessing only the magnitude or the frequency power of the heart rate time series. They assess the self-affinity of heartbeat fluctuations over multiple time scales (fractal measures); the regularity/irregularity or randomness of heartbeat fluctuations (entropy measures); the coarse-grained dynamics of HR fluctuations based on symbols (symbolic dynamics) and the heartbeat dynamics based on a simplified phase-space embedding [1].Symbolic dynamics is based on a coarse graining of the dynamics of a signal. The time series (in our cases the ECG or the noninvasively recorded blood pressure curve) are transformed into symbol sequences with symbols of a given alphabet. Some detail information is lost but the coarse dynamic behavior retains and

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Section: Schizophrenia: Cardiorespiratory Systemsupporting

confidence: 81%

Complex and Nonlinear Analysis of Heart Rate Variability in the Assessment of Fetal and Neonatal Wellbeing

Signorini

Magenes

Ferrario

et al. 2017

Complexity and Nonlinearity in Cardiovascular Signals

View full text Add to dashboard Cite

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“…2 (#9) Emotion can influence breathing rates [6], [34]. The measuring device could be a respiration belt or sensors embedded into clothing.…”

Section: ) Central Nervous System Signalsmentioning

confidence: 99%

Technological Advancements in Affective Gaming: A Historical Survey

Christy¹,

Kuncheva²

2014

GSTF J Comput

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Abstract-Affective gaming (AG) is a cross-disciplinary area drawing upon psychology, physiology, electronic engineering and computer science, among others. This paper presents a historical overview of affective gaming, bringing together psychophysiological system developments, a time-line of video game graphical advancements and industry trends, thereby offering an entry point into affective gaming research. It is proposed that video games may soon reach a peak in perceivable graphical improvements. This opens up the door for innovative game enhancement strategies, such as emotiondriven interactions between the player and the gaming environment.

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“…Investigations were performed between 15.00 and 18.00 in a quiet room which was kept comfortably warm (22)(23)(24) • C) and began after subjects had rested in the supine position for 10 min. Subjects were asked to relax and to breathe normally to avoid hyperventilation.…”

Section: (B) Data Recordings and Pre-processingmentioning

confidence: 99%

“…This network originates within the interconnected bilateral columns of the medullary neurons as well as in the ventral respiratory columns and is controlled by inputs from other medullary structures, including the retrotrapezoid nucleus, raphe nuclei and other rostral pontine circuits [15]. Given that respiration is primarily regulated for metabolic and homeostatic purposes in the brainstem but also constantly responds to changes in emotions, such as sadness, anxiety or fear [22], it seems that psychotic states of schizophrenic patients will have a major impact on the cardiorespiratory system. The interactions within the cardiorespiratory system are mainly expressed as respiratory sinus arrhythmia (RSA).…”

Section: Introductionmentioning

confidence: 99%