An open source tool for heart rate variability spectral analysis

Rodríguez-Liñares, Leandro; Méndez, Adriana; Lado, Marı́a J.; Olivieri, David N.; Vila, Xosé A.; Gómez-Conde, Iván

doi:10.1016/j.cmpb.2010.05.012

Cited by 70 publications

(50 citation statements)

References 11 publications

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“…The data collected was preprocessed and analyzed using the package RHRV [58]. Different from some physiological signals that can be used to infer only arousal, such as galvanic skin response, HRV is widely used to analyze the interplay between sympathetic and parasympathetic dominance, and the capacity for regulated emotional responding [4].…”

Section: Emotions -Physiologicalmentioning

confidence: 99%

Regulating Feelings During Interpersonal Conflicts by Changing Voice Self-perception

Costa

Jung

Czerwinski

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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Emotions play a major role in how interpersonal conflicts unfold. Although several strategies and technological approaches have been proposed for emotion regulation, they often require conscious attention and effort. This often limits their efficacy in practice. In this paper, we propose a different approach inspired by self-perception theory: noticing that people are often reacting to the perception of their own behavior, we artificially change their perceptions to influence their emotions. We conducted two studies to evaluate the potential of this approach by automatically and subtly altering how people perceive their own voice. In one study, participants that received voice feedback with a calmer tone during relationship conflicts felt less anxious. In the other study, participants who listened to their own voices with a lower pitch during contentious debates felt more powerful. We discuss the implications of our findings and the opportunities for designing automatic and less perceptible emotion regulation systems.

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Section: Emotions -Physiologicalmentioning

confidence: 99%

Regulating Feelings During Interpersonal Conflicts by Changing Voice Self-perception

Costa

Jung

Czerwinski

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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“…R-R interval data was extracted from the Android ® device and imported into RHRV for Ubuntu (Rodríguez-Liñares et al, 2011). Signal was visually inspected for artifact (Prinsloo et al, 2011; Quintana et al, 2012; Wells, Outhred, Heathers, Quintana, & Kemp, 2012).…”

Section: Methodsmentioning

confidence: 99%

Resting high frequency heart rate variability is not associated with the recognition of emotional facial expressions in healthy human adults

Beffara

Vermeulen

Mermillod

2016

Preprint

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This study explores whether the myelinated vagal connection between the heart and the brain is involved in emotion recognition. The Polyvagal theory postulates that the activity of the myelinated vagus nerve underlies socio-emotional skills. It has been proposed that the perception of emotions could be one of this skills dependent on heart-brain interactions. However, this assumption was differently supported by diverging results suggesting that it could be related to confounded factors. In the current study, we recorded the resting state vagal activity (reflected by High Frequency Heart Rate Variability, HF-HRV) of 77 (68 suitable for analysis) healthy human adults and measured their ability to identify dynamic emotional facial expressions. Results show that HF-HRV is not related to the recognition of emotional facial expressions in healthy human adults. We discuss this result in the frameworks of the polyvagal theory and the neurovisceral integration model.

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“…For example, currently available R and MATLAB statistical software packages for HRV analysis mostly deal with outliers by allowing the user to manipulate directly the HRV records (Perakakis et al, 2010;Rodríguez-Liñares et al, 2011). This kind of pre-processing, however, comes with a major risk: the filtered trajectory might be clear of evident outliers, but other atypical observations incompatible with the assumed model remain hidden within the processed sample.…”

Section: Introductionmentioning

confidence: 96%