Following the Rhythm of the Heart: HeartMath Institute’s Path to HRV Biofeedback

McCraty, Rollin

doi:10.1007/s10484-022-09554-2

Cited by 23 publications

(35 citation statements)

References 78 publications

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“…Besides the frontal-vagal pathway, there are also bottomup influences from the heart to the brain. The heart and brain are connected in four ways [14]: (1) physically by the pulsating (cerebral) blood flow, (2) biochemically through hormones and neurotransmitters, (3) electrically by an electric field of the heart that continually affects the whole body, and (4) the nervous system that controls HR and HRV. Although the knowledge about the afferent influence of the heart on the brain is still limited, this might play an important role in cognitive processing and emotions.…”

Section: Introductionmentioning

confidence: 99%

Heartbeat-Evoked Potential in Major Depressive Disorder: A Biomarker for Differential Treatment Prediction between Venlafaxine and rTMS?

Zwienenberg¹,

Enriquez‐Geppert²,

Vinne³

et al. 2023

Neuropsychobiology

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Introduction: Currently, major depressive disorder (MDD) treatment plans are based on trial-and-error, and remission rates remain low. A strategy to replace trial-and-error and increase remission rates could be treatment stratification. We explored the heartbeat-evoked potential (HEP) as a biomarker for treatment stratification to either antidepressant medication or rTMS treatment. Methods: Two datasets were analyzed: (1) the International Study to Predict Optimized Treatment in Depression (iSPOT-D; n = 1,008 MDD patients, randomized to escitalopram, sertraline, or venlafaxine, and n = 336 healthy controls) and (2) a multi-site, open-label rTMS study (n = 196). The primary outcome measure was remission. Cardiac field artifacts were removed from the baseline EEG using independent component analysis (ICA). The HEP-peak was detected in a bandwidth of 20 ms around 8 ms and 270 ms (N8, N270) after the R-peak of the electrocardiogram signal. Differences between remitters and non-remitters were statistically assessed by repeated-measures ANOVAs for electrodes Fp1, Cz, and Oz. Results: In the venlafaxine subgroup, remitters showed a lower HEP around the N8 peak than non-remitters on electrode site Cz (p = 0.004; d = 0.497). The rTMS group showed a non-significant difference in the opposite direction (d = −0.051). Retrospective stratification to one of the treatments based on the HEP resulted in enhanced treatment outcome prediction for venlafaxine (+22.98%) and rTMS (+10.66%). Conclusion: These data suggest that the HEP could be used as a stratification biomarker between venlafaxine and rTMS; however, future out-of-sample replication is warranted.

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Section: Introductionmentioning

confidence: 99%

Heartbeat-Evoked Potential in Major Depressive Disorder: A Biomarker for Differential Treatment Prediction between Venlafaxine and rTMS?

Zwienenberg¹,

Enriquez‐Geppert²,

Vinne³

et al. 2023

Neuropsychobiology

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“…It reacts to any external stimuli by firing neurons inside the brain, causing changes in ANS activity, due to which, the routine activity of the heart and other peripheral organs varies. In addition, change in the emotional state affects heart activity [ 8 ]. EEG and ECG signals are the biopotential that reflect the activities of the brain and heart, respectively.…”

Section: Introductionmentioning

confidence: 99%

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Nalwaya

Das

Pachori

2022

Entropy

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Human dependence on computers is increasing day by day; thus, human interaction with computers must be more dynamic and contextual rather than static or generalized. The development of such devices requires knowledge of the emotional state of the user interacting with it; for this purpose, an emotion recognition system is required. Physiological signals, specifically, electrocardiogram (ECG) and electroencephalogram (EEG), were studied here for the purpose of emotion recognition. This paper proposes novel entropy-based features in the Fourier–Bessel domain instead of the Fourier domain, where frequency resolution is twice that of the latter. Further, to represent such non-stationary signals, the Fourier–Bessel series expansion (FBSE) is used, which has non-stationary basis functions, making it more suitable than the Fourier representation. EEG and ECG signals are decomposed into narrow-band modes using FBSE-based empirical wavelet transform (FBSE-EWT). The proposed entropies of each mode are computed to form the feature vector, which are further used to develop machine learning models. The proposed emotion detection algorithm is evaluated using publicly available DREAMER dataset. K-nearest neighbors (KNN) classifier provides accuracies of 97.84%, 97.91%, and 97.86% for arousal, valence, and dominance classes, respectively. Finally, this paper concludes that the obtained entropy features are suitable for emotion recognition from given physiological signals.

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“…It is a recognized indicator of a person’s physiological and behavioral regulatory capacities underlying physical and psychological resilience ( Thayer and Lane, 2000 ; Andreassi, 2010 ; Holzman and Bridgett, 2017 ). Moreover, increased levels of HRV have been associated with beneficial effects for health ( Gevirtz, 2013 ; McCraty and Shaffer, 2015 ; Lehrer et al, 2020 ) and cognitive performance ( McCraty, 2003 , 2016 ; Hansen et al, 2004 ; Thayer et al, 2009 ; Forte et al, 2019 ). Different theories suggest that the interaction between HRV and cognition is associated with cardiac vagal control (CVC), which is an index of the tonic and phasic parasympathetic activity that the vagus nerves exert to regulate the heart ( Lehrer et al, 2000 ; Thayer and Lane, 2000 , 2009 ; McCraty and Childre, 2010 ; Shaffer et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Respiratory sinus arrhythmia during biofeedback is linked to persistent improvements in attention, short-term memory, and positive self-referential episodic memory

2022

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BackgroundHeart rate variability (HRV) biofeedback, an intervention based on the voluntary self-regulation of autonomic parameters, has been shown to affect prefrontal brain functioning and improve executive functions. The interest in using HRV biofeedback as cognitive training is typically ascribed to parasympathetic activation and optimized physiological functioning deriving from increased cardiac vagal control. However, the persistence of cognitive effects is poorly studied and their association with biofeedback-evoked autonomic changes has not yet been explored. In addition, no study has so far investigated the influence of HRV biofeedback in adults on long-term episodic memory, which is particularly concerned with self-referential encoding processing.MethodsIn the present study, a novel training system was developed integrating HRV and respiratory biofeedback into an immersive virtual reality environment to enhance training efficacy. Twenty-two young healthy adults were subjected to a blinded randomized placebo-controlled experiment, including six self-regulation training sessions, to evaluate the effect of biofeedback on autonomic and cognitive changes. Cardiac vagal control was assessed before, during, and 5 min after each training session. Executive functions, episodic memory, and the self-referential encoding effect were evaluated 1 week before and after the training program using a set of validated tasks.ResultsLinear mixed-effects models showed that HRV biofeedback greatly stimulated respiratory sinus arrhythmia during and after training. Moreover, it improved the attentional capabilities required for the identification and discrimination of stimuli (ηp2 = 0.17), auditory short-term memory (ηp2 = 0.23), and self-referential episodic memory recollection of positive stimuli (ηp2 = 0.23). Episodic memory outcomes indicated that HRV biofeedback reinforced positive self-reference encoding processing. Cognitive changes were strongly dependent on the level of respiratory sinus arrhythmia evoked during self-regulation training.ConclusionThe present study provides evidence that biofeedback moderates respiration-related cardiac vagal control, which in turn mediates improvements in several cognitive processes crucial for everyday functioning including episodic memory, that are maintained beyond the training period. The results highlight the interest in HRV biofeedback as an innovative research tool and medication-free therapeutic approach to affect autonomic and neurocognitive functioning. Finally, a neurocognitive model of biofeedback-supported autonomic self-regulation as a scaffolding for episodic memory is proposed.

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Following the Rhythm of the Heart: HeartMath Institute’s Path to HRV Biofeedback

Cited by 23 publications

References 78 publications

Heartbeat-Evoked Potential in Major Depressive Disorder: A Biomarker for Differential Treatment Prediction between Venlafaxine and rTMS?

Heartbeat-Evoked Potential in Major Depressive Disorder: A Biomarker for Differential Treatment Prediction between Venlafaxine and rTMS?

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Respiratory sinus arrhythmia during biofeedback is linked to persistent improvements in attention, short-term memory, and positive self-referential episodic memory

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