scholarly journals Conscious processing of narrative stimuli synchronizes heart rate between individuals

2020 ◽  
Author(s):  
Pauline Pérez ◽  
Jens Madsen ◽  
Leah Banellis ◽  
Başak Türker ◽  
Federico Raimondo ◽  
...  
Keyword(s):  
Heart Rate ◽  
Autonomic Function ◽  
Conscious State ◽  
Attentional Focus ◽  
Disorders Of Consciousness ◽  
Attentional State ◽  
Healthy Individuals ◽  
Neural Processes ◽  
Heart Rate Fluctuations ◽  
Conscious Processing

AbstractHeart rate has natural fluctuations that are typically ascribed to autonomic function. Recent evidence suggests that conscious processing can affect the timing of the heartbeat. We hypothesized that heart rate is modulated by conscious processing and therefore dependent on attentional focus. To test this, we leverage the observation that neural processes can be synchronized between subjects by presenting an identical narrative stimulus. As predicted, we find significant inter-subject correlation of the heartbeat (ISC-HR) when subjects are presented with an auditory or audiovisual narrative. Consistent with the conscious processing hypothesis, we find that ISC-HR is reduced when subjects are distracted from the narrative, and that higher heart rate synchronization predicts better recall of the narrative. Finally, patients with disorders of consciousness who are listening to a story have lower ISC-HR, as compared to healthy individuals, and that individual ISC-HR might predict a patients’ prognosis.. We conclude that heart rate fluctuations are partially driven by conscious processing, depend on attentional state, and may represent a simple metric to assess conscious state in unresponsive patients.

scholarly journals Assessment of autonomic function in hemodialysed patients by power spectral analysis of heart rate fluctuations.

1996 ◽  
Vol 29 (1) ◽  
pp. 37-44
Author(s):  
Yoshio Kawase ◽  
Shingo Hosoi ◽  
Hideaki Itoh ◽  
Satoru Yamasaki ◽  
Noriyuki Iwamoto ◽  
...  
Keyword(s):  
Heart Rate ◽  
Spectral Analysis ◽  
Autonomic Function ◽  
Power Spectral Analysis ◽  
Power Spectral ◽  
Heart Rate Fluctuations

Assessment of autonomic function in humans by heart rate spectral analysis

1985 ◽  
Vol 248 (1) ◽  
pp. H151-H153 ◽  
Author(s):  
B. Pomeranz ◽  
R. J. Macaulay ◽  
M. A. Caudill ◽  
I. Kutz ◽  
D. Adam ◽  
...  
Keyword(s):  
Heart Rate ◽  
Nervous System ◽  
Spectral Analysis ◽  
High Frequency ◽  
Autonomic Function ◽  
Parasympathetic Nervous System ◽  
Low Frequency ◽  
Parasympathetic System ◽  
Heart Rate Fluctuations ◽  
Frequency Fluctuations

Spectral analysis of spontaneous heart rate fluctuations were assessed by use of autonomic blocking agents and changes in posture. Low-frequency fluctuations (below 0.12 Hz) in the supine position are mediated entirely by the parasympathetic nervous system. On standing, the low-frequency fluctuations increase and are jointly mediated by the sympathetic and parasympathetic nervous systems. High-frequency fluctuations, at the respiratory frequency, are decreased by standing and are mediated solely by the parasympathetic system. Heart rate spectral analysis is a powerful noninvasive tool for quantifying autonomic nervous system activity.

scholarly journals Towards New Diagnostic Approaches in Disorders of Consciousness: A Proof of Concept Study on the Promising Use of Imagery Visuomotor Task

2020 ◽  
Vol 10 (10) ◽  
pp. 746
Author(s):  
Antonino Naro ◽  
Rocco Salvatore Calabrò
Keyword(s):  
Motor Imagery ◽  
Functional Neuroimaging ◽  
Minimally Conscious State ◽  
Conscious State ◽  
Disorders Of Consciousness ◽  
Control Group ◽  
Imagery Task ◽  
Healthy Individuals ◽  
Overt Behavior ◽  
Visuomotor Task

Background: advanced paraclinical approaches using functional neuroimaging and electroencephalography (EEG) allow identifying patients who are covertly aware despite being diagnosed as unresponsive wakefulness syndrome (UWS). Bedside detection of covert awareness employing motor imagery tasks (MI), which is a universally accepted clinical indicator of awareness in the absence of overt behavior, may miss some of these patients, as they could still have a certain level of awareness. We aimed at assessing covert awareness in patients with UWS using a visuomotor-guided motor imagery task (VMI) during EEG recording. Methods: nine patients in a minimally conscious state (MCS), 11 patients in a UWS, and 15 healthy individuals (control group—CG) were provided with an VMI (imagine dancing while watching a group dance video to command), a simple-MI (imagine squeezing their right hand to command), and an advanced-MI (imagine dancing without watching a group dance video to command) to detect command-following. We analyzed the command-specific EEG responses (event-related synchronization/desynchronization—ERS/ERD) of each patient, assessing whether these responses were appropriate, consistent, and statistically similar to those elicited in the CG, as reliable markers of motor imagery. Results: All patients in MCS, all healthy individuals and one patient in UWS repeatedly and reliably generated appropriate EEG responses to distinct commands of motor imagery with a classification accuracy of 60–80%. Conclusions: VMI outperformed significantly MI tasks. Therefore, patients in UWS may be still misdiagnosed despite a rigorous clinical assessment and an appropriate MI assessment. It is thus possible to suggest that motor imagery tasks should be delivered to patients with chronic disorders of consciousness in visuomotor-aided modality (also in the rehabilitation setting) to greatly entrain patient’s participation. In this regard, the EEG approach we described has the clear advantage of being cheap, portable, widely available, and objective. It may be thus considered as, at least, a screening tool to identify the patients who deserve further, advanced paraclinical approaches.

scholarly journals Unsupervised Clustering of Individuals Sharing Selective Attentional Focus Using Physiological Synchrony

2022 ◽  
Vol 2 ◽  
Author(s):  
Ivo V. Stuldreher ◽  
Alexandre Merasli ◽  
Nattapong Thammasan ◽  
Jan B. F. van Erp ◽  
Anne-Marie Brouwer
Keyword(s):  
Heart Rate ◽  
Real World ◽  
Electrodermal Activity ◽  
Clustering Algorithms ◽  
Attentional Focus ◽  
Unsupervised Clustering ◽  
Limited Information ◽  
Attentional State ◽  
Sensory Stimuli ◽  
Physiological Synchrony

Research on brain signals as indicators of a certain attentional state is moving from laboratory environments to everyday settings. Uncovering the attentional focus of individuals in such settings is challenging because there is usually limited information about real-world events, as well as a lack of data from the real-world context at hand that is correctly labeled with respect to individuals' attentional state. In most approaches, such data is needed to train attention monitoring models. We here investigate whether unsupervised clustering can be combined with physiological synchrony in the electroencephalogram (EEG), electrodermal activity (EDA), and heart rate to automatically identify groups of individuals sharing attentional focus without using knowledge of the sensory stimuli or attentional focus of any of the individuals. We used data from an experiment in which 26 participants listened to an audiobook interspersed with emotional sounds and beeps. Thirteen participants were instructed to focus on the narrative of the audiobook and 13 participants were instructed to focus on the interspersed emotional sounds and beeps. We used a broad range of commonly applied dimensionality reduction ordination techniques—further referred to as mappings—in combination with unsupervised clustering algorithms to identify the two groups of individuals sharing attentional focus based on physiological synchrony. Analyses were performed using the three modalities EEG, EDA, and heart rate separately, and using all possible combinations of these modalities. The best unimodal results were obtained when applying clustering algorithms on physiological synchrony data in EEG, yielding a maximum clustering accuracy of 85%. Even though the use of EDA or heart rate by itself did not lead to accuracies significantly higher than chance level, combining EEG with these measures in a multimodal approach generally resulted in higher classification accuracies than when using only EEG. Additionally, classification results of multimodal data were found to be more consistent across algorithms than unimodal data, making algorithm choice less important. Our finding that unsupervised classification into attentional groups is possible is important to support studies on attentional engagement in everyday settings.

Heart Rate Signal Decomposition

2000 ◽  
Vol 39 (02) ◽  
pp. 200-203
Author(s):  
H. Mizuta ◽  
K. Yana
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Pressure Change ◽  
Normal Subjects ◽  
Signal Decomposition ◽  
Large Individual ◽  
Heart Rate Fluctuations ◽  
Type Pattern ◽  
Signal Cancellation ◽  
Pressure Changes

Abstract:This paper proposes a method for decomposing heart rate fluctuations into background, respiratory and blood pressure oriented fluctuations. A signal cancellation scheme using the adaptive RLS algorithm has been introduced for canceling respiration and blood pressure oriented changes in the heart rate fluctuations. The computer simulation confirmed the validity of the proposed method. Then, heart rate fluctuations, instantaneous lung volume and blood pressure changes are simultaneously recorded from eight normal subjects aged 20-24 years. It was shown that after signal decomposition, the power spectrum of the heart rate showed a consistent monotonic 1/fa type pattern. The proposed method enables a clear interpretation of heart rate spectrum removing uncertain large individual variations due to the respiration and blood pressure change.

Estimation of Heart Rate Variability Fluctuations by Wavelet Transform

2011 ◽  
Vol 57 (3) ◽  
pp. 395-400 ◽  
Author(s):  
Anton Popov ◽  
Yevgeniy Karplyuk ◽  
Volodymyr Fesechko
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Wavelet Transform ◽  
Experimental Results ◽  
Dyadic Wavelet ◽  
Heart Rate Fluctuations ◽  
Slow Heart Rate ◽  
Dyadic Wavelet Transform

Estimation of Heart Rate Variability Fluctuations by Wavelet TransformTechnique for separate estimation of fast and slow fluctuations in the heart rate signal is developed. The orthogonal dyadic wavelet transform is used to separate the slow heart rate changes in approximation part of decomposition and fast changes in detail parts. Experimental results using the recordings from persons practicing Chi meditation demonstrated the applicability of estimation heart rate fluctuations with the proposed approach.

scholarly journals Holter-Derived Autonomic Function, Arrhythmias and Carbohydrate Metabolism in Patients with Class III Obesity Treated with Laparoscopic Sleeve Gastrectomy

2021 ◽  
Vol 10 (10) ◽  
pp. 2140
Author(s):  
Piotr Bienias ◽  
Zuzanna Rymarczyk ◽  
Justyna Domienik-Karłowicz ◽  
Wojciech Lisik ◽  
Piotr Sobieraj ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Heart Rate ◽  
Weight Loss ◽  
Sleeve Gastrectomy ◽  
Laparoscopic Sleeve Gastrectomy ◽  
Carbohydrate Metabolism ◽  
Autonomic Function ◽  
Young Patients ◽  
Resistance Index ◽  
Class Iii

The effects of weight loss following bariatric surgery on autonomic balance, arrhythmias and insulin resistance are still of interest. We prospectively investigated 50 patients with BMI > 40 kg/m2, aged 36.5 (18–56) years who underwent laparoscopic sleeve gastrectomy. Among other examinations, all subjects had 24-h Holter monitoring with heart rate variability (HRV) and heart rate turbulence (HRT) evaluation. After a median of 15 months, BMI decreased from 43.9 to 29.7 kg/m2, the incidence of hypertension decreased from 54 to 32% (p = 0.04) and any carbohydrate disorders decreased from 24 to 6% (p = 0.02). Fasting insulin concentration and insulin resistance index improved significantly (p < 0.001). Improvements in HRV parameters related to the sympathetic autonomic division were also observed (p < 0.001), while HRT evaluation was not conclusive. The enhancement of autonomic tone indices was correlated with reduction of BMI (SDNN-I r = 0.281 p = 0.04; SDNN r = 0.267 p = 0.05), but not with reduction of waist circumference, and it was also associated with decrease of mean heart rate (OR 0.02, 95%CI 0.0–0.1, p < 0.001). The incidence of arrhythmias was low and similar before and after follow-up. In conclusion, improvement of homeostasis of carbohydrate metabolism and autonomic function is observed in relatively young patients after weight loss due to laparoscopic sleeve gastrectomy.

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