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Related Concept Videos

Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

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The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...
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Decreased pulse rate01:14

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Bradycardia is a medical condition in which the heart rate is slower than normal. It occurs when the heart's natural pacemaker, the sinus node, generates slower electrical impulses than the standard rhythm. In adults, bradycardia is diagnosed when the pulse rate falls below 60 beats per minute, indicating a deviation from the normal heart rate range.
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Regulation of Heart Rates01:31

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The regulation of heart rate is a complex process controlled by the autonomic nervous system (ANS), hormonal influences, and intrinsic cardiac mechanisms. The ANS has two main components: the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS).
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Disturbances in Heart Rhythm01:29

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Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
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Assessment of apical radial pulse01:25

Assessment of apical radial pulse

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Apical-Radial (A-R) Pulse Assessment
The A-R pulse assessment involves simultaneous evaluation of the apical and radial pulses. When the apical and radial pulse rates vary, this assessment helps identify a pulse deficit.
Pre-Procedural Preparation
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Regulation of Pulse01:20

Regulation of Pulse

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Pulse regulation involves physiological mechanisms that ensure adequate blood flow throughout the body. The heartbeat, regulated by the autonomic nervous system, is influenced by hormonal balance, physical activity, and emotional state.
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Related Experiment Video

Updated: Oct 29, 2025

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
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Heart rate variability predicts decline in sensorimotor rhythm control.

Marius Nann1,2, David Haslacher2, Annalisa Colucci2

  • 1Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, University Hospital of Tübingen, Tübingen, Germany.

Journal of Neural Engineering
|July 6, 2021
PubMed
Summary

Heart rate variability (HRV) can predict declines in brain-computer interface (BCI) control performance during a session. This finding may help optimize BCI systems for individuals with motor impairments, such as stroke survivors.

Keywords:
BCIGranger causalityHRVSMRbrain–computer interfaceheart rate variabilitysensorimotor rhythms

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Area of Science:

  • Neuroscience and Biomedical Engineering
  • Brain-Computer Interfaces (BCI)
  • Rehabilitation Technology

Background:

  • Brain-computer interfaces (BCI) utilize sensorimotor rhythms (SMRs) for controlling assistive devices, particularly for individuals with motor impairments like stroke survivors.
  • Current BCI control methods, such as motor imagery (MI), are cognitively demanding and can lead to performance degradation over time, especially in those with cognitive deficits.
  • Identifying biomarkers to predict BCI performance decline is crucial for optimizing human-machine interaction in real-time.

Purpose of the Study:

  • To investigate the relationship between heart rate variability (HRV) and within-session BCI control performance.
  • To determine if HRV can serve as a predictive biomarker for the decline in SMR-ERD control performance.
  • To explore the potential of HRV for adaptive BCI control paradigms.

Main Methods:

  • Seventeen healthy participants performed MI-based SMR-ERD control tasks with visually displayed feedback.
  • Granger causality analysis was employed to assess the predictive relationship between HRV and SMR-ERD control performance.
  • Task difficulty was incrementally increased during the second 8.5-minute run to induce performance decline.

Main Results:

  • While BCI control performance and HRV remained stable during the initial run, both declined significantly over time during the second run.
  • A strong negative correlation was observed between the decline in performance and HRV during the second run.
  • HRV demonstrated significant predictive power for within-session BCI control performance at the individual participant level.

Conclusions:

  • Heart rate variability (HRV) can effectively predict declines in BCI control performance during a single session.
  • These findings support the use of HRV as a biomarker for real-time adaptation of BCI systems.
  • Integrating HRV-based prediction holds promise for personalizing and enhancing assistive BCI technologies for stroke rehabilitation.