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Related Concept Videos

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Pulse rhythm01:30

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Dysrhythmias V: Evaluating Dysrhythmias01:30

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Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
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Electrocardiogram01:29

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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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Holter Monitor: 24-Hour Monitoring01:23

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Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
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Related Experiment Video

Updated: Feb 28, 2026

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
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Early Drowsiness Detection via Second-Order Derivative Analysis of Heart Rate Variability: A Non-Contact ECG Approach

Fabrice Vaussenat1, Abhiroop Bhattacharya1, Julie Payette1

  • 1Department of Electrical Engineering, École de Technologie supéRieure, Montreal, QC H3C 1K3, Canada.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

New research suggests that physiological changes, measured by heart rate variability (HRV) derivatives, can predict drowsy driving crashes earlier than behavioral cues. These HRV metrics complement driving performance data for enhanced safety.

Keywords:
HRV derivativescapacitive sensingdriver monitoring systemsdrowsy driving detectionheart rate variabilitymachine learningnon-contact ECGroad safety

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Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
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Area of Science:

  • Physiological monitoring
  • Traffic safety research
  • Machine learning for predictive analytics

Background:

  • Drowsy driving is a major cause of traffic fatalities, accounting for approximately 20% of all such deaths.
  • Current detection systems often rely on behavioral indicators that manifest only after impairment has begun, limiting their effectiveness in preventing accidents.
  • Non-contact methods for monitoring physiological signals are needed to overcome privacy concerns associated with camera-based systems.

Purpose of the Study:

  • To investigate the potential of using first and second derivatives of heart rate variability (HRV) to detect pre-crash states in drowsy drivers earlier than conventional methods.
  • To evaluate the efficacy of combined HRV metrics (conventional and derivative) and driving performance indicators for predicting imminent crashes.
  • To determine the temporal advantage of HRV derivative-based detection over behavioral manifestations and actual crash events.

Main Methods:

  • Utilized a driving simulator with 25 participants completing 49 sessions.
  • Recorded cardiac activity non-invasively using capacitive ECG electrodes embedded in the seat.
  • Defined ground truth labels based strictly on crash proximity to avoid circular evaluation.
  • Analyzed conventional HRV metrics alongside their first and second derivatives, combined with driving performance indicators.

Main Results:

  • The combined HRV feature set achieved an Area Under the Curve (AUC) of 0.863 for pre-crash prediction.
  • HRV derivatives alone showed limited predictive power (AUC = 0.573), highlighting their role as complementary features.
  • Driving performance indicators were the strongest predictors, with an AUC of 0.999.
  • Derivative-based detection preceded behavioral signs by 5-8 minutes and crash events by an average of 6.8 minutes.

Conclusions:

  • HRV derivatives capture subtle physiological changes that precede overt signs of drowsy driving impairment.
  • Integrating HRV derivatives with other data, particularly driving performance indicators, significantly enhances the early detection of pre-crash states.
  • These findings suggest a promising avenue for developing more proactive drowsy driving detection systems.