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Related Concept Videos

Cardiac Output II: Effect of Stroke Volume on Cardiac Output01:22

Cardiac Output II: Effect of Stroke Volume on Cardiac Output

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Cardiac output (CO), the amount of blood the heart pumps per minute, is a parameter in cardiovascular physiology determined by stroke volume and heart rate. Stroke volume, the amount of blood pushed from one of the ventricles per heartbeat, is influenced by preload, afterload, and contractility.
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Mnemonic devices are cognitive tools that facilitate memory retention by linking new information to familiar patterns or organizational strategies. These techniques are beneficial for remembering complex or lengthy sets of information by simplifying and structuring them in easily retrievable ways.
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Non-ohmic Devices00:51

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In most substances, the current flow is proportional to the voltage applied to it. A simple relationship between the values of current, voltage, and resistance is known as Ohm's law. Nonohmic devices do not exhibit a linear relationship between voltage and current. One such device is the semiconducting circuit element known as a diode. A diode is a circuit device that allows current flow in only one direction.
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Cardiac Output I:Effect of Heart Rate on Cardiac Output01:19

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Cardiac Output
Cardiac output (CO) refers to the total amount of blood ejected by one of the ventricles in liters per minute (L/min). In a resting adult, CO ranges from 5 to 6 L/min, adjusting according to the body's metabolic requirements.
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The cardiac cycle refers to the sequence of events that occur in the heart from the beginning of one heartbeat to the next. It's characterized by alternating periods of contraction (systole) and relaxation (diastole) of the heart muscles.
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The Cardiac Cycle

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The heart beats rhythmically in a sequence called the cardiac cycle—a rapid coordination of contraction (systole) and relaxation (diastole).
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Updated: Jan 22, 2026

Normothermic Cardiac Arrest and Cardiopulmonary Resuscitation: A Mouse Model of Ischemia-Reperfusion Injury
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Normothermic Cardiac Arrest and Cardiopulmonary Resuscitation: A Mouse Model of Ischemia-Reperfusion Injury

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Contactless cardiac arrest detection using smart devices.

Justin Chan1, Thomas Rea2,3, Shyamnath Gollakota1

  • 11Paul G. Allen School of Computer Science and Engineering, University of Washington, Washington, WA USA.

NPJ Digital Medicine
|July 16, 2019
PubMed
Summary
This summary is machine-generated.

A new AI system can detect agonal breathing, an early sign of cardiac arrest, using smart devices. This technology aims to improve survival rates for unwitnessed cardiac events at home.

Keywords:
Computer scienceDiagnostic markers

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Emergency Medicine

Background:

  • Out-of-hospital cardiac arrest is a major global cause of death.
  • Early cardiopulmonary resuscitation (CPR) is critical, but unwitnessed events, often at home, limit survival.
  • Agonal breathing, a sign of severe hypoxia, is an under-recognized diagnostic marker.

Purpose of the Study:

  • To develop and validate a machine learning model for real-time detection of agonal breathing.
  • To assess the system's accuracy and reliability in diverse bedroom environments.
  • To prototype a contactless system for identifying cardiac arrest using commodity smart devices.

Main Methods:

  • A support vector machine (SVM) was trained using real-world 9-1-1 audio data of cardiac arrests.
  • The SVM was evaluated on polysomnographic sleep lab data and real-world home sleep environment data.
  • A proof-of-concept system was prototyped using smart devices (Amazon Echo, iPhone).

Main Results:

  • The SVM achieved high accuracy in classifying agonal breathing (AUC of 0.9993).
  • The system demonstrated excellent sensitivity (97.24%) and specificity (99.51%).
  • Low false positive rates were observed in both lab (0-0.14%) and home (0-0.22%) environments.

Conclusions:

  • AI-powered detection of agonal breathing is feasible and accurate in home environments.
  • This technology has the potential to significantly improve early diagnosis and intervention for cardiac arrest.
  • Contactless monitoring using smart devices offers a promising approach for out-of-hospital cardiac arrest detection.