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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

2.4K
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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Cardiomyopathy I: Introduction and Classification01:25

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Cardiomyopathy, or CMP, is a group of diseases affecting the myocardial structure, impairing its ability to pump blood effectively. This condition can lead to arrhythmias, heart failure, or sudden cardiac death.Cardiomyopathies are classified into primary and secondary categories:Primary Cardiomyopathy refers to conditions involving only the heart muscle that are often idiopathic (of unknown cause) or genetic. They primarily affect the myocardium without the involvement of other systemic...
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Related Experiment Video

Updated: Jul 12, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Electrocardiogram-based deep learning model to screen peripartum cardiomyopathy.

Young Mi Jung1, Sora Kang2, Jeong Min Son2

  • 1Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea Drs Jung, C Park, J Park, Jun, and S Lee); Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea (Drs Jung and S Lee); Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee).

American Journal of Obstetrics & Gynecology MFM
|October 20, 2023
PubMed
Summary

Artificial intelligence algorithms using 12-lead and 1-lead electrocardiography show high effectiveness in screening for peripartum cardiomyopathy. These noninvasive tools can aid in the early detection of this critical condition in pregnant women.

Keywords:
artificial intelligence/machine learning modelelectrocardiographyheart diseaseleft ventricular systolic dysfunctionperipartum cardiomyopathy

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Area of Science:

  • Cardiology
  • Medical Technology
  • Artificial Intelligence

Background:

  • Peripartum cardiomyopathy is a life-threatening condition causing heart failure in pregnant women.
  • Electrocardiography abnormalities can indicate left ventricular dysfunction, but its role in peripartum cardiomyopathy screening is unclear.

Purpose of the Study:

  • To evaluate the effectiveness of AI/ML-based software using 12-lead ECG for screening peripartum cardiomyopathy.
  • To assess a 1-lead ECG AI/ML algorithm for detecting left ventricular systolic dysfunction in peripartum cardiomyopathy.

Main Methods:

  • Retrospective cohort study of pregnant women undergoing echocardiography and ECG.
  • Evaluation of AiTiALVSD (12-lead ECG) and a 1-lead ECG AI/ML algorithm for peripartum cardiomyopathy detection.
  • Analysis of performance metrics including AUC, sensitivity, specificity, and predictive values.

Main Results:

  • The 12-lead ECG AI/ML software (AiTiALVSD) demonstrated high effectiveness (AUC 0.979) in identifying peripartum cardiomyopathy.
  • A 1-lead ECG AI/ML algorithm also showed excellent performance (AUC 0.944) in detecting the condition.
  • Both methods exhibited high sensitivity and specificity for screening peripartum cardiomyopathy.

Conclusions:

  • AI/ML analysis of 12-lead and 1-lead ECGs are effective, noninvasive screening tools for peripartum cardiomyopathy.
  • These algorithms show potential for widespread use in identifying peripartum cardiomyopathy during the peripartum period.