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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

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 the T...
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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...
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin to...
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

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...
Cardiopulmonary Resuscitation III: AED Use01:23

Cardiopulmonary Resuscitation III: AED Use

Introduction to AEDAn Automated External Defibrillator (AED) is a portable medical device that analyzes the heart's rhythm and, if necessary, delivers an electrical shock to help the heart re-establish an effective rhythm during sudden cardiac arrest (SCA). SCA occurs when the heart suddenly and unexpectedly stops beating, leading to a loss of blood flow to the brain and other vital organs. In such emergencies, time is of the essence, and using an AED, combined with Cardiopulmonary...

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Related Experiment Video

Updated: Jun 12, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

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Explainable artificial intelligence in electrocardiography: A systematic review.

Amirsajjad Taleban1, Rodney Sparapani2, Patrick Noffke3

  • 1Health Informatics Program, Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.

Biomedical Signal Processing and Control
|April 9, 2026
PubMed
Summary
This summary is machine-generated.

Explainable AI (Artificial Intelligence) methods for electrocardiography (ECG) show promise but require further validation. Techniques highlighting model attention to physiological ECG intervals are most effective for clinical trust and adoption.

Keywords:
Clinical decision supportECGElectrocardiographyExplainable artificial intelligenceInterpretability

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Signal Processing

Background:

  • Electrocardiography (ECG) is crucial for diagnosing heart conditions.
  • Deep learning models achieve high accuracy in ECG interpretation but lack transparency.
  • Explainable AI (XAI) is essential for clinical trust and regulatory approval of AI in cardiology.

Purpose of the Study:

  • To systematically review and evaluate explainable AI techniques specifically for ECG interpretation.
  • To identify the most effective XAI methods for enhancing trust and clinical adoption of AI in cardiology.

Main Methods:

  • Systematic literature review following PRISMA guidelines.
  • Screened 380 records across six databases, including 45 peer-reviewed studies.
  • Analyzed diverse XAI methods: perturbation-based, gradient-based, intrinsically interpretable, sequence-aware, and counterfactual.

Main Results:

  • Perturbation-based XAI methods are suboptimal for ECG due to their inability to capture temporal dependencies.
  • Sequence-aware methods revealing model attention to physiological ECG intervals (P wave, QRS complex, ST segment) show superior performance.
  • Current XAI methods for ECG are fragmented, lack robust validation, and face challenges in stability and efficiency.

Conclusions:

  • XAI methods that align with physiological ECG features are most promising for clinical translation.
  • Further development requires open, multi-institutional benchmarks and clinician-in-the-loop validation.
  • Accelerating clinical integration of XAI in ECG interpretation necessitates addressing stability, efficiency, and regulatory readiness.