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Related Concept Videos

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...
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
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...
Instrumentation Amplifier01:25

Instrumentation Amplifier

An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...

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Related Experiment Video

Updated: May 27, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
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BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

CODE-II: a large-scale dataset for artificial intelligence in ECG analysis.

Petrus E O G B Abreu1, Gabriela M M Paixão2, Jiawei Li3

  • 1Faculdade de Medicina, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil. petrusabreu@ufmg.br.

NPJ Digital Medicine
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

A new large-scale dataset, CODE-II, aids artificial intelligence (AI) in electrocardiogram (ECG) interpretation. AI models trained on CODE-II show improved performance on external benchmarks, advancing automated ECG analysis.

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Advancements in artificial intelligence (AI) for electrocardiogram (ECG) interpretation are hindered by limitations in dataset annotation quality, size, and scope.
  • Developing robust AI models for ECG analysis requires large, well-annotated, and diverse datasets.

Purpose of the Study:

  • To introduce CODE-II, a large-scale, real-world dataset of 12-lead ECGs for AI-driven interpretation.
  • To provide standardized annotations and clinically relevant diagnostic classes for ECG analysis.
  • To facilitate the development and benchmarking of AI algorithms for ECG interpretation.

Main Methods:

  • Collected 2,735,269 12-lead ECGs from 2,093,807 adult patients via the Telehealth Network of Minas Gerais (TNMG), Brazil.
  • Annotated each ECG using standardized diagnostic criteria, with reviews conducted by cardiologists.
  • Developed a set of 66 clinically meaningful diagnostic classes based on cardiologist input and telehealth practice.
  • Created openly available subsets: CODE-II-open (15,000 patients) and CODE-II-test (8,475 exams) for public use and blinded evaluation.

Main Results:

  • The CODE-II dataset comprises over 2.7 million ECGs with comprehensive annotations and 66 diagnostic classes.
  • A neural network pre-trained on CODE-II demonstrated superior transfer learning performance on external benchmarks (PTB-XL and CPSC 2018).
  • Models trained on CODE-II outperformed those trained on larger, albeit less comprehensively annotated, datasets.

Conclusions:

  • CODE-II represents a significant resource for advancing AI in ECG interpretation, addressing current dataset limitations.
  • The dataset's scale, annotation quality, and defined diagnostic classes facilitate the development of more accurate and reliable AI diagnostic tools.
  • Pre-training on CODE-II enhances AI model generalizability and performance on diverse ECG interpretation tasks.