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Electrocardiogram

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...

Electrocardiogram Fundamentals

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...

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

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...

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Updated: Nov 8, 2025

Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure

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Published on: February 8, 2022

Diagnosing Atrial Septal Defect from Electrocardiogram with Deep Learning.

Hiroki Mori^1,2, Kei Inai¹, Hisashi Sugiyama¹

¹Department of Pediatric Cardiology, Tokyo Women's Medical University Hospital, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-0054, Japan.

Pediatric Cardiology

|April 28, 2021

Summary

This summary is machine-generated.

A new deep learning model combining convolutional neural networks (CNN) and long short-term memory (LSTM) significantly improves the accuracy of diagnosing atrial septal defects using electrocardiograms.

Keywords:

Atrial septal defect Deep learning Electrocardiogram

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Published on: July 20, 2022

Area of Science:

Cardiology
Artificial Intelligence in Medicine
Medical Diagnostics

Background:

Atrial septal defects (ASDs) often present with faint heart murmurs, making diagnosis challenging.
Electrocardiogram (ECG) findings for ASDs can be difficult to identify, necessitating advanced diagnostic tools.

Purpose of the Study:

To evaluate the diagnostic performance of a novel deep learning model for identifying atrial septal defects using ECG data.
To compare the diagnostic accuracy of the deep learning model against that of pediatric cardiologists.

Main Methods:

A retrospective observational study utilizing 1192 ECGs from 728 participants (2000-2017).
A deep learning model integrating convolutional neural networks (CNN) and long short-term memory (LSTM) was developed.
ECG data from patients with confirmed ASDs (via echocardiography) and healthy controls were analyzed.
The model's diagnostic ability was compared with the interpretations of 12 pediatric cardiologists blinded to patient status.

Main Results:

The deep learning model achieved high diagnostic performance: accuracy (0.89), sensitivity (0.76), specificity (0.96), positive predictive value (0.88), and F1 score (0.81).
Pediatric cardiologists achieved significantly lower mean scores: accuracy (0.58 ± 0.06), sensitivity (0.53 ± 0.04), specificity (0.67 ± 0.10), positive predictive value (0.69 ± 0.18), and F1 score (0.58 ± 0.06).

Conclusions:

The proposed deep learning model demonstrates superior accuracy in diagnosing atrial septal defects from ECGs compared to human expert interpretation.
This AI-driven approach offers a promising advancement for the early and accurate detection of ASDs.