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Related Concept Videos

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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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...
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Electrocardiogram Fundamentals01:28

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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...
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Electrocardiogram01:29

Electrocardiogram

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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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Related Experiment Video

Updated: Oct 3, 2025

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End-to-End Depression Recognition Based on a One-Dimensional Convolution Neural Network Model Using Two-Lead ECG

Xiaohan Zang1, Baimin Li2, Lulu Zhao3

  • 1School of Control Science and Engineering, Shandong University, Jinan, China.

Journal of Medical and Biological Engineering
|February 14, 2022
PubMed
Summary

This study introduces a deep learning model using electrocardiogram (ECG) signals to identify depression. The convolutional neural network (CNN) achieved high accuracy, suggesting ECG as a potential biomarker for depression diagnosis.

Keywords:
CNNComputer-aided diagnosisDepressionECGInter-patient

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Area of Science:

  • Cardiology
  • Psychiatry
  • Artificial Intelligence

Background:

  • Depression is a prevalent global mental illness with diagnostic challenges.
  • Current depression diagnosis relies on subjective clinical experience, leading to inefficiencies.
  • There is an urgent need for objective, computer-aided diagnostic models.

Purpose of the Study:

  • To develop and evaluate a convolutional neural network (CNN) model for depression identification using electrocardiogram (ECG) signals.
  • To establish a physiological and psychological model for computer-aided depression diagnosis.
  • To investigate the potential of ECG as a biomarker for depression.

Main Methods:

  • Utilized a 1D CNN to process raw ECG signals, enabling automatic feature learning.
  • Compared ECG segments of varying durations (3-6s) and CNN architectures.
  • Employed an inter-patient data classification approach with 37 depressed patients and 37 healthy controls.

Main Results:

  • A 5-second ECG segment with a 5-layer CNN demonstrated optimal performance.
  • Achieved high classification accuracy of 93.96%, sensitivity of 89.43%, and specificity of 98.49%.
  • The model showed high positive predictive value (98.34%).

Conclusions:

  • An end-to-end deep learning approach effectively identifies depression from ECG signals.
  • The proposed method demonstrates high diagnostic performance for depression.
  • ECG signals show promise as a potential biomarker for depression diagnosis.