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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

3.2K
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...
3.2K
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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Related Experiment Video

Updated: Sep 13, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Explainable machine learning for neoplasms diagnosis via electrocardiograms: an externally validated study.

Juan Miguel Lopez Alcaraz1, Wilhelm Haverkamp2, Nils Strodthoff3

  • 1AI4Health Division, Carl von Ossietzky Universität Oldenburg, Ammerländer Heerstraße 114-118, Oldenburg, Lower Saxony, 26129, Germany.

Cardio-Oncology (London, England)
|July 27, 2025
PubMed
Summary

This study shows that electrocardiogram (ECG) data, combined with machine learning, can non-invasively diagnose neoplasms. This cost-effective method identifies cardiovascular changes linked to cancer, improving early detection, especially in resource-limited settings.

Keywords:
Electrocardiogram (ECG)Explainable Artificial Intelligence (XAI)Machine LearningNeoplasm diagnosis

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Area of Science:

  • Cardiovascular Medicine
  • Oncology
  • Artificial Intelligence in Healthcare

Background:

  • Neoplasms are a leading global cause of mortality, necessitating early and accessible diagnostic tools.
  • Current diagnostic methods for neoplasms are often invasive, costly, and not widely accessible.
  • Electrocardiogram (ECG) data presents a non-invasive, widely available alternative for neoplasm detection.

Purpose of the Study:

  • To explore the potential of ECG data for non-invasive neoplasm diagnosis.
  • To develop and validate a machine learning model for identifying neoplasms using ECG signals.
  • To investigate the cardiovascular changes associated with neoplastic presence and therapies.

Main Methods:

  • A diagnostic pipeline integrating tree-based machine learning models and Shapley value analysis for interpretability was developed.
  • The model underwent rigorous internal validation on a large dataset and external validation on an independent cohort.
  • Key ECG features driving diagnostic predictions were identified and analyzed for clinical relevance.

Main Results:

  • The developed model demonstrated high diagnostic accuracy in both internal and external validation cohorts.
  • Shapley value analysis identified significant ECG features, including novel predictors of neoplasms.
  • The approach proved cost-effective, scalable, and suitable for resource-limited settings.

Conclusions:

  • The study confirms the feasibility of using ECG signals and machine learning for non-invasive neoplasm diagnosis.
  • The method offers interpretable insights into the complex cardio-neoplasm interactions.
  • This approach can address diagnostic gaps and be integrated into existing diagnostic and therapeutic frameworks.