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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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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Related Experiment Video

Updated: Nov 4, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

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An Interpretable Hand-Crafted Feature-Based Model for Atrial Fibrillation Detection.

Rahimeh Rouhi1, Marianne Clausel2, Julien Oster3

  • 1Université de Lorraine, CNRS, LORIA, Nancy, France.

Frontiers in Physiology
|May 31, 2021
PubMed
Summary
This summary is machine-generated.

This study enhances Atrial Fibrillation (AF) detection using interpretable machine learning. SHAP and Random Forest offer cardiologists tangible insights for improved diagnosis of this common cardiac arrhythmia.

Keywords:
atrial fibrillationclassificationcomputer-aided diagnosisfeature importancefeature selectioninterpretability

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Signal Processing

Background:

  • Atrial Fibrillation (AF) is a prevalent cardiac arrhythmia impacting patient prognosis.
  • Early AF diagnosis is crucial for effective treatment and improved outcomes.
  • Machine Learning (ML) enhances Computer-Aided Diagnosis (CADx) for AF detection.

Purpose of the Study:

  • To apply and evaluate explanation techniques for ML models in heart rhythm classification.
  • To assess the interpretability and diagnostic utility of ML models for cardiologists.
  • To compare the performance of different explanation techniques on hand-crafted features for AF detection.

Main Methods:

  • Utilized hand-crafted features from Electrocardiogram (ECG) signals.
  • Applied various explanation techniques, including SHapley Additive exPlanations (SHAP).
  • Employed Random Forest (RF) and Support Vector Machine (SVM) classifiers on the 2017 CinC/PhysioNet challenge dataset.

Main Results:

  • The SHAP technique combined with RF achieved a mean F-score of 0.746 for AF detection.
  • This performance surpassed the cascaded SVM approach (mean F-score of 0.706) using the same features.
  • The interpretable model provided cardiologists with a compact feature set and actionable diagnostic information.

Conclusions:

  • SHAP and RF offer an effective and efficient approach for interpretable AF detection from ECG signals.
  • Interpretable ML models can simplify complex algorithms, providing tangible diagnostic insights for clinicians.
  • This methodology aids in improving the accuracy and clinical utility of CADx systems for cardiac arrhythmias.