Classification of cardiac abnormalities using heart rate signals

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Published on: May 23, 2021

R Acharya1, A Kumar, P S Bhat

  • 1Department of ECE, Ngee Ann Polytechnic, Singapore. aru@np.edu.sg

Medical & Biological Engineering & Computing
|June 12, 2004
PubMed

Insights

Analyzing heart rate variation using artificial neural networks and fuzzy logic aids in diagnosing cardiac conditions. This computational approach achieves 80-85% accuracy in classifying cardiac rhythms, improving diagnostic efficiency.

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Computational Neuroscience

Background:

  • Heart rate (HR) is a non-stationary signal with variations indicating current or impending cardiac diseases.
  • Analyzing long-term HR data for abnormalities is time-consuming and strenuous.
  • Heart rate variation (HRV) measurement is a non-invasive tool for assessing autonomic nervous system function.

Purpose of the Study:

  • To develop and evaluate computer-based analytical tools for in-depth study and classification of day-long HR data.
  • To improve the efficiency and accuracy of cardiac rhythm classification for diagnostic purposes.

Main Methods:

  • Classification of cardiac rhythms using an artificial neural network (ANN).
  • Integration of fuzzy relationships with ANN for enhanced data analysis.
  • Utilizing instantaneous heart rate against time measurements.

Main Results:

  • The study achieved a high level of efficacy in classifying cardiac rhythms.
  • The developed tools demonstrated an accuracy level of 80-85% in classification tasks.
  • Computer-based analysis proved useful for diagnostics over day-long intervals.

Conclusions:

  • Artificial neural networks combined with fuzzy logic offer a highly effective method for cardiac rhythm classification.
  • The computational approach significantly aids in the non-invasive assessment of cardiac conditions.
  • This methodology enhances diagnostic capabilities for cardiovascular health monitoring.

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