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Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee

Pantea Keikhosrokiani1, A Bhanupriya Naidu A/P Anathan1, Suzi Iryanti Fadilah1

  • 1School of Computer Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia.

Digital Health
|January 19, 2023
PubMed

Insights

This study introduces an optimized Adaptive Neuro-Fuzzy Inference System (ANFIS) using an artificial bee colony (ABC) algorithm for accurate heart sound classification. The novel ABC-ANFIS model achieved 93% accuracy in detecting murmur sounds, aiding early cardiovascular disease diagnosis.

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Cardiovascular diseases are a leading global cause of mortality.
  • Accurate diagnosis of heart conditions often relies on detecting abnormal heart sounds (murmurs).
  • Traditional stethoscope auscultation requires extensive physician training and can be challenging for less experienced practitioners.

Purpose of the Study:

  • To develop a highly accurate automated method for classifying heart sounds, specifically detecting murmurs.
  • To improve the efficiency and reliability of cardiovascular disease diagnosis through advanced data analytics.
  • To introduce a novel optimized Adaptive Neuro-Fuzzy Inference System (ANFIS) model for heart sound analysis.

Main Methods:

  • Heartbeat sound data was collected, cleaned, and pre-processed.
  • Mel-Frequency Cepstral Coefficients (MFCC) were extracted as features from the heart sounds.
  • An Artificial Bee Colony (ABC) algorithm was employed to optimize the ANFIS model for classification.

Main Results:

  • The proposed ABC-ANFIS model demonstrated a high accuracy of 93% for classifying the murmur heart sound class.
  • The ABC-ANFIS model outperformed existing methods including ANFIS, PSO ANFIS, Support Vector Machine (SVM), KSTM, and K-Nearest Neighbors (KNN).
  • The optimized ANFIS approach significantly improved the accuracy of heart sound classification compared to conventional techniques.

Conclusions:

  • The ABC-ANFIS model offers a promising, accurate, and automated solution for heart sound classification.
  • This approach can assist physicians in the early detection of cardiovascular diseases by reliably identifying murmurs.
  • The study highlights the potential of optimized neuro-fuzzy systems in enhancing diagnostic capabilities in cardiology.

Keywords:
Heartbeat soundadaptive neuro-Fuzzy inferences systemartificial bee colonyclassificationoptimization

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