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Entropy-based reliable non-invasive detection of coronary microvascular dysfunction using machine learning algorithm.

Xiaoye Zhao1,2,3, Yinlan Gong4, Lihua Xu5

  • 1School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei 230009, Anhui, China.

Mathematical Biosciences and Engineering : MBE
|July 28, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning algorithms using electrocardiogram (ECG) data can effectively detect coronary microvascular dysfunction (CMD). This non-invasive approach shows promise for early patient-specific diagnosis of CMD.

Keywords:
coronary microvascular dysfunction (CMD)electrocardiogram (ECG)entropymachine learningmyocardial ischemiavectorcardiogram (VCG)

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Data Science

Background:

  • Coronary microvascular dysfunction (CMD) is an increasingly recognized cause of myocardial ischemia.
  • Current diagnostic methods for CMD lack non-invasive capabilities for early detection.

Purpose of the Study:

  • To develop a machine learning algorithm utilizing electrocardiogram (ECG) data for the non-invasive detection of CMD.
  • To establish a foundation for patient-specific, early-stage CMD diagnosis.

Main Methods:

  • Vectorcardiography (VCG) was derived from 10-second ECG recordings of patients with CMD and healthy controls.
  • Multiscale entropy analysis (Sample entropy, Approximate entropy, Complexity index) was performed on ST-T segments.
  • Machine learning models were trained and validated using sequential backward selection and five-fold cross-validation.

Main Results:

  • The Approximate entropy (ApEn)-based Support Vector Machine (SVM) model demonstrated optimal performance.
  • The ApEn-based SVM model achieved evaluation metrics exceeding 0.8 in both intra-patient and inter-patient schemes.
  • The study identified the most effective entropy features for CMD detection.

Conclusions:

  • Entropy analysis of ECG and VCG signals can effectively detect CMD.
  • The developed machine learning models offer a potential non-invasive, ECG-based tool for CMD detection.
  • This research paves the way for early, patient-specific diagnosis and management of CMD.