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Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
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Left ventricular hypertrophy detection using electrocardiographic signal.

Cheng-Wei Liu1, Fu-Hsing Wu2, Yu-Lun Hu3

  • 1Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital Songshan Branch, National Defense Medical Center, Taipei, Taiwan.

Scientific Reports
|February 13, 2023
PubMed
Summary

A novel back propagation neural network (BPN) system effectively detects Left Ventricular Hypertrophy (LVH) using electrocardiogram (ECG) signals. This AI approach shows high accuracy, outperforming traditional ECG criteria and other AI models for early cardiovascular disease detection.

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Left Ventricular Hypertrophy (LVH) signifies subclinical organ damage and is linked to cardiovascular disease incidence.
  • Electrocardiogram (ECG) is a cost-effective, non-invasive tool for preliminary heart disease diagnosis.
  • Current ECG criteria for LVH rely on voltage thresholds of RS peaks.

Purpose of the Study:

  • To develop and evaluate an automated system for detecting LVH using ECG signals.
  • To compare the performance of the developed system against traditional ECG criteria and existing AI models.

Main Methods:

  • A two-step system was developed: 1) Extraction of 24 features from R-peak and S-valley amplitudes of 12-lead ECG, followed by ECG beat segmentation. 2) Training a back propagation neural network (BPN) with these features.
  • Echocardiography (ECHO) served as the gold standard for LVH diagnosis.
  • The BPN model was trained and tested on a dataset from a Taiwanese population, including 173 LVH cases and 1466 segmented ECG cycles.

Main Results:

  • The BPN model achieved high testing performance: accuracy (0.961), precision (0.958), sensitivity (0.966), and specificity (0.956).
  • The developed BPN system demonstrated superior detection performance compared to 7 traditional ECG criteria methods.
  • Performance also surpassed previously reported ECG-based artificial intelligence (AI) models for LVH detection.

Conclusions:

  • The developed BPN system offers a highly accurate and efficient method for detecting LVH from ECG signals.
  • This AI-driven approach shows significant potential for improving early diagnosis of cardiovascular conditions.
  • The system's performance suggests a valuable advancement over conventional diagnostic methods for LVH.