Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neural networks: an introduction
Neural networks: an introduction
Neural network and fuzzy logic applications in C/C++
Neural network and fuzzy logic applications in C/C++
The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Learning from imbalanced data in surveillance of nosocomial infection
Artificial Intelligence in Medicine
Computer Methods and Programs in Biomedicine
Computers in Biology and Medicine
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
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Objective: Paroxysmal atrial fibrillation (PAF) is a serious arrhythmia associated with morbidity and mortality. We explore the possibility of distant prediction of PAF by analyzing changes in heart rate variability (HRV) dynamics of non-PAF rhythms immediately before PAF event. We use that model for distant prognosis of PAF onset with artificial intelligence methods. Methods and materials: We analyzed 30-min non-PAF HRV records from 51 subjects immediately before PAF onset and at least 45min distant from any PAF event. We used spectral and complexity analysis with sample (SmEn) and approximate (ApEn) entropies and their multiscale versions on extracted HRV data. We used that features to train the artificial neural networks (ANNs) and support vector machine (SVM) classifiers to differentiate the subjects. The trained classifiers were further tested for distant PAF event prognosis on 16 subjects from independent database on non-PAF rhythm lasting from 60 to 320min before PAF onset classifying the 30-min segments as distant or leading to PAF. Results: We found statistically significant increase in 30-min non-PAF HRV recordings from 51 subjects in the VLF, LF, HF bands and total power (p