IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Estimating VDT mental fatigue using multichannel linear descriptors and KPCA-HMM
EURASIP Journal on Advances in Signal Processing
Feature Selection with Kernel Class Separability
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers in Biology and Medicine
Heartbeat time series classification with support vector machines
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Nonlinear dynamical analysis of magnetic resonance spectroscopy data
IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
SDNN/RMSSD as a surrogate for LF/HF: a revised investigation
Modelling and Simulation in Engineering
Expert Systems with Applications: An International Journal
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This article presents a successfully developed methodology for mining physiological conditions from heart rate variability (HRV) analysis. The application of HRV analysis in both research and clinical settings has seen rapid development in the past decades. Unlike previous research, this study employed features derived from longterm monitoring of HRV indices, as these trends can best reflect the autonomic nervous system dynamics influenced by various physiological conditions. We proposed two methods for mining physiological conditions from HRV trends: a decision-tree learning method and a hybrid learning method that combines feature selection, feature extraction, and classifier construction processes. The proposed methods have been validated through a clinical case study: severity classification for Parkinson's disease. Our approach yielded classification accuracy greater than 90.0%, and high sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV).