Mining physiological conditions from heart rate variability analysis

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)


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).

Original languageEnglish
Article number5386095
Pages (from-to)50-58
Number of pages9
JournalIEEE Computational Intelligence Magazine
Issue number1
Publication statusPublished - 2010 Feb

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Artificial Intelligence

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