An online sleep apnea detection method based on recurrence quantification analysis

Hoa Dinh Nguyen, Brek A. Wilkins, Qi Cheng, Bruce Allen Benjamin

Research output: Contribution to journalArticlepeer-review

97 Scopus citations


This paper introduces an online sleep apnea detection method based on heart rate complexity as measured by recurrence quantification analysis (RQA) statistics of heart rate variability (HRV) data. RQA statistics can capture nonlinear dynamics of a complex cardiorespiratory system during obstructive sleep apnea. In order to obtain a more robust measurement of the nonstationarity of the cardiorespiratory system, we use different fixed amount of neighbor thresholdings for recurrence plot calculation. We integrate a feature selection algorithm based on conditional mutual information to select the most informative RQA features for classification, and hence, to speed up the real-time classification process without degrading the performance of the system. Two types of binary classifiers, i.e., support vector machine and neural network, are used to differentiate apnea from normal sleep. A soft decision fusion rule is developed to combine the results of these classifiers in order to improve the classification performance of the whole system. Experimental results show that our proposed method achieves better classification results compared with the previous recurrence analysis-based approach. We also show that our method is flexible and a strong candidate for a real efficient sleep apnea detection system.

Original languageEnglish
Article number6676792
Pages (from-to)1285-1293
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Issue number4
StatePublished - Jul 2014


  • Feature selection
  • recurrence quantification analysis (RQA)
  • sleep apnea detection
  • soft decision fusion


Dive into the research topics of 'An online sleep apnea detection method based on recurrence quantification analysis'. Together they form a unique fingerprint.

Cite this