Sleep apnea detection from ECG signal: Analysis on optimal features, principal components, and nonlinearity

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Citations (Scopus)

Abstract

This paper describes implementation of Principal Component Analysis (PCA) on sleep apnea detection using Electrocardiogram (ECG) signal. The statistics of RR-intervals per epoch with 1 minute duration were used as an input. The combination of features proposed by Chazal and Yilmaz was transformed into orthogonal features using PCA. Cross validation, random sampling, and test on train data were used on model selection. The results of classification using kNN, Naïve- Bayes, and Support Vector Machine (SVM) show that PCA features give better classification accuracy compared to Chazal and Yilmaz features. SVM with RBF (Radial Basis Function) kernel gives the best classification accuracy by using 7 principal components (PC) as a features. The experimental results show that relation between Chazal features with target class tend to be linear, but Yilmaz and PCA features are non-linear.

Original languageEnglish
Title of host publication5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011
DOIs
Publication statusPublished - 14 Jul 2011
Event5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011 - Wuhan, China
Duration: 10 May 201112 May 2011

Publication series

Name5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011

Conference

Conference5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011
Country/TerritoryChina
CityWuhan
Period10/05/1112/05/11

Keywords

  • Feature selection
  • Nonlinearity
  • PCA
  • Sleep apnea

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