@inproceedings{00eae7183a5142d5b2620fb1b736412b,
title = "Sleep apnea detection from ECG signal: Analysis on optimal features, principal components, and nonlinearity",
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, Na{\"i}ve- 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.",
keywords = "Feature selection, Nonlinearity, PCA, Sleep apnea",
author = "Isa, {Sani M.} and Fanany, {Mohamad Ivan} and Wisnu Jatmiko and Arymurthy, {Aniati Murni}",
year = "2011",
doi = "10.1109/icbbe.2011.5780285",
language = "English",
isbn = "9781424450893",
series = "5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011",
booktitle = "5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011",
note = "5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011 ; Conference date: 10-05-2011 Through 12-05-2011",
}