Using Bootstrap AdaBoost with KNN for ECG-based automated obstructive sleep apnea detection

Tzu Ping Kao, Jeen Shing Wang, Che Wei Lin, Ya Ting Yang, Fang Chen Juang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This paper presents an integrated Bootstrap AdaBoost with k- nearest neighbor (KNN) algorithm for obstructive sleep apnea (OSA) screening based on electrocardiogram (ECG) recordings during sleep. The proposed method processes single-lead ECG recordings for predicting the presence of major sleep apnea and provides a minute-by-minute analysis of disordered breathing. In our analysis, 35 recordings collected from the Physionet Apnea-ECG database were used as the training/testing dataset. A variety of features based on RR interval, an ECG-derived respiratory signal, and cardiopulmonary coupling techniques were employed. A Bootstrap AdaBoost with k-dimensional tree KNN was used as the classifier, adopting feature selection to optimize classifier performance. The Bootstrap AdaBoost with KDKNN (BA-KDKNN) algorithm reached an accuracy of 91.95%, sensitivity of 99.36%, and specificity of up to 89.02% with ten features.

Original languageEnglish
Title of host publication2012 International Joint Conference on Neural Networks, IJCNN 2012
DOIs
Publication statusPublished - 2012 Aug 22
Event2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia
Duration: 2012 Jun 102012 Jun 15

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
CountryAustralia
CityBrisbane, QLD
Period12-06-1012-06-15

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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