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

研究成果: Conference contribution

4 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2012 International Joint Conference on Neural Networks, IJCNN 2012
DOIs
出版狀態Published - 2012 八月 22
事件2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia
持續時間: 2012 六月 102012 六月 15

出版系列

名字Proceedings 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
國家Australia
城市Brisbane, QLD
期間12-06-1012-06-15

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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