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

doi:10.1109/IJCNN.2012.6252716

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 proceeding › Conference contribution

6 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 language	English
Title of host publication	2012 International Joint Conference on Neural Networks, IJCNN 2012
DOIs	https://doi.org/10.1109/IJCNN.2012.6252716
Publication status	Published - 2012
Event	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 Duration: 2012 Jun 10 → 2012 Jun 15

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Other

Other	2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
Country/Territory	Australia
City	Brisbane, QLD
Period	12-06-10 → 12-06-15

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

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10.1109/IJCNN.2012.6252716

Cite this

@inproceedings{21e53a85979646e1a37693f68bcb95a5,

title = "Using Bootstrap AdaBoost with KNN for ECG-based automated obstructive sleep apnea detection",

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.",

author = "Kao, \{Tzu Ping\} and Wang, \{Jeen Shing\} and Lin, \{Che Wei\} and Yang, \{Ya Ting\} and Juang, \{Fang Chen\}",

year = "2012",

doi = "10.1109/IJCNN.2012.6252716",

language = "English",

isbn = "9781467314909",

series = "Proceedings of the International Joint Conference on Neural Networks",

booktitle = "2012 International Joint Conference on Neural Networks, IJCNN 2012",

note = "2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 ; Conference date: 10-06-2012 Through 15-06-2012",

}

Kao, TP, Wang, JS , Lin, CW , Yang, YT & Juang, FC 2012, Using Bootstrap AdaBoost with KNN for ECG-based automated obstructive sleep apnea detection. in 2012 International Joint Conference on Neural Networks, IJCNN 2012., 6252716, Proceedings of the International Joint Conference on Neural Networks, 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, 12-06-10. https://doi.org/10.1109/IJCNN.2012.6252716

Using Bootstrap AdaBoost with KNN for ECG-based automated obstructive sleep apnea detection. / Kao, Tzu Ping; Wang, Jeen Shing ; Lin, Che Wei et al.
2012 International Joint Conference on Neural Networks, IJCNN 2012. 2012. 6252716 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Using Bootstrap AdaBoost with KNN for ECG-based automated obstructive sleep apnea detection

AU - Kao, Tzu Ping

AU - Wang, Jeen Shing

AU - Lin, Che Wei

AU - Yang, Ya Ting

AU - Juang, Fang Chen

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

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DO - 10.1109/IJCNN.2012.6252716

M3 - Conference contribution

AN - SCOPUS:84865091751

SN - 9781467314909

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2012 International Joint Conference on Neural Networks, IJCNN 2012

T2 - 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012

Y2 - 10 June 2012 through 15 June 2012

ER -

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

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