Detection of obstructive sleep apnea in pediatric subjects using surface lead electrocardiogram features. Shouldice, R., B., O'Brien, L., M., O'Brien, C., de Chazal, P., Gozal, D., & Heneghan, C. Sleep, 27(4):784-92, 6, 2004. Paper abstract bibtex STUDY OBJECTIVES: To investigate the feasibility of detecting obstructive sleep apnea (OSA) in children using an automated classification system based on analysis of overnight electrocardiogram (ECG) recordings. DESIGN: Retrospective observational study. SETTING: A pediatric sleep clinic. PARTICIPANTS: Fifty children underwent full overnight polysomnography. INTERVENTION: N/A. MEASUREMENTS AND RESULTS: Expert polysomnography scoring was performed. The datasets were divided into a training set of 25 subjects (11 normal, 14 with OSA) and a withheld test set of 25 subjects (11 normal, 14 with OSA). Features, calculated from the ECG of the 25 training datasets, were empirically chosen to train a modified quadratic discriminant analysis classification system. The selected configuration used a segment length of 60 seconds and processed mean, SD, power spectral density, and serial correlation measures to classify segments as apneic or normal. By combining per-segment classifications and using receiver-operator characteristic analysis, a per-subject classifier was obtained that had a sensitivity of 85.7%, specificity of 90.9%, and accuracy of 88% on the training datasets. The same decision threshold was applied to the withheld datasets and yielded a sensitivity of 85.7%, specificity of 81.8%, and accuracy of 84%. The positive and negative predictive values were 85.7% and 81.8%, respectively, on the test dataset. CONCLUSIONS: The ability to correctly identify 12 out of 14 cases of OSA (with the 2 false negatives arising from subjects with an apnea-hypopnea index less than 10) indicates that the automated apnea classification system outlined may have clinical utility in pediatric patients.
@article{
id = {f8484443-f651-3495-a8e6-29c57d65e4b0},
title = {Detection of obstructive sleep apnea in pediatric subjects using surface lead electrocardiogram features.},
type = {article},
year = {2004},
identifiers = {[object Object]},
keywords = {Adult,Body Mass Index,Electrocardiography,Female,Humans,Male,Observation,Obstructive,Obstructive: diagnosis,Polysomnography,Retrospective Studies,Sleep Apnea},
created = {2010-08-14T01:08:40.000Z},
pages = {784-92},
volume = {27},
websites = {http://www.ncbi.nlm.nih.gov/pubmed/15283015},
month = {6},
file_attached = {true},
profile_id = {fe7067eb-58b8-34c6-b8cd-6717fdf7605c},
group_id = {ba0deb47-e19a-3151-83cc-b6262d5edb6e},
last_modified = {2014-07-19T19:17:36.000Z},
read = {true},
starred = {false},
authored = {false},
confirmed = {true},
hidden = {false},
citation_key = {Shouldice2004b},
client_data = {"desktop_id":"964ad333-289c-4449-9f56-3a11f86ca341"},
abstract = {STUDY OBJECTIVES: To investigate the feasibility of detecting obstructive sleep apnea (OSA) in children using an automated classification system based on analysis of overnight electrocardiogram (ECG) recordings. DESIGN: Retrospective observational study. SETTING: A pediatric sleep clinic. PARTICIPANTS: Fifty children underwent full overnight polysomnography. INTERVENTION: N/A. MEASUREMENTS AND RESULTS: Expert polysomnography scoring was performed. The datasets were divided into a training set of 25 subjects (11 normal, 14 with OSA) and a withheld test set of 25 subjects (11 normal, 14 with OSA). Features, calculated from the ECG of the 25 training datasets, were empirically chosen to train a modified quadratic discriminant analysis classification system. The selected configuration used a segment length of 60 seconds and processed mean, SD, power spectral density, and serial correlation measures to classify segments as apneic or normal. By combining per-segment classifications and using receiver-operator characteristic analysis, a per-subject classifier was obtained that had a sensitivity of 85.7%, specificity of 90.9%, and accuracy of 88% on the training datasets. The same decision threshold was applied to the withheld datasets and yielded a sensitivity of 85.7%, specificity of 81.8%, and accuracy of 84%. The positive and negative predictive values were 85.7% and 81.8%, respectively, on the test dataset. CONCLUSIONS: The ability to correctly identify 12 out of 14 cases of OSA (with the 2 false negatives arising from subjects with an apnea-hypopnea index less than 10) indicates that the automated apnea classification system outlined may have clinical utility in pediatric patients.},
bibtype = {article},
author = {Shouldice, Redmond B and O'Brien, Louise M and O'Brien, Ciara and de Chazal, Philip and Gozal, David and Heneghan, Conor},
journal = {Sleep},
number = {4}
}
Downloads: 0
{"_id":"NTxSDbYSvzkTeSXk8","authorIDs":[],"author_short":["Shouldice, R., B.","O'Brien, L., M.","O'Brien, C.","de Chazal, P.","Gozal, D.","Heneghan, C."],"bibbaseid":"shouldice-obrien-obrien-dechazal-gozal-heneghan-detectionofobstructivesleepapneainpediatricsubjectsusingsurfaceleadelectrocardiogramfeatures-2004","bibdata":{"id":"f8484443-f651-3495-a8e6-29c57d65e4b0","title":"Detection of obstructive sleep apnea in pediatric subjects using surface lead electrocardiogram features.","type":"article","year":"2004","identifiers":"[object Object]","keywords":"Adult,Body Mass Index,Electrocardiography,Female,Humans,Male,Observation,Obstructive,Obstructive: diagnosis,Polysomnography,Retrospective Studies,Sleep Apnea","created":"2010-08-14T01:08:40.000Z","pages":"784-92","volume":"27","websites":"http://www.ncbi.nlm.nih.gov/pubmed/15283015","month":"6","file_attached":"true","profile_id":"fe7067eb-58b8-34c6-b8cd-6717fdf7605c","group_id":"ba0deb47-e19a-3151-83cc-b6262d5edb6e","last_modified":"2014-07-19T19:17:36.000Z","read":"true","starred":"false","authored":"false","confirmed":"true","hidden":"false","citation_key":"Shouldice2004b","client_data":"\"desktop_id\":\"964ad333-289c-4449-9f56-3a11f86ca341\"","abstract":"STUDY OBJECTIVES: To investigate the feasibility of detecting obstructive sleep apnea (OSA) in children using an automated classification system based on analysis of overnight electrocardiogram (ECG) recordings. DESIGN: Retrospective observational study. SETTING: A pediatric sleep clinic. PARTICIPANTS: Fifty children underwent full overnight polysomnography. INTERVENTION: N/A. MEASUREMENTS AND RESULTS: Expert polysomnography scoring was performed. The datasets were divided into a training set of 25 subjects (11 normal, 14 with OSA) and a withheld test set of 25 subjects (11 normal, 14 with OSA). Features, calculated from the ECG of the 25 training datasets, were empirically chosen to train a modified quadratic discriminant analysis classification system. The selected configuration used a segment length of 60 seconds and processed mean, SD, power spectral density, and serial correlation measures to classify segments as apneic or normal. By combining per-segment classifications and using receiver-operator characteristic analysis, a per-subject classifier was obtained that had a sensitivity of 85.7%, specificity of 90.9%, and accuracy of 88% on the training datasets. The same decision threshold was applied to the withheld datasets and yielded a sensitivity of 85.7%, specificity of 81.8%, and accuracy of 84%. The positive and negative predictive values were 85.7% and 81.8%, respectively, on the test dataset. CONCLUSIONS: The ability to correctly identify 12 out of 14 cases of OSA (with the 2 false negatives arising from subjects with an apnea-hypopnea index less than 10) indicates that the automated apnea classification system outlined may have clinical utility in pediatric patients.","bibtype":"article","author":"Shouldice, Redmond B and O'Brien, Louise M and O'Brien, Ciara and de Chazal, Philip and Gozal, David and Heneghan, Conor","journal":"Sleep","number":"4","bibtex":"@article{\n id = {f8484443-f651-3495-a8e6-29c57d65e4b0},\n title = {Detection of obstructive sleep apnea in pediatric subjects using surface lead electrocardiogram features.},\n type = {article},\n year = {2004},\n identifiers = {[object Object]},\n keywords = {Adult,Body Mass Index,Electrocardiography,Female,Humans,Male,Observation,Obstructive,Obstructive: diagnosis,Polysomnography,Retrospective Studies,Sleep Apnea},\n created = {2010-08-14T01:08:40.000Z},\n pages = {784-92},\n volume = {27},\n websites = {http://www.ncbi.nlm.nih.gov/pubmed/15283015},\n month = {6},\n file_attached = {true},\n profile_id = {fe7067eb-58b8-34c6-b8cd-6717fdf7605c},\n group_id = {ba0deb47-e19a-3151-83cc-b6262d5edb6e},\n last_modified = {2014-07-19T19:17:36.000Z},\n read = {true},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Shouldice2004b},\n client_data = {\"desktop_id\":\"964ad333-289c-4449-9f56-3a11f86ca341\"},\n abstract = {STUDY OBJECTIVES: To investigate the feasibility of detecting obstructive sleep apnea (OSA) in children using an automated classification system based on analysis of overnight electrocardiogram (ECG) recordings. DESIGN: Retrospective observational study. SETTING: A pediatric sleep clinic. PARTICIPANTS: Fifty children underwent full overnight polysomnography. INTERVENTION: N/A. MEASUREMENTS AND RESULTS: Expert polysomnography scoring was performed. The datasets were divided into a training set of 25 subjects (11 normal, 14 with OSA) and a withheld test set of 25 subjects (11 normal, 14 with OSA). Features, calculated from the ECG of the 25 training datasets, were empirically chosen to train a modified quadratic discriminant analysis classification system. The selected configuration used a segment length of 60 seconds and processed mean, SD, power spectral density, and serial correlation measures to classify segments as apneic or normal. By combining per-segment classifications and using receiver-operator characteristic analysis, a per-subject classifier was obtained that had a sensitivity of 85.7%, specificity of 90.9%, and accuracy of 88% on the training datasets. The same decision threshold was applied to the withheld datasets and yielded a sensitivity of 85.7%, specificity of 81.8%, and accuracy of 84%. The positive and negative predictive values were 85.7% and 81.8%, respectively, on the test dataset. CONCLUSIONS: The ability to correctly identify 12 out of 14 cases of OSA (with the 2 false negatives arising from subjects with an apnea-hypopnea index less than 10) indicates that the automated apnea classification system outlined may have clinical utility in pediatric patients.},\n bibtype = {article},\n author = {Shouldice, Redmond B and O'Brien, Louise M and O'Brien, Ciara and de Chazal, Philip and Gozal, David and Heneghan, Conor},\n journal = {Sleep},\n number = {4}\n}","author_short":["Shouldice, R., B.","O'Brien, L., M.","O'Brien, C.","de Chazal, P.","Gozal, D.","Heneghan, C."],"urls":{"Paper":"http://bibbase.org/service/mendeley/6d353feb-efe4-367e-84a2-0815eb9ca878/file/0214ddc5-30a2-b2b9-644d-cb659a3dfb0e/Detection of obstructive sleep apnea in pediatric subjects using surface lead electrocardiogram features..pdf"},"bibbaseid":"shouldice-obrien-obrien-dechazal-gozal-heneghan-detectionofobstructivesleepapneainpediatricsubjectsusingsurfaceleadelectrocardiogramfeatures-2004","role":"author","keyword":["Adult","Body Mass Index","Electrocardiography","Female","Humans","Male","Observation","Obstructive","Obstructive: diagnosis","Polysomnography","Retrospective Studies","Sleep Apnea"],"downloads":0},"bibtype":"article","biburl":null,"creationDate":"2014-10-29T08:44:43.676Z","downloads":0,"keywords":["adult","body mass index","electrocardiography","female","humans","male","observation","obstructive","obstructive: diagnosis","polysomnography","retrospective studies","sleep apnea"],"search_terms":["detection","obstructive","sleep","apnea","pediatric","subjects","using","surface","lead","electrocardiogram","features","shouldice","o'brien","o'brien","de chazal","gozal","heneghan"],"title":"Detection of obstructive sleep apnea in pediatric subjects using surface lead electrocardiogram features.","year":2004}