TY - GEN
T1 - SleepAp
T2 - 2013 40th Computing in Cardiology Conference, CinC 2013
AU - Behar, J.
AU - Roebuck, A.
AU - Shahid, M.
AU - Daly, J.
AU - Hallack, A.
AU - Palmius, N.
AU - Stradling, J. R.
AU - Clifford, G. D.
PY - 2013
Y1 - 2013
N2 - Obstructive Sleep Apnoea (OSA) is a sleep disorder with long term consequences. It is often diagnosed with an overnight sleep study or polysomnogram. Monitoring can be costly with long wait times for diagnosis. In this paper we describe a novel OSA screening framework and prototype phone application (app). A database of 856 patients that underwent at-home polysomnography was collected. Features were derived from audio, actigraphy, photoplethysmography (PPG) and demographics, and used as the inputs of a support vector machine (SVM) classifier. The SVM was trained on 735 patients (368 non-OSA and 567 OSA) and tested on 121 patients (44-77 split). Classification on the test set had an accuracy of up to 92.3%. The signal processing and machine learning algorithms were ported to Java and integrated into the phone app. The app records the audio, actigraphy and PPG signals, implements the clinically validated STOP-BANG questionnaire, derives features from the signals, and finally classifies the patient as needing treatment or not using the trained SVM. The resulting software could provide a new, easy-to-use, low-cost and widely available modality for OSA screening.
AB - Obstructive Sleep Apnoea (OSA) is a sleep disorder with long term consequences. It is often diagnosed with an overnight sleep study or polysomnogram. Monitoring can be costly with long wait times for diagnosis. In this paper we describe a novel OSA screening framework and prototype phone application (app). A database of 856 patients that underwent at-home polysomnography was collected. Features were derived from audio, actigraphy, photoplethysmography (PPG) and demographics, and used as the inputs of a support vector machine (SVM) classifier. The SVM was trained on 735 patients (368 non-OSA and 567 OSA) and tested on 121 patients (44-77 split). Classification on the test set had an accuracy of up to 92.3%. The signal processing and machine learning algorithms were ported to Java and integrated into the phone app. The app records the audio, actigraphy and PPG signals, implements the clinically validated STOP-BANG questionnaire, derives features from the signals, and finally classifies the patient as needing treatment or not using the trained SVM. The resulting software could provide a new, easy-to-use, low-cost and widely available modality for OSA screening.
UR - http://www.scopus.com/inward/record.url?scp=84894205449&partnerID=8YFLogxK
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AN - SCOPUS:84894205449
SN - 9781479908844
T3 - Computing in Cardiology
SP - 257
EP - 260
BT - Computing in Cardiology 2013, CinC 2013
Y2 - 22 September 2013 through 25 September 2013
ER -