Classification of Sleep Disorder from Single Lead Non-overlapping of ECG-apnea based Non-Linear Analysis using Ensemble Approach

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dc.contributor.advisorPurnomo, Mauridhi Hery
dc.contributor.authorFahruzi, Iman
dc.contributor.authorPurnama, I Ketut Eddy
dc.contributor.authorTakahashi, Hideya
dc.contributor.authorPurnomo, Mauridhi Hery
dc.date.accessioned2023-06-12T14:15:49Z
dc.date.available2023-06-12T14:15:49Z
dc.date.issued2019-12-05
dc.descriptionInternational proceeding iCAST 2029-JAPANen_US
dc.description.abstrakThe most significant determinant of quality of life is sleep quality, with better sleep resulting in a healthier and longer life. Polysomnography, or PSG, is a standardized system to get the medical records from multi-lead ECG recordings. However, PSG is a complicated, expensive and time-consuming procedure. Other alternatives include home sleep centre (HSC) development as a tool for early diagnosis and prevention of sleep disorders while keeping high accuracy. HSC uses low-cost equipment by utilizing single-lead ECG and accompanying applications. ECG is one of the media used in diagnosing and analysis of medical information related to sleep disorders. This study aims to develop a computerized sleep diagnosis application to help experts classify symptoms by investigation and evaluation of QRS morphological, time-frequency characteristics, and nonlinear analysis from single-lead ECG recordings. The classification of non-overlapping of ECG-apnea based non-linear analysis using an ensemble approach. The ensemble learning model approach, using the Boosted Tree test, yielded an accuracy of 94.7%, prediction speed of 120 obs/s and training time of 2.374 s. The QRS morphological characteristic and improved non-overlapping ECG recordings provided satisfactory diagnostic performance in sleep disorder classification for HSC usage.en_US
dc.identifier.issn2325-5994
dc.identifier.urihttps://repository.polibatam.ac.id/xmlui/handle/123456789/1687
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectcomputerized sleep disorderen_US
dc.subjectECG apneaen_US
dc.subjectensemble learningen_US
dc.subjectqrsen_US
dc.subjecttime-frequencyen_US
dc.titleClassification of Sleep Disorder from Single Lead Non-overlapping of ECG-apnea based Non-Linear Analysis using Ensemble Approachen_US
dc.typeArticleen_US
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