In-Home Sleep Apnea Severity Classification using Contact-free Load Cells and an AdaBoosted Decision Tree Algorithm
| Autor: | Joseph Leitschuh, Peter G. Jacobs, J.R. Condon, Clara Mosquera-Lopez, Chad C. Hagen, Cody Hanks |
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| Rok vydání: | 2018 |
| Předmět: |
Male
medicine.medical_specialty Polysomnography 0206 medical engineering Feature extraction Decision tree 02 engineering and technology Article 03 medical and health sciences Sleep Apnea Syndromes 0302 clinical medicine Internal medicine Linear regression medicine Humans Contact free Sleep disorder business.industry Decision tree learning Decision Trees Sleep apnea medicine.disease 020601 biomedical engineering respiratory tract diseases Sleep disordered breathing Cardiology Female Sleep business Algorithms 030217 neurology & neurosurgery |
| Zdroj: | EMBC |
| Popis: | We present a method for automated diagnosis and classification of severity of sleep apnea using an array of non-contact pressure-sensitive sensors placed underneath a mattress as an alternative to conventional obtrusive sensors. Our algorithm comprises two stages: i) A decision tree classifier that identifies patients with sleep apnea, and ii) a subsequent linear regression model that estimates the Apnea-Hypopnea Index (AHI), which is used to determine the severity of sleep disordered breathing. We tested our algorithm on a cohort of 14 patients who underwent overnight home sleep apnea test. The machine learning algorithm was trained and performance was evaluated using leave-one-patient-out cross-validation. The accuracy of the proposed approach in detecting sleep apnea is 86.96%, with sensitivity and specificity of 81.82% and 91.67%, respectively. Moreover, classification of severity of the sleep disorder was correctly assigned in 11 out of 14 cases, and the mean absolute error in the AHI estimation was calculated to be 3.83 events/hr. |
| Databáze: | OpenAIRE |
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