Obstructive sleep apnea is considered to be one of the most prevalent sleep-related disorders that can affect the general population. However, the gold standard for the diagnosis, polysomnography, is an expensive and complicated process that is commonly unavailable to a large group of the population. Alternatively, automatic approaches have been developed to address this issue. One of the goals of this research is to perform the classification of the apnea events with the lowest possible number of sensors. Therefore, the blood oxygen saturation signal was employed in this work since it is correlated with the occurrence of apnea events and it can be measured from a single noninvasive sensor. The events detection was performed by a combination of classifiers. However, choosing the type of classifier to combine and select the most relevant features for each classifier is considered to be a well-known problem in the field of machine learning. A self-configuring classifier combination technique based on genetic algorithms was developed for multiple classifiers and features selection which was tested along with different databases and input sizes. The best performance for obstructive sleep apnea detection was achieved using maximum voting independent feature selection with 1 min time window having the best sensitivity of 82.48% similar database in the literature. This model was later tested on another database for cross-database accuracy. With an average accuracy of 91.33%, the system proved its capabilities for clinical diagnosis since the model was developed and validated with both subject and database independence.
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This research has been supported by the Portuguese Foundation for Science and Technology through Projeto Estratégico UID/EEA/50009/2019, ARDITI—Agência Regional para o Desenvolvimento da Investigação, Tecnologia e Inovação under the scope of the Project M1420-09-5369-FSE-000001-PhD Studentship and MITIExcell—Excelencia Internacional de IDT&I NAS TIC (Project Number M1420-01-01450FEDER0000002), provided by the Regional Government of Madeira.
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The Acc, Sen and Spc for 1 min, 3 min and 5 min are presented in Fig. 5. The values of the performance metrics fluctuated over the generation. It did not have a steady increment or decrement such as \(CF\). Because the SC3 was trying to optimize \(CF\) instead of the accuracy, sensitivity and specificity. The highest Acc (85.22%) was achieved with the 3 min window of SF MaxV (MaxVSF). However, the same window had the lowest Sen (79.86%). MaxVIF 1 min achieved a similar Acc but with a better Sen (82.48%). The highest Acc, Sen, Spc among MaxV were achieved, respectively, by MaxVIF 1 min (85.30%), MaxVSF 1 min (83.51%) and MaxVSF 3 min (87.08%). For the WLC, WLCSF1 has the highest Acc (85.30%) and Spc (86.28%) among WLC SC3. The best Sen (86.33%) was achieved by WLCIF5 (Fig. 5). Selected features and classifiers for SC3 classifiers are presented in Table 5. For LDA classifier Figs. 6 and 7 represents performance information.
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Mostafa, S.S., Mendonça, F., Juliá-Serdá, G. et al. SC3: self-configuring classifier combination for obstructive sleep apnea. Neural Comput & Applic 32, 17825–17841 (2020). https://doi.org/10.1007/s00521-019-04582-2
- Combined classifiers
- Sleep apnea
- Genetic algorithm
- Machine learning