Abstract: J. Rolink, P. Fonseca, X. Long, S. Leonhardt (2019)

In this work the method of Recurrence Quantification Analysis (RQA), often used for the analysis of complex dynamic systems, is employed to extract novel features for sleep/wake classification only using cardio-respiratory signals like electrocardiogram (ECG), heart rate (HR) and respiratory effort (RE). A polysomnography data set consisting of 313 full-night recordings is used to evaluate the features. The sleep/wake classification is performed with a classifier based on Linear Discriminant Analysis (LDA) and it is validated in a Leave One Subject Out cross-validation (LOSOCV) scheme. More than 1300 features are extracted from five different cardio-respiratory modalities (ECG, RE, HR, and combinations of ECG+RE and RE+HR). Each modality is processed to obtain 13 basic RQA features, five post-processed versions and, furthermore, three normalizations are applied, leading to a total count of 975 RQA features. From literature, 126 known cardio-respiratory and actigraphy features, normalized with the same procedures thus resulting in 378 distinct features, are used for performance comparison. A feature selection method based on the Mahalanobis distance and the inter-feature correlation is used to determine the most relevant features of both sets, resulting in a set of 158 from the existing features (set A) and 232 from the RQA based features (set B). The pooled Cohen's kappa coefficient for set A and set B is 0.586 and 0.522, respectively. The combination of both feature sets (set C) improves the kappa value to 0.625.

In addition, ROC and PR curves with their corresponding Area Under Curve (AUC) values are computed. It is also shown that derived sleep statistics (sleep efficiency, sleep onset, total sleep time, etc.) deviate less with respect to the ground truth annotations using the additional RQA features compared to the use of literature-based features only.

Besides the overall performance, the data set is split into six different but almost equally sized age and sex groups to allow unbiased comparisons among each other. It is shown, that the classification performance decreases with increasing age but is almost independent from sex. Hence, for future work it is suggested to implement the RQA features and also to focus on the improvement of features especially for elderly persons.

Please note: The abstracts of the bibliography database may underly other copyrights.

Ihr Browser versucht gerade eine Seite aus dem sogenannten Internet auszudrucken. Das Internet ist ein weltweites Netzwerk von Computern, das den Menschen ganz neue Möglichkeiten der Kommunikation bietet.

Da Politiker im Regelfall von neuen Dingen nichts verstehen, halten wir es für notwendig, sie davor zu schützen. Dies ist im beidseitigen Interesse, da unnötige Angstzustände bei Ihnen verhindert werden, ebenso wie es uns vor profilierungs- und machtsüchtigen Politikern schützt.

Sollten Sie der Meinung sein, dass Sie diese Internetseite dennoch sehen sollten, so können Sie jederzeit durch normalen Gebrauch eines Internetbrowsers darauf zugreifen. Dazu sind aber minimale Computerkenntnisse erforderlich. Sollten Sie diese nicht haben, vergessen Sie einfach dieses Internet und lassen uns in Ruhe.

Die Umgehung dieser Ausdrucksperre ist nach §95a UrhG verboten.

Mehr Informationen unter www.politiker-stopp.de.

Improving sleep/wake classification with recurrence quantification analysis features