Details

Autor(en) / Beteiligte

• Xi Long
• Fonseca, P.
• Foussier, J.
• Haakma, R.
• Aarts, R. M.

Titel

Using dynamic time warping for sleep and wake discrimination

Ist Teil von

• Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, 2012, p.886-889

Ort / Verlag

IEEE

Erscheinungsjahr

2012

Quelle

IEEE Xplore

Beschreibungen/Notizen

• In previous work, a Linear Discriminant (LD) classifier was used to classify sleep and wake states during single-night polysomnography recordings (PSG) of actigraphy, respiratory effort and electrocardiogram (ECG). In order to improve the sleep-wake discrimination performance and to reduce the number of modalities needed for class discrimination, this study incorporated Dynamic Time Warping (DTW) to help discriminate between sleep and wake states based on actigraphy and respiratory effort signal. DTW quantifies signal similarities manifested in the features extracted from the respiratory effort signal. Experiments were conducted on a dataset acquired from nine healthy subjects, using an LD-based classifier. Leave-one-out cross-validation shows that adding this DTW-based feature to the original actigraphy- and respiratory-based feature set results in an epoch-by-epoch Cohen's Kappa agreement coefficient of κ = 0.69 (at an overall accuracy of 95.4%), which represents a significant improvement when compared with the performance obtained without using this feature. Furthermore it is comparable to the result obtained in the previous work which used additional ECG features (κ = 0.70).