This preliminary study investigated the use of cardiac information or more specifically, heart rate variability (HRV), for automatic deep sleep detection throughout the night. The HRV data can be derived from cardiac signals, which were obtained from polysomnography (PSG) recordings. In total 42 features were extracted from the HRV data of 15 single-night PSG recordings (from 15 healthy subjects) for each 30-s epoch, used to perform epoch-by-epoch classification of deep sleep and non-deep sleep (including wake state and all the other sleep stages except deep sleep). To reduce variation of cardiac physiology between subjects, we normalized each feature per subject using a simple Z-score normalization method by subtracting the mean and dividing by the standard deviation of the feature values. A correlation-based feature selection (CFS) method was employed to select informative features as well as removing feature redundancy and a linear discriminant (LD) classifier was applied for deep and non-deep sleep classification. Results show that the use of Z-score normalization can significantly improve the classification performance. A Cohen's Kappa coefficient of 0.42 and an overall accuracy of 81.3% based on a leave-one-subject-out cross-validation were achieved.
|Title of host publication||Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'14), 26-30 August 2014, Chicago, Illinois|
|Place of Publication||Piscataway|
|Publisher||Institute of Electrical and Electronics Engineers|
|Publication status||Published - 2014|
|Event||36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States|
Duration: 26 Aug 2014 → 30 Aug 2014
Conference number: 36
|Conference||36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014|
|Abbreviated title||EMBC 2014|
|Period||26/08/14 → 30/08/14|
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Merel M. van Gilst (Content manager) & M.B. (Beatrijs) van der Hout-van der Jagt (Content manager)
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