Indexed on: 15 Apr '16Published on: 15 Apr '16Published in: IEEE journal of biomedical and health informatics
This manuscript explores the probabilistic properties of sleep stage sequences and transitions to improve the performance of sleep stage detection using cardiorespiratory features. A new classifier, based on Conditional Random Fields, is used in different sleep stage detection tasks (N3, NREM, REM and wake) in night-time recordings of ECG and RIP of healthy subjects. Using a dataset of 342 PSG recordings of healthy subjects, amongst which 135 with regular sleep architecture, it outperforms Hidden Markov Models and Bayesian Linear Discriminants in all tasks, achieving an average accuracy of 87:38% and kappa of 0:41 (87:27% and 0:49 for regular subjects) for N3 detection, 78:71% and 0:55 (80:34% and 0:56 for regular subjects) for NREM detection, 88:49% and 0:51 (87:35% and 0:57 for regular subjects) for REM, and 85:69% and 0:51 (90:42% and 0:52 for regular subjects) for wake. In comparison with the stateof- the-art, and having been tested on a much larger data set, the classifier was found to outperform most of the work reported in the literature for some of the tasks, in particular for subjects with regular sleep architecture. It achieves a comparable accuracy for N3, higher accuracy and kappa for REM, and higher accuracy and comparable kappa for NREM than the best performing classifiers described in literature.