A comparison of probabilistic classifiers for sleep stage classification

\u3cp\u3eObjective: To compare conditional random fields (CRF), hidden Markov models (HMMs) and Bayesian linear discriminants (LDs) for cardiorespiratory sleep stage classification on a five-class sleep staging task (wake/N1/N2/N3/REM), to explore the benefits of incorporating time information in the classification and to evaluate the feasibility of sleep staging on obstructive sleep apnea (OSA) patients. Approach: The classifiers with and without time information were evaluated with 10-fold cross-validation on five-, four- (wake/N1 + N2/N3/REM) and three-class (wake/NREM/REM) classification tasks using a data set comprising 443 night-time polysomnography (PSG) recordings of 231 participants (180 healthy participants, 100 of which had a 'regular' sleep architecture, and 51 participants previously diagnosed with OSA). Main results: CRF with time information (CRFt) outperforms all other classifiers on all tasks, achieving a median accuracy and Cohen's κ for all participants of 62.8% and 0.44 for five classes, 68.8% and 0.47 for four classes, and 77.6% and 0.55 for three classes. An advantage was found in training classifiers, specifically for 'regular' and 'OSA' participants, achieving an improvement in classification performance for these groups. For 'regular' participants, CRFt achieved a median accuracy and Cohen's κ of 67.0% and 0.51, 70.8% and 0.53 and 81.3% and 0.62 for five-, four- and three-classes respectively, and for 'OSA' patients, of 59.9% and 0.40, 69.7% and 0.45, and 75.8% and 0.51 for five-, four- and three-classes respectively. Significance: The results suggest that CRFt is not only better at learning and predicting more complex and irregular sleep architectures, but that it also performs reasonably well in five-class classification - the standard for sleep scoring used in clinical PSG. Additionally, and albeit with a decrease in performance when compared with healthy participants, sleep stage classification in OSA patients using cardiorespiratory features and CRFt seems feasible with reasonable accuracy.\u3c/p\u3

Cardiorespiratory sleep stage detection using conditional random fields

\u3cp\u3eThis paper 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 electrocardiogram and respiratory inductance plethysmography of healthy subjects. Using a dataset of 342 polysomnographic recordings of healthy subjects, among 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 state of the art, and having been tested on a much larger dataset, 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 the literature.\u3c/p\u3

System and method for slow wave sleep detection

The present disclosure pertains to a system configured to detect slow wave sleep and/or non-slow wave sleep in a subject during a sleep session based on a predicted onset time of slow wave sleep and/or a predicted end time of slow wave sleep that is determined based on changes in cardiorespiratory parameters of the subject. Cardiorespiratory parameters in a subject typically begin to change before transitions between non-slow wave sleep and slow wave sleep. Predicting this time delay between the changes in the cardiorespiratory parameters and the onset and/or end of slow wave sleep facilitates better (e.g., more sensitive and/or more accurate) determination of slow wave sleep and/or non-slow wave sleep

System and method for cardiorespiratory sleep stage classification

The present disclosure pertains to a system configured to determine one or more parameters based on cardiorespiratory information from a subject and determine sleep stage classifications based on a discriminative undirected probabilistic graphical model such as Conditional Random Fields using the determined parameters. The system is advantageous because sleep is a structured process in which parameters determined for individual epochs are not independent over time and the system determines the sleep stage classifications based on parameters determined for a current epoch, determined relationships between parameters, sleep stage classifications determined for previous epochs, and/or other information. The system does not assume that determined parameters are discriminative during an entire sleep stage, but maybe indicative of a sleep stage transition alone. In some embodiments, the system comprises one or more sensors, one or more physical computer processors, electronic storage, and a user interface