An end-to-end framework for real-time automatic sleep stage classification.

Sleep staging is a fundamental but time consuming process in any sleep laboratory. To greatly speed up sleep staging without compromising accuracy, we developed a novel framework for performing real-time automatic sleep stage classification. The client-server architecture adopted here provides an end-to-end solution for anonymizing and efficiently transporting polysomnography data from the client to the server and for receiving sleep stages in an interoperable fashion. The framework intelligently partitions the sleep staging task between the client and server in a way that multiple low-end clients can work with one server, and can be deployed both locally as well as over the cloud. The framework was tested on four datasets comprising ≈1700 polysomnography records (≈12000 hr of recordings) collected from adolescents, young, and old adults, involving healthy persons as well as those with medical conditions. We used two independent validation datasets: one comprising patients from a sleep disorders clinic and the other incorporating patients with Parkinson's disease. Using this system, an entire night's sleep was staged with an accuracy on par with expert human scorers but much faster (≈5 s compared with 30-60 min). To illustrate the utility of such real-time sleep staging, we used it to facilitate the automatic delivery of acoustic stimuli at targeted phase of slow-sleep oscillations to enhance slow-wave sleep

Vulnerability to sleep deprivation : a drift diffusion model perspective

Most of us have experienced sleep deprivation (SD) every now and then, and know from experience that lack of sleep can adversely affect cognitive function and vigilance. The neurophysiology of sleep and lack thereof, its association with diseases and general well being has been studied extensively for over a century. The lack of sleep has enormous economic, health and life cost. The loss of performance and attention after SD can lead to industrial and transportation accidents, medical errors and lapses in security. While the decline in performance with SD is well established, it was recently (2004) observed that this decline in performance varies significantly between subjects, with some subjects remaining relatively unaffected while others show considerable decline in performance. This subject specific vulnerability towards SD remains stable in time and shows trait like features, i.e., the relative ranking of individuals according to subject specific performance (on some behavioral task) is maintained over time irrespective of sleep history. This suggests a stable neuro-cognitive basis for between-subjects differences in performance. Being able to predict an individual's vulnerability to performance decline when sleep deprived is therefore of considerable interest. The Psychomotor Vigilance Task (PVT) is a sustained-attention, simple one-choice reaction time (RT) task that measures the speed with which subjects respond to a visual stimulus. It is a proven assay for evaluating vigilance. We use the PVT to evaluate and quantify the degradation of performance with SD on three large independent data sets collected from two different labs over an extended period of time. Instead of looking at the RTs using summary statistics as it is traditionally done, we used the drift diffusion model (DDM) which is a powerful model of perceptual decision making with strong neuro-behavioral underpinnings. Using DDM we tried to address three fundamental questions: (1) How are subjects vulnerable to SD differentially affected compared to resistant subjects. (2) Can these differences measured prior to SD predict performance following SD? (3) If there are measurable differences in DDM parameters between vulnerable and resistant subjects, can these differences be supported and substantiated by neuro-imaging experiments? In addressing these questions, we intended to gain new insights into the neuro-behavioural underpinnings of differential vulnerability to SD and to construct a classification system that can use easily measurable behavioural data collected prior to SD to predict an individual's vulnerability to performance decline when sleep deprived. To be able to reliably and efficiently use the DDM to address our research objectives, considerable improvements had to be made to the DDM which was only recently (Ratcliff, 2011) adapted to the one choice RT task like PVT. The statistical properties of the model were poorly understood. The model further lacked efficient ways to simulate and estimate the model parameters. Furthermore, even if we assume that there are measurable differences between the vulnerable and resistant subjects, it remains to be seen as to how these differences are influenced by experimental conditions. Given the importance of wearable devices and smartphones, the behavioural data may soon come from these portable devices instead of controlled laboratory environments. Therefore, the performance of our model must be ascertained across varying experimental conditions. In this thesis, we made significant improvements in simulation and estimation of the model and understood the statistical limitations of the model. Using the DDM, we showed that the vulnerable subjects had a significantly slower rate of information uptake compared to resistant ones even prior to any SD. This was not anticipated by merely observing PVT performance. We tied these observations to actual brain functions using functional magnetic resonance imaging (fMRI) obtained from subjects in rested wakefulness prior to SD. Finally, we showed the DDM parameters are the most important metrics when it comes to characterizing vulnerability amongst the behavioral metrics. We constructed a classifier that was capable of predicting vulnerability using only behavioral data, taken prior to SD, accurately across data sets taken under varying experimental conditions. We showed that the classification rates were reliable, reproducible and promising. Our results will help clinicians gain a better understanding of differential vulnerability to SD and the classification model has the potential to be used in many practical settings.DOCTOR OF PHILOSOPHY (SCE

Robust detection of microaneurysms for sight threatening retinopathy screening

Diabetic retinopathy is one of the major causes of blindness. However diabetic retinopathy does not usually cause a loss of sight until it has reached an advanced stage. The earliest sign of the disease are microaneurysms (MA) which appear as small red dots on retinal fundus images. Various screening programmes have been established in the UK and other countries to collect and assess images on a regular basis, especially in the diabetic population. A considerable amount of time and money is spent in manually grading these images, a large percentage of which are normal. By automatically identifying the normal images, the manual workload and costs could be reduced greatly while increasing the effectiveness of the screening programmes. A novel method of microaneurysm detection from digital retinal screening images is proposed. It is based on filtering using complex-valued circular-symmetric filters, and an eigen-image, morphological analysis of the candidate regions to reduce the false-positive rate. We detail the image processing algorithms and present results on a typical set of 89 image from a published database. Our method is shown to have a best operating sensitivity of 82.6% at a specificity of 80.2% which makes it viable for screening. We discuss the results in the context of a model of visual search and the ROC curves that it can predict

Classifying vulnerability to sleep deprivation using baseline measures of psychomotor vigilance

Objective: To identify measures derived from baseline psychomotor vigilance task (PVT) performance that can reliably predict vulnerability to sleep deprivation. Design: Subjects underwent total sleep deprivation and completed a 10-min PVT every 1–2 h in a controlled laboratory setting. Participants were categorized as vulnerable or resistant to sleep deprivation, based on a median split of lapses that occurred following sleep deprivation. Standard reaction time, drift diffusion model (DDM), and wavelet metrics were derived from PVT response times collected at baseline. A support vector machine model that incorporated maximum relevance and minimum redundancy feature selection and wrapper-based heuristics was used to classify subjects as vulnerable or resistant using rested data. Setting: Two academic sleep laboratories. Participants: Independent samples of 135 (69 women, age 18 to 25 y), and 45 (3 women, age 22 to 32 y) healthy adults. Measurements and Results: In both datasets, DDM measures, number of consecutive reaction times that differ by more than 250 ms, and two wavelet features were selected by the model as features predictive of vulnerability to sleep deprivation. Using the best set of features selected in each dataset, classification accuracy was 77% and 82% using fivefold stratified cross-validation, respectively. Conclusions: Despite differences in experimental conditions across studies, drift diffusion model parameters associated reliably with individual differences in performance during total sleep deprivation. These results demonstrate the utility of drift diffusion modeling of baseline performance in estimating vulnerability to psychomotor vigilance decline following sleep deprivation. Citation: Patanaik A, Kwoh CK, Chua EC, Gooley JJ, Chee MW. Classifying vulnerability to sleep deprivation using baseline measures of psychomotor vigilance. SLEEP 2015;38(5):723–734.ASTAR (Agency for Sci., Tech. and Research, S’pore)Published versio

Data_Sheet_1_An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device.docx

BackgroundThe rapid advancement in wearable solutions to monitor and score sleep staging has enabled monitoring outside of the conventional clinical settings. However, most of the devices and algorithms lack extensive and independent validation, a fundamental step to ensure robustness, stability, and replicability of the results beyond the training and testing phases. These systems are thought not to be feasible and reliable alternatives to the gold standard, polysomnography (PSG).Materials and methodsThis validation study highlights the accuracy and precision of the proposed heart rate (HR)-based deep-learning algorithm for sleep staging. The illustrated solution can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4- levels (Wake; Light; Deep; REM) in 30-s epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n = 994 participants, 994 recordings) and a proprietary dataset of ECG recordings (Z3Pulse, n = 52 participants, 112 recordings) collected with a chest-worn, wireless sensor and simultaneous PSG collection using SOMNOtouch.ResultsWe evaluated the performance of the models in both datasets in terms of Accuracy (A), Cohen’s kappa (K), Sensitivity (SE), Specificity (SP), Positive Predictive Value (PPV), and Negative Predicted Value (NPV). In the CinC dataset, the highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect scoring, while a significant decrease of performance by age was reported across the models. In the Z3Pulse dataset, the highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment.ConclusionThe results of the validation procedure demonstrated the feasibility of accurate HR-based sleep staging. The combination of the proposed sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution deployable in the home environment and robust across age, sex, and AHI scores.</p