Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms

We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polysomnogram recordings and subsequently tested on 230 independent recordings. Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of 0.746, improving on previous reported results by other groups also using only raw polysomnogram data. Most errors were made on non-REM stage 1 and 3 decisions, errors likely resulting from the definition of these stages. Further testing on independent cohorts is needed to verify performance for clinical use

Automatic Detection of Cortical Arousals in Sleep and their Contribution to Daytime Sleepiness

Cortical arousals are transient events of disturbed sleep that occur spontaneously or in response to stimuli such as apneic events. The gold standard for arousal detection in human polysomnographic recordings (PSGs) is manual annotation by expert human scorers, a method with significant interscorer variability. In this study, we developed an automated method, the Multimodal Arousal Detector (MAD), to detect arousals using deep learning methods. The MAD was trained on 2,889 PSGs to detect both cortical arousals and wakefulness in 1 second intervals. Furthermore, the relationship between MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was analyzed in 1447 MSLT instances in 873 subjects. In a dataset of 1,026 PSGs, the MAD achieved a F1 score of 0.76 for arousal detection, while wakefulness was predicted with an accuracy of 0.95. In 60 PSGs scored by multiple human expert technicians, the MAD significantly outperformed the average human scorer for arousal detection with a difference in F1 score of 0.09. After controlling for other known covariates, a doubling of the arousal index was associated with an average decrease in MSL of 40 seconds ($\beta$ = -0.67, p = 0.0075). The MAD outperformed the average human expert and the MAD-predicted arousals were shown to be significant predictors of MSL, which demonstrate clinical validity the MAD.Comment: 40 pages, 13 figures, 9 table

Inter-expert and intra-expert reliability in sleep spindle scoring

Objectives: To measure the inter-expert and intra-expert agreement in sleep spindle scoring, and to quantify how many experts are needed to build a reliable dataset of sleep spindle scorings. Methods: The EEG dataset was comprised of 400 randomly selected 115 s segments of stage 2 sleep from 110 sleeping subjects in the general population (57 ± 8, range: 42–72 years). To assess expert agreement, a total of 24 Registered Polysomnographic Technologists (RPSGTs) scored spindles in a subset of the EEG dataset at a single electrode location (C3-M2). Intra-expert and inter-expert agreements were calculated as F_1-scores, Cohen’s kappa (κ), and intra-class correlation coefficient (ICC). Results: We found an average intra-expert F_1-score agreement of 72 ± 7% (κ: 0.66 ± 0.07). The average inter-expert agreement was 61 ± 6% (κ: 0.52 ± 0.07). Amplitude and frequency of discrete spindles were calculated with higher reliability than the estimation of spindle duration. Reliability of sleep spindle scoring can be improved by using qualitative confidence scores, rather than a dichotomous yes/no scoring system. Conclusions: We estimate that 2–3 experts are needed to build a spindle scoring dataset with ‘substantial’ reliability (κ: 0.61–0.8), and 4 or more experts are needed to build a dataset with ‘almost perfect’ reliability (κ: 0.81–1). Significance: Spindle scoring is a critical part of sleep staging, and spindles are believed to play an important role in development, aging, and diseases of the nervous system

Balance control systems in Parkinson's disease and the impact of pedunculopontine area stimulation

Impaired balance is a major contributor to falls and diminished quality of life in Parkinson’s disease, yet the pathophysiology is poorly understood. Here, we assessed if patients with Parkinson’s disease and severe clinical balance impairment have deficits in the intermittent and continuous control systems proposed to maintain upright stance, and furthermore, whether such deficits are potentially reversible, with the experimental therapy of pedunculopontine nucleus deep brain stimulation. Two subject groups were assessed: (i) 13 patients with Parkinson’s disease and severe clinical balance impairment, implanted with pedunculopontine nucleus deep brain stimulators; and (ii) 13 healthy control subjects. Patients were assessed in the OFF medication state and blinded to two conditions; off and on pedunculopontine nucleus stimulation. Postural sway data (deviations in centre of pressure) were collected during quiet stance using posturography. Intermittent control of sway was assessed by calculating the frequency of intermittent switching behaviour (discontinuities), derived using a wavelet-based transformation of the sway time series. Continuous control of sway was assessed with a proportional–integral–derivative (PID) controller model using ballistic reaction time as a measure of feedback delay. Clinical balance impairment was assessed using the ‘pull test’ to rate postural reflexes and by rating attempts to arise from sitting to standing. Patients with Parkinson’s disease demonstrated reduced intermittent switching of postural sway compared with healthy controls. Patients also had abnormal feedback gains in postural sway according to the PID model. Pedunculopontine nucleus stimulation improved intermittent switching of postural sway, feedback gains in the PID model and clinical balance impairment. Clinical balance impairment correlated with intermittent switching of postural sway (rho = − 0.705, P < 0.001) and feedback gains in the PID model (rho = 0.619, P = 0.011). These results suggest that dysfunctional intermittent and continuous control systems may contribute to the pathophysiology of clinical balance impairment in Parkinson’s disease. Clinical balance impairment and their related control system deficits are potentially reversible, as demonstrated by their improvement with pedunculopontine nucleus deep brain stimulation

Post-Streptococcal Antibodies Are Associated with Metabolic Syndrome in a Population-Based Cohort

Background: Streptococcal infections are known to trigger autoimmune disorders, affecting millions worldwide. Recently, we found an association between post-streptococcal autoantibodies against Protein Disulphide Isomerase (PDI), an enzyme involved in insulin degradation and insulin resistance. This led us to evaluate associations between post-streptococcal antibodies and metabolic syndrome, as defined by the updated National Cholesterol Education Program definition, 2005. Methods and Findings: Metabolic data (HDL, triglycerides, fasting glucose, blood pressure, waist circumference, BMI, smoking), post-streptococcal antibodies (anti-Streptolysin O (ASO) and anti-PDI), and C-reactive protein (CRP, as a general inflammatory marker), were assessed in 1156 participants of the Wisconsin Sleep Cohort Study. Anti-PDI antibodies were found in 308 participants (26.6%), ASO$100 in 258 (22.3%), and 482 (41.7%) met diagnostic criteria for metabolic syndrome. Anti-PDI antibodies but not ASO were significantly associated with metabolic syndrome [n = 1156, OR 1.463 (95 % CI 1.114, 1.920), p = 0.0062; adjusted for age, gender, education, smoking]. Importantly, the anti-PDI- metabolic syndrome association remained significant after adjusting for CRP and fasting insulin. Conclusions: Post-streptococcal anti-PDI antibodies are associated with metabolic syndrome regardless of fasting insulin and CRP levels. Whereas these data are in line with a growing body of evidence linking infections, immunity an

Data enhancement for co-morbidity measurement among patients referred for sleep diagnostic testing: an observational study

<p>Abstract</p> <p>Background</p> <p>Observational outcome studies of patients with obstructive sleep apnea (OSA) require adjustment for co-morbidity to produce valid results. The aim of this study was to evaluate whether the combination of administrative data and self-reported data provided a more complete estimate of co-morbidity among patients referred for sleep diagnostic testing.</p> <p>Methods</p> <p>A retrospective observational study of 2149 patients referred for sleep diagnostic testing in Calgary, Canada. Self-reported co-morbidity was obtained with a questionnaire; administrative data and validated algorithms (when available) were also used to define the presence of these co-morbid conditions within a two-year period prior to sleep testing.</p> <p>Results</p> <p>Patient self-report of co-morbid conditions had varying levels of agreement with those derived from administrative data, ranging from substantial agreement for diabetes (κ = 0.79) to poor agreement for cardiac arrhythmia (κ = 0.14). The enhanced measure of co-morbidity using either self-report or administrative data had face validity, and provided clinically meaningful trends in the prevalence of co-morbidity among this population.</p> <p>Conclusion</p> <p>An enhanced measure of co-morbidity using self-report and administrative data can provide a more complete measure of the co-morbidity among patients with OSA when agreement between the two sources is poor. This methodology will aid in the adjustment of these coexisting conditions in observational studies in this area.</p