Deep transfer learning for improving single-EEG arousal detection

Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two deep transfer learning strategies for overcoming the channel mismatch problem for cases where two datasets do not contain exactly the same setup leading to degraded performance in single-EEG models. Specifically, we train a baseline model on multivariate polysomnography data and subsequently replace the first two layers to prepare the architecture for single-channel electroencephalography data. Using a fine-tuning strategy, our model yields similar performance to the baseline model (F1=0.682 and F1=0.694, respectively), and was significantly better than a comparable single-channel model. Our results are promising for researchers working with small databases who wish to use deep learning models pre-trained on larger databases.Comment: Accepted for presentation at EMBC202

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

Neurophysiological basis of rapid eye movement sleep behavior disorder:Informing future drug development

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by a history of recurrent nocturnal dream enactment behavior and loss of skeletal muscle atonia and increased phasic muscle activity during REM sleep: REM sleep without atonia. RBD and associated comorbidities have recently been identified as one of the most specific and potentially sensitive risk factors for later development of any of the alpha-synucleinopathies: Parkinson’s disease, dementia with Lewy bodies, and other atypical parkinsonian syndromes. Several other sleep-related abnormalities have recently been identified in patients with RBD/Parkinson’s disease who experience abnormalities in sleep electroencephalographic frequencies, sleep–wake transitions, wake and sleep stability, occurrence and morphology of sleep spindles, and electrooculography measures. These findings suggest a gradual involvement of the brainstem and other structures, which is in line with the gradual involvement known in these disorders. We propose that these findings may help identify biomarkers of individuals at high risk of subsequent conversion to parkinsonism

Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram

Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation of arousals and leg movements. Both of these are commonly seen during normal sleep, while an excessive amount of either is linked to disrupted sleep patterns, excessive daytime sleepiness impacting quality of life, and various sleep disorders. Our model was trained on 1,485 subjects and tested on 1,000 separate recordings of sleep. We tested two different experimental setups and found optimal arousal detection was attained by including a recurrent neural network module in our default model with a dynamic default event window (F1 = 0.75), while optimal leg movement detection was attained using a static event window (F1 = 0.65). Our work show promise while still allowing for improvements. Specifically, future research will explore the proposed model as a general-purpose sleep analysis model.Comment: Accepted for publication in 41st International Engineering in Medicine and Biology Conference (EMBC), July 23-27, 201

Mortality and use of psychotropic medication in patients with stroke:a population-wide, register-based study

OBJECTIVES: The study sought to describe whether psychotropic medication may have long-term side effects in patients with stroke compared with controls. SETTING: Use of national register data from healthcare services were identified from the Danish National Patient Registry in Denmark. Information about psychotropic medication use was obtained from the Danish Register of Medicinal Product Statistics. OBJECTIVES: We aimed to evaluate all-cause mortality in relation to the use of benzodiazepines, antidepressants and antipsychotics in patients with stroke and matched controls. PARTICIPANTS: Patients with a diagnosis of stroke and either no drug use or preindex use of psychotropic medication (n=49 968) and compared with control subjects (n=86 100) matched on age, gender, marital status and community location. PRIMARY OUTCOME MEASURE: All-cause mortality. RESULTS: All-cause mortality was higher in patients with previous stroke compared with control subjects. Mortality HRs were increased for participants prescribed serotonergic antidepressant drugs (HR=1.699 (SD=0.030), p=0.001 in patients; HR=1.908 (0.022), p<0.001 in controls, respectively), tricyclic antidepressants (HR=1.365 (0.045), p<0.001; HR=1.733 (0.022), p<0.001), benzodiazepines (HR=1.643 (0.040), p<0.001; HR=1.776 (0.053), p<0.001), benzodiazepine-like drugs (HR=1.776 (0.021), p<0.001; HR=1.547 (0.025), p<0.001), first-generation antipsychotics (HR=2.001 (0.076), p<0.001; HR=3.361 (0.159), p<0.001) and second-generation antipsychotics (HR=1.645 (0.070), p<0.001; HR=2.555 (0.086), p<0.001), compared with no drug use. Interaction analysis suggested statistically significantly higher mortality HRs for most classes of psychotropic drugs in controls compared with patients with stroke. CONCLUSIONS: All-cause mortality was higher in patients with stroke and controls treated with benzodiazepines, antidepressants and antipsychotics than in their untreated counterparts. Our findings suggest that care should be taken in the use and prescription of such drugs, and that they should be used in conjunction with adequate clinical controls

Automatic sleep stage classification with deep residual networks in a mixed-cohort setting

Study Objectives: Sleep stage scoring is performed manually by sleep experts and is prone to subjective interpretation of scoring rules with low intra- and interscorer reliability. Many automatic systems rely on few small-scale databases for developing models, and generalizability to new datasets is thus unknown. We investigated a novel deep neural network to assess the generalizability of several large-scale cohorts. Methods: A deep neural network model was developed using 15684 polysomnography studies from five different cohorts. We applied four different scenarios: 1) impact of varying time-scales in the model; 2) performance of a single cohort on other cohorts of smaller, greater or equal size relative to the performance of other cohorts on a single cohort; 3) varying the fraction of mixed-cohort training data compared to using single-origin data; and 4) comparing models trained on combinations of data from 2, 3, and 4 cohorts. Results: Overall classification accuracy improved with increasing fractions of training data (0.25$\%$: 0.782 $\pm$ 0.097, 95$\%$ CI [0.777-0.787]; 100$\%$: 0.869 $\pm$ 0.064, 95$\%$ CI [0.864-0.872]), and with increasing number of data sources (2: 0.788 $\pm$ 0.102, 95$\%$ CI [0.787-0.790]; 3: 0.808 $\pm$ 0.092, 95$\%$ CI [0.807-0.810]; 4: 0.821 $\pm$ 0.085, 95$\%$ CI [0.819-0.823]). Different cohorts show varying levels of generalization to other cohorts. Conclusions: Automatic sleep stage scoring systems based on deep learning algorithms should consider as much data as possible from as many sources available to ensure proper generalization. Public datasets for benchmarking should be made available for future research.Comment: Author's original version. This article has been accepted for publication in SLEEP published by Oxford University Pres