Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph—a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.

Study Objectives Up to 5% of adults in Western countries have undiagnosed sleep-disordered breathing (SDB). Studies have shown that electrocardiogram (ECG)-based algorithms can identify SDB and may provide alternative screening. Most studies, however, have limited generalizability as they have been conducted using the apnea-ECG database, a small sample database that lacks complex SDB cases. Methods Here, we developed a fully automatic, data-driven algorithm that classifies apnea and hypopnea events based on the ECG using almost 10 000 polysomnographic sleep recordings from two large population-based samples, the Sleep Heart Health Study (SHHS) and the Multi-Ethnic Study of Atherosclerosis (MESA), which contain subjects with a broad range of sleep and cardiovascular diseases (CVDs) to ensure heterogeneity. Results Performances on average were sensitivity(Se)=68.7%, precision (Pr)=69.1%, score (F1)=66.6% per subject, and accuracy of correctly classifying apnea–hypopnea index (AHI) severity score was Acc=84.9%. Target AHI and predicted AHI were highly correlated (R2 = 0.828) across subjects, indicating validity in predicting SDB severity. Our algorithm proved to be statistically robust between databases, between different periodic leg movement index (PLMI) severity groups, and for subjects with previous CVD incidents. Further, our algorithm achieved the state-of-the-art performance of Se=87.8%, Sp=91.1%, Acc=89.9% using independent comparisons and Se=90.7%, Sp=95.7%, Acc=93.8% using a transfer learning comparison on the apnea-ECG database. Conclusions Our robust and automatic algorithm constitutes a minimally intrusive and inexpensive screening system for the detection of SDB events using the ECG to alleviate the current problems and costs associated with diagnosing SDB cases and to provide a system capable of identifying undiagnosed SDB cases.

The presented algorithm shows good performance when considering that more than 80% of the false positives (FP) found by the detection algorithm appeared in relation to either leg movement or respiratory events. This indicates that most FP constitute autonomic activations that are indistinguishable from those with cortical cohesion. The proposed algorithm provides an automatic system trained in a clinical environment, which can be utilized to analyze the systemic and clinical impacts of arousals.

The adaptation of cells to hyperosmotic conditions involves accumulation of organic osmolytes to achieve osmotic equilibrium and maintenance of cell volume. The Na+ and Cl(-)-coupled betaine/GABA transporter, designated BGT-1, is responsible for the cellular accumulation of betaine and has been proposed to play a role in osmoregulation in the brain. BGT-1 is also called GAT2 (GABA transporter 2) when referring to the mouse transporter homologue. Using Western Blotting the expression of the mouse GAT2 protein was investigated in astrocyte primary cultures exposed to a growth medium made hyperosmotic (353+/-2.5 mosmol/kg) by adding sodium chloride. A polyclonal anti-BGT-1 antibody revealed the presence of two characteristic bands at 69 and 138 kDa. When astrocytes were grown for 24 h under hyperosmotic conditions GAT2 protein was up-regulated 2-4-fold compared to the level of the isotonic control. Furthermore, the expected dimer of GAT2 was also up-regulated after 24 h under the hyperosmotic conditions. The [3H]GABA uptake was examined in the hyperosmotic treated astrocytes, and characterized using different selective GABA transport inhibitors. The up-regulation of GAT2 protein was not affecting total GABA uptake but the hyperosmotic condition did change total GABA uptake possibly involving GAT1. Immunocytochemical studies revealed cell membrane localization of GAT2 throughout astroglial processes. Taken together, these results indicate that astroglial GAT2 expression and function may be regulated by hyperosmolarity in cultured mouse astrocytes, suggesting a role of GAT2 in osmoregulation in neural cells.

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Introduction Wearable, multisensory consumer devices that estimate sleep are prevalent and hold great potential. Most validated actigraphic prediction studies of sleep stages (SS) have only used low resolution (30 sec) data and the Cole-Kripke algorithm. Other algorithms are often proprietary and not accessible or validated. We present an automatic, data-driven deep learning algorithm that process raw actigraphy (ACC) and photoplethysmography (PPG) using a low-cost consumer device at high (25Hz) and low resolution to predict SS and to detect sleep disordered breathing (SDB) events. Methods Our automatic, data-driven algorithm is a deep neural network trained and evaluated to predict SS and SDB events on 236 recordings of ACC data from a wrist-worn accelerometer and PPG data from the overlapping PSG. The network was tested on raw ACC and PPG data, which was collected at 25 Hz using the HUAMI Arc2 wristband from 39 participants that underwent a nocturnal polysomnography (PSG). Results Overall accuracy (Acc), recall (Re), specificity (Sp), and kappa (κ) per subject on the test dataset the prediction of wake, NREM, REM was Acc=76.6%, Re=72.4%, Sp=78.0%, kappa=0.42. On average, we found a 7 % higher performance using the raw sensor data as input instead of processed, low resolution inputs. PPG was especially useful for REM detection. The network assigned 55.6% of patients to the correct SDB severity group when using an apnea-hypopnea index above 15. Conclusion Current results show that SS prediction is significantly improved when using the raw sensor data; it indicates that the system holds promise as a potential pervasive monitoring device for patients with chronic sleep disorders. In contrast the system did not show potential as a sleep apnea screening tool. Additional studies are ongoing to examine the effects of pathology such as sleep apnea and periodic leg movement on SS prediction. Support Technical University of Denmark; University of Copenhagen, Copenhagen Center for Health Technology, Klarman Family Foundation.

Introduction Evaluation of sleep apnea involves manual annotation of Polysomnography (PSG) file, a time-consuming process subject to interscorer variations. The DOSED algorithm has been shown to be helpful in detecting Central Sleep Apnea (CSA), Obstructive Sleep Apnea (OSA), and Hypopnea when merged into a single event type. This work uses a modified version of DOSED capable of detecting each event type separately. Methods The network consists of 3 blocks of 1D convolutional layers followed by 6 blocks of 2D convolutional layers. The network has 2 classification layers, one determines the probability of each class, and the other determines the start and duration time of the event with the highest probability. Four channels from nasal and mouth airflow and position of abdomen and thorax are used as input to the model. The model was trained using 2800 PSG from 4 different cohorts (MESA, MROS, SSC, WSC) and tested on 70 PSG, which have been scored by six technicians (Stanford, U Penn, St Louis). Results On an event by event basis, model F1-scores versus a weighted consensus score based on 6 technicians were 0.60 for OSA, 0.43 for CSA, and 0.34 for Hypopnea. Average F1-scores for the 6 technicians were 0.48 (std 0.04) for OSA, 0.29 (std 0.145) for CSA, and 0.54 (std 0.183) for Hypopnea, indicating that the model functions better on an event-by-event basis than an average technician. Correlations between indices/hr for central apnea, obstructive apnea, and hypopnea indicate excellent correlations for apneas, but poor correlation for hypopnea. We are now adding the snoring channel to explore if predictions can be improved. Conclusion The result shows that deep learning-based models can detect respiratory events with an accuracy similar to technicians. The poor agreement between technicians from different universities indicates that we need better definitions of hypopnea. Support

Wrist-worn consumer sleep technologies (CST) that contain accelerometers (ACC) and photoplethysmography (PPG) are increasingly common and hold great potential to function as out-of-clinic (OOC) sleep monitoring systems. However, very few validation studies exist because raw data from CSTs are rarely made accessible for external use. We present a deep neural network (DNN) with a strong temporal core, inspired by U-Net, that can process multivariate time series inputs with different dimensionality to predict sleep stages (wake, light-, deep-, and REM sleep) using ACC and PPG signals from nocturnal recordings. The DNN was trained and tested on 3 internal datasets, comprising raw data both from clinical and wrist-worn devices from 301 recordings (PSG-PPG: 266, Wrist-worn PPG: 35). External validation was performed on a hold-out test dataset containing 35 recordings comprising only raw data from a wristworn CST. An accuracy=0.71±0.09, 0.76±0.07, 0.73±0.06, and κ=0.58±0.13, 0.64±0.09, 0.59±0.09 was achieved on the internal test sets. Our experiments show that spectral preprocessing yields superior performance when compared to surrogate-, feature-, raw data-based preparation. Combining both modalities produce the overall best performance, although PPG proved to be the most impactful and was the only modality capable of detecting REM sleep well. Including ACC improved model precision to wake and sleep metric estimation. Increasing input segment size improved performance consistently; the best performance was achieved using 1024 epochs (~8.5 hrs.). An accuracy=0.69±0.13 and κ=0.58±0.18 was achieved on the hold-out test dataset, proving the generalizability and robustness of our approach to raw data collected with a wrist-worn CST.