A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requirement for overnight polysomnography (PSG) to achieve formal sleep staging. This is not only costly, but also spending a night in a hospital setting is not always advisable in this patient group. As an alternative to PSG, portable electroencephalography (EEG) headbands (HB) have been developed, which reduce cost, increase patient comfort, and allow sleep recordings in a person's home environment. However, naïve applications of current automated sleep staging systems tend to perform inadequately with HB data, due to their relatively lower quality. Here we present a deep learning (DL) model for automated sleep staging of HB EEG data to overcome these critical limitations. The solution includes a simple band-pass filtering, a data augmentation step, and a model using convolutional (CNN) and long short-term memory (LSTM) layers. With this model, we have achieved 74% (±10%) validation accuracy on low-quality two-channel EEG headband data and 77% (±10%) on gold-standard PSG. Our results suggest that DL approaches achieve robust sleep staging of both portable and in-hospital EEG recordings, and may allow for more widespread use of ambulatory sleep assessments across clinical conditions, including neurodegenerative disorders.

Home EEG sleep assessment shows reduced slow-wave sleep in mild-moderate Alzheimer's disease.

Introduction: Sleep disturbances are common in Alzheimer's disease (AD), with estimates of prevalence as high as 65%. Recent work suggests that specific sleep stages, such as slow-wave sleep (SWS) and rapid eye movement (REM), may directly impact AD pathophysiology. A major limitation to sleep staging is the requirement for clinical polysomnography (PSG), which is often not well tolerated in patients with dementia. We have recently developed a deep learning model to reliably analyze lower quality electroencephalogram (EEG) data obtained from a simple, two-lead EEG headband. Here we assessed whether this methodology would allow for home EEG sleep staging in patients with mild-moderate AD. Methods: A total of 26 mild-moderate AD patients and 24 age-matched, healthy control participants underwent home EEG sleep recordings as well as actigraphy and subjective sleep measures through the Pittsburgh Sleep Quality Index (PSQI). Each participant wore the EEG headband for up to three nights. Sleep was staged using a deep learning model previously developed by our group, and sleep stages were correlated with actigraphy measures as well as PSQI scores. Results: We show that home EEG with a headband is feasible and well tolerated in patients with AD. Patients with mild-moderate AD were found to spend less time in SWS compared to healthy control participants. Other sleep stages were not different between the two groups. Actigraphy or the PSQI were not found to predict home EEG sleep stages. Discussion: Our data show that home EEG is well tolerated, and can ascertain reduced SWS in patients with mild-moderate AD. Similar findings have previously been reported, but using clinical PSG not suitable for the home environment. Home EEG will be particularly useful in future clinical trials assessing potential interventions that may target specific sleep stages to alter the pathogenesis of AD. Highlights: Home electroencephalogram (EEG) sleep assessments are important for measuring sleep in patients with dementia because polysomnography is a limited resource not well tolerated in this patient population.Simplified at-home EEG for sleep assessment is feasible in patients with mild-moderate Alzheimer's disease (AD).Patients with mild-moderate AD exhibit less time spent in slow-wave sleep in the home environment, compared to healthy control participants.Compared to healthy control participants, patients with mild-moderate AD spend more time in bed, with decreased sleep efficiency, and more awakenings as measured by actigraphy, but these measures do not correlate with EEG sleep stages.

The effect of LRRK2 mutations on the cholinergic system in manifest and premanifest stages of Parkinson's disease: a cross-sectional PET study.

BACKGROUND: Markers of neuroinflammation are increased in some patients with LRRK2 Parkinson's disease compared with individuals with idiopathic Parkinson's disease, suggesting possible differences in disease pathogenesis. Previous PET studies have suggested amplified dopamine turnover and preserved serotonergic innervation in LRRK2 mutation carriers. We postulated that patients with LRRK2 mutations might show abnormalities of central cholinergic activity, even before the diagnosis of Parkinson's disease. METHODS: Between June, 2009, and December, 2015, we recruited participants from four movement disorder clinics in Canada, Norway, and the USA. Patients with Parkinson's disease were diagnosed by movement disorder neurologists on the basis of the UK Parkinson's Disease Society Brain Bank criteria. LRRK2 carrier status was confirmed by bidirectional Sanger sequencing. We used the PET tracer N-11C-methyl-piperidin-4-yl propionate to scan for acetylcholinesterase activity. The primary outcome measure was rate of acetylcholinesterase hydrolysis, calculated using the striatal input method. We compared acetylcholinesterase hydrolysis rates between groups using ANCOVA, with adjustment for age based on the results of linear regression analysis. FINDINGS: We recruited 14 patients with LRRK2 Parkinson's disease, 16 LRRK2 mutation carriers without Parkinson's disease, eight patients with idiopathic Parkinson's disease, and 11 healthy controls. We noted significant between-group differences in rates of acetylcholinesterase hydrolysis in cortical regions (average cortex p=0·009, default mode network-related regions p=0·006, limbic network-related regions p=0·020) and the thalamus (p=0·008). LRRK2 mutation carriers without Parkinson's disease had increased acetylcholinesterase hydrolysis rates compared with healthy controls in the cortex (average cortex, p=0·046). Patients with LRRK2 Parkinson's disease had significantly higher acetylcholinesterase activity in some cortical regions (average cortex p=0·043, default mode network-related regions p=0·021) and the thalamus (thalamus p=0·004) compared with individuals with idiopathic disease. Acetylcholinesterase hydrolysis rates in healthy controls were correlated inversely with age. INTERPRETATION: LRRK2 mutations are associated with significantly increased cholinergic activity in the brain in mutation carriers without Parkinson's disease compared with healthy controls and in LRRK2 mutation carriers with Parkinson's disease compared with individuals with idiopathic disease. Changes in cholinergic activity might represent early and sustained attempts to compensate for LRRK2-related dysfunction, or alteration of acetylcholinesterase in non-neuronal cells. FUNDING: Michael J Fox Foundation, National Institutes of Health, and Pacific Alzheimer Research Foundation.

Periorbital edema secondary to positive airway pressure therapy.

Two patients developed bilateral, periorbital edema after initiating positive airway pressure (PAP) therapy with a full face mask. The periorbital edema was more pronounced in the morning and would dissipate throughout the day. This phenomenon seemed to be correlated with the direct pressure of the full face mask, which may have impaired lymphatic and venous drainage. To test this hypothesis, each patient was changed to a nasal pillow interface with subsequent improvement in the periorbital edema.