Neurology

Sleep Disorders

Latest AI and machine learning research in sleep disorders for healthcare professionals.

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Development and validation of a machine learning model for prediction of comorbid major depression disorder among narcolepsy type 1.

BACKGROUND: Major depression disorder (MDD) forms a common psychiatric comorbidity among patients wi...

A machine learning model to predict the risk of perinatal depression: Psychosocial and sleep-related factors in the Life-ON study cohort.

Perinatal depression (PND) is a common complication of pregnancy associated with serious health cons...

Machine learning-empowered sleep staging classification using multi-modality signals.

The goal is to enhance an automated sleep staging system's performance by leveraging the diverse sig...

Data-driven shortened Insomnia Severity Index (ISI): a machine learning approach.

BACKGROUND: The Insomnia Severity Index (ISI) is a widely used questionnaire with seven items for id...

Expert-level sleep staging using an electrocardiography-only feed-forward neural network.

Reliable classification of sleep stages is crucial in sleep medicine and neuroscience research for p...

Identifying time-resolved features of nocturnal sleep characteristics of narcolepsy using machine learning.

The differential diagnosis of narcolepsy type 1, a rare, chronic, central disorder of hypersomnolenc...

Physics-Informed Transfer Learning to Enhance Sleep Staging.

OBJECTIVE: At-home sleep staging using wearable medical sensors poses a viable alternative to in-hos...

Predicting the impact of CPAP on brain health: A study using the sleep EEG-derived brain age index.

OBJECTIVE: This longitudinal study investigated potential positive impact of CPAP treatment on brain...

Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity.

BACKGROUND: /Objective: Automatic apnea/hypopnea events classification, crucial for clinical applica...

Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring.

Manual sleep staging (MSS) using polysomnography is a time-consuming task, requires significant trai...

Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders.

Deep learning methods have gained significant attention in sleep science. This study aimed to assess...

SelANet: decision-assisting selective sleep apnea detection based on confidence score.

BACKGROUND: One of the most common sleep disorders is sleep apnea syndrome. To diagnose sleep apnea ...

A robot intervention for adults with ADHD and insomnia-A mixed-method proof-of-concept study.

OBJECTIVE: To investigate individual effects of a three-week sleep robot intervention in adults with...

Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry.

Obstructive sleep apnea (OSA) is a serious medical condition with a high prevalence, although diagno...

Automatic stridor detection using small training set via patch-wise few-shot learning for diagnosis of multiple system atrophy.

Stridor is a rare but important non-motor symptom that can support the diagnosis and prediction of w...

Detection of preceding sleep apnea using ECG spectrogram during CPAP titration night: A novel machine-learning and bag-of-features framework.

Obstructive sleep apnea (OSA) has a heavy health-related burden on patients and the healthcare syste...

Challenges of Applying Automated Polysomnography Scoring at Scale.

Automatic polysomnography analysis can be leveraged to shorten scoring times, reduce associated cost...

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