Article

A Unified Flexible Large Polysomnography Model for Sleep Staging and Brain Disorder Diagnosis

2024-12-11

Abstract excerpt

Sleep disorders affect billions worldwide, yet clinical polysomnography (PSG) analysis remains hindered by labor-intensive manual scoring and limited generalizability of automated sleep staging tools across heterogeneous protocols. We present LPSGM, a large-scale PSG model designed to address two critical challenges in sleep medicine: cross-center generalization and adaptable diagnosis of neuropsychiatric disorder...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 09436a24-161f-520d-94cb-3a001ef17a2f
DOI: 10.1101/2024.12.11.24318815

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A Unified Flexible Large Polysomnography Model for Sleep Staging and Brain Disorder DiagnosisDOI 10.1101/2024.12.11.24318815Read

A Unified Flexible Large Polysomnography Model for Sleep Staging and Mental Disorder DiagnosisSemantic proximity Read

SleepGPT: A Sleep Stage Language Model for Efficient Sleep AssessmentSemantic proximity Read

A Multimodal Sleep Foundation Model for Disease PredictionSemantic proximity Read

A Zero-Burden Sleep Foundation Model Built on Cardiorespiratory Signals from 1,400,000+ Hours of Multi-Ethnic Sleep RecordingsSemantic proximity Read

Multi-modal sleep staging in the clinic for REM sleep behaviour disorderSemantic proximity Read

Multimodal sleep stage classification and label-free abnormality scoring in mid-to-older adultsSemantic proximity Read

Automated Sleep Stage and Event Detection Algorithms Using Quality-Controlled PSG AnnotationsSemantic proximity Read

Sleep Staging Foundation Models Encode Neural Disorder-Related EEG Representations that Generalize to WakefulnessSemantic proximity Read

Expert-level probabilistic breathing event detector informs phenotyping of sleep apnea.Semantic proximity Read

Optimized Machine Learning Algorithms for the Classification and Diagnosis of Sleep DisordersSemantic proximity Read

Advances in Sleep Stage Detection Using Wearable Physiological Signals: A Systematic ReviewSemantic proximity Read

Can We Reduce the Diagnostic Burden of Sleep Disorders? A-Single Centre Study of Subjective and Objective Sleep-Related Diagnostic ParametersSemantic proximity Read