新しいAIモデルが睡眠の全夜分析を高精度で実施(New AI Model Analyzes Full Night of Sleep With High Accuracy in Largest Study of Its Kind)

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2025-03-17 マウントサイナイ医療システム(MSHS)

マウントサイナイのIcahn医学大学の研究者たちは、睡眠解析の分野で画期的なAIモデル「Patch Foundational Transformer for Sleep(PFTSleep)」を開発しました。このモデルは、脳波、筋活動、心拍数、呼吸パターンなどの生理学的信号を解析し、一晩の睡眠全体を高精度で評価します。従来の手法では、短時間の睡眠データを手動で評価するか、短時間のデータのみを解析するAIモデルに依存していましたが、PFTSleepは一晩全体の睡眠データを解析することで、より詳細で一貫性のある結果を提供します。この研究は、1,011,192時間の睡眠データを解析し、睡眠研究としては最大規模となりました。PFTSleepは、自己教師あり学習法を用いており、生理学的信号から直接関連する臨床的特徴を学習します。これにより、睡眠段階の分類だけでなく、将来的には睡眠時無呼吸症候群の検出や睡眠の質に関連する健康リスクの評価など、臨床応用の可能性が広がります。研究者たちは、このAIツールが臨床専門家を補完し、睡眠解析の効率化と標準化に寄与すると期待しています。

<関連情報>

一晩中、多チャンネルの睡眠検査データを活用した基礎変換器が、睡眠段階を正確に分類する A foundational transformer leveraging full night, multichannel sleep study data accurately classifies sleep stages

Benjamin Fox, Joy Jiang, Sajila Wickramaratne, Patricia Kovatch, Mayte Suarez-Farinas, Neomi A Shah, Ankit Parekh, Girish N Nadkarni
Sleep  Published:13 March 2025
DOI:https://doi.org/10.1093/sleep/zsaf061

新しいAIモデルが睡眠の全夜分析を高精度で実施(New AI Model Analyzes Full Night of Sleep With High Accuracy in Largest Study of Its Kind)
Graphical Abstract

Abstract

Study Objectives
To evaluate whether a foundational transformer using 8-hour, multichannel polysomnogram (PSG) data can effectively encode signals and classify sleep stages with state-of-the-art performance.

Methods
The Sleep Heart Health Study, Wisconsin Sleep Cohort, and Osteoporotic Fractures in Men (MrOS) Study Visit 1 were used for training, and the Multi-Ethnic Study of Atherosclerosis (MESA), Apnea Positive Pressure Long-term Efficacy Study (APPLES), and MrOS visit 2 served as independent test sets. We developed PFTSleep, a self-supervised foundational transformer that encodes full night sleep studies with brain, movement, cardiac, oxygen, and respiratory channels. These representations were used to train another model to classify sleep stages. We compared our results to existing methods, examined differences in performance by varying channel input data and training dataset size, and investigated an AI explainability tool to analyze decision processes.

Results
PFTSleep was trained with 13,888 sleep studies and tested on 4,169 independent studies. Cohen’s Kappa scores were 0.81 for our held-out set, 0.59 for APPLES, 0.60 for MESA, and 0.75 for MrOS Visit 2. Performance increases to 0.76 on a held-out MESA set when MESA is included in the training of the classifier head but not the transformer. Compared to other state-of-the-art AI models, our model shows high performance across diverse datasets while only using task agnostic PSG representations from a foundational transformer as input for sleep stage classification.

Conclusions
Full night, multichannel PSG representations from a foundational transformer enable accurate sleep stage classification comparable to state-of-the-art AI methods across diverse datasets.

医療・健康
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