睡眠時無呼吸症候群の疑いのある成人の心臓呼吸器および身体運動活動による無制限の深層学習ベースの睡眠段階分類

Seiichi Morokuma¹, Toshinari Hayashi², Naoyuki Motomura³

¹Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University.

Proceedings of the Japan Academy. Series B, Physical and biological sciences

|August 31, 2025

まとめ

この研究ではマットレスの下のセンサーを用いたディープラーニングモデルが睡眠段階を正確に分類できることを示していますこの非侵襲的な方法は快適な家庭での睡眠モニタリングに期待されます

科学分野:

生物医学工学
睡眠医学
人工知能

背景:

睡眠障害の診断には睡眠段階の正確な分類が不可欠です
伝統的なポリソムノグラフィー (PSG) は資源密集的で,家庭での広範なモニタリングには適していません.
スケール可能な睡眠分析には非侵襲的なセンサーを用いた方法が必要です

研究の目的:

制限のない睡眠段階の分類のためのディープラーニングの実現可能性を評価する.
マットレスの下のピエゾ電気センサーから心臓呼吸器と身体の動きのデータを利用する
睡眠段階を定めるための双方向の長期短期記憶 (LSTM) ネットワークの性能を評価する.

主な方法:

心臓呼吸器信号,変性,コップリング,身体の動きを PSGとマットレスの下のセンサーで同時に取得する.
5つの睡眠段階を予測する双方向のLSTMネットワークの開発と応用.
モデルの最適化のための5つの異なる入力機能の組み合わせをテストします.

主要な成果:

心臓呼吸器の変動を含む6つのパラメータを使用した最も優れたモデルは,0. 70 ± 0. 05のバランスのとれた精度を達成しました.
性能指標には,0. 40 ± 0. 12のコーエンのk と,0. 62 ± 0. 08のF1スコアが含まれています.
デミング回帰とBland- Altman分析では,モデル推定値とPSGで決定された睡眠パラメータの間の有意な相関 (r = 0. 426- 0. 695) と低いバイアスが示されました.

結論:

マットレスの下のセンサーを用いたディープラーニングによる睡眠段階の分類は実現可能で効果的です
提案されたアプローチは,家庭内の睡眠監視能力を拡大する可能性を示しています.
この技術により睡眠障害の検診と管理がより容易かつ広く行えるようになるでしょう

キーワード:

バリストカルディオグラフィーディープラーニング睡眠段階のスコア付け

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