新しいパルス到着時間と機械学習技術を使用して,血圧の急速な上昇を検出する
Cody P Anderson1, Gwenael Layec1, Song-Young Park1,2
1School of Health and Kinesiology, University of Nebraska at Omaha, Omaha, Nebraska.
American journal of physiology. Heart and circulatory physiology
|September 5, 2025
まとめ
この研究は,新しいウェアラブルデバイスのプラットフォームが,パルス到着時間を使用してシミュレートされた高血圧 (BP) を検出できることを示しています. この技術は高血圧の危機を迅速に特定するのに役立ちます.
科学分野:
- 生物医学工学
- 心血管の生理学
- ウェアラブル テクノロジー
背景:
- 悪性高血圧の危機は迅速に発見され 効果的な介入が必要である.
- 継続的な血圧 (BP) 監視のための現在の方法は,特にウェアラブルアプリケーションでは限られています.
- ウェアラブルデバイスは 非侵襲的なリアルタイム生理学的モニタリングの 可能性を秘めています
研究 の 目的:
- ウェアラブルデバイスのための急性高血圧検出プラットフォーム (AHDP) の初期実現可能性を評価する.
- AHDPが血圧の上昇を正確に検出できるかどうかを判断する.
- 穿戴可能な文脈でBPモニタリングのためのパルス到着時間 (PAT) の有用性を調査する.
主な方法:
- 42人の健康な若者が血圧上昇を誘発するために,血流の制限を伴う同位体握り運動を行いました.
- 脈動到着時間 (PAT) を計算するために,継続的な心電図 (ECG) と光パレットスモグラフィ (PPG) のデータを収集した.
- PATのzスコアを用いたロジスティック回帰モデルは,PR-AUCによって評価された正常血圧状態と高血圧状態を分類した.
主要な成果:
- アイソメトリック運動は血圧を有意に上昇させ (Δ18. 8±15. 1 mmHg),PATを低下させ (Δ5. 6±5. 7 ms) (p<0. 001).
- AHDPモデルは,0. 68 (p<0. 001) の精度リコール領域 (PR-AUC) を達成し,ランダムな確率を超えた成功検出を示した.
- 血圧上昇を分類するために最適のPATzスコアの値 - 3. 87が特定されました.
結論:
- 開発されたAHDPは,実験室でシミュレートされた急性高血圧を検出する実用性を示した.
- ECGとPPGから計算されたPATは ウェアラブルシステムにおける高血圧のバイオマーカーとして有望です
- AHDPのさらなる開発は,高血圧の早期発見のためのウェアラブルデバイスにつながる可能性があります.
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