BP-DeepONet: A New Method for Cuffless Blood Pressure Estimation Using Physics-Informed DeepONet

Core Concepts

Proposing a novel physics-informed DeepONet framework to predict ABP waveforms continuously, improving cardiovascular health monitoring.

Abstract

新しい物理情報DeepONetフレームワークを提案して、連続的にABP波形を予測し、心血管健康のモニタリングを向上させる。
Cardiovascular diseases (CVDs) are the leading cause of death globally, with blood pressure serving as a crucial indicator. Arterial blood pressure (ABP) waveforms provide continuous pressure measurements throughout the cardiac cycle and offer valuable diagnostic insights. The study proposes a novel framework based on the physics-informed DeepONet approach to predict ABP waveforms. Unlike previous methods, this approach requires the predicted ABP waveforms to satisfy specific equations and boundary conditions. The framework can predict ABP waveforms continuously within the artery segment being simulated. Incorporating Windkessel boundary condition allows for generating natural physical reflection waves closely resembling real-world measurements. Meta-learning is introduced to estimate model hyper-parameters during training process.
According to WHO, CVDs are responsible for 32% of global deaths in 2019, with high blood pressure being a predominant cause. Monitoring ABP waveforms continuously is crucial for disease detection and prevention. Invasive methods are risky and expensive, making non-invasive methods desirable for daily monitoring of ABP waveforms. Deep learning-based methods have gained interest in estimating ABP from physiological signals like ECG and PPG.
Deep learning models have been used to estimate systolic and diastolic blood pressure, as well as entire ABP waveforms using various neural network architectures. These models are trained by minimizing differences between ground truth labels and network predictions obtained from physiological signals like ECG and PPG.
The proposed BP-DeepONet aims to predict ABP waveforms at different locations in arteries based on Navier-Stokes equation governing blood flow dynamics. The model incorporates physics-informed learning to map physiological signals to PDE solutions accurately predicting hemodynamics in radial arteries.
Numerical experiments validate the effectiveness of the proposed method by solving one instance of Navier-Stokes equation using MacCormack scheme as reference solution. A residual network is trained using time-periodic conditions and boundary constraints showing close agreement with simulated solutions.

Stats

Cardiovascular diseases account for 32% of global deaths in 2019.
Proposed BP-DeepONet predicts ABP waveforms continuously within arteries based on Navier-Stokes equation.
Incorporates Windkessel boundary condition for generating natural reflection waves resembling real-world measurements.
Meta-learning introduced to estimate model hyper-parameters during training process.

Quotes

"Incorporating the Windkessel boundary condition in our solution allows for generating natural physical reflection waves."
"Our proposed method not only accurately predicts continuous ABP waveforms but also outperforms traditional approaches."
"Meta-learning is introduced enabling neural networks to learn these parameters during training process."

Key Insights Distilled From

BP-DeepONet

by Lingfeng Li,... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.18886.pdf

Deeper Inquiries

How can non-invasive methods like BP-DeepONet improve long-term monitoring of cardiovascular health beyond traditional invasive techniques

非侵襲的な手法、例えばBP-DeepONetは、従来の侵襲的技術を超えて心血管健康の長期モニタリングをどのように改善できるでしょうか？
BP-DeepONetなどの深層学習ベースのモデルは、連続した血圧波形を予測することが可能であり、これにより患者が日常生活や医療施設外でも自身の血圧を監視することが容易になります。このような非侵襲的手法は、定期的かつ持続的に心臓や血管系統へアクセスし、重要なバイオマーカーである平均動脈圧（MAP）や拡張期および収縮期血圧（SBPおよびDBP）を推定するために役立ちます。従来の侵襲的方法では得られない連続した情報提供が可能となり、個々人レベルでカスタマイズされた治療計画や早期警告システムの開発に貢献します。さらに、長期間データを収集し分析することで個々人ごとのパターンやトレンドを把握し、将来起こり得る問題を予測して予防措置を取ることも可能です。

What counterarguments exist against relying solely on deep learning-based models like BP-DeepONet for accurate prediction of complex physiological phenomena

BP-DeepONet のような深層学習ベースのモデルだけに頼って正確な生理現象予測が行われる際に存在する反対意見は何ですか？
深層学習ベースのモデルは高度かつ柔軟性がありますが、「ブラックボックス」と呼ばれる特性も持っています。これらのアプローチでは内部メカニズムや決定プロセスが不透明であり解釈性が低い場合があります。そのため専門家以外から見た場合、「どうしてその結果・判断・推奨 」 されているか理解しずらく信頼性へ影響与え得ます。「ブラック ボックス」特有問題点から出発すれば誰でも使えそうだけど実際上適用難易度高く,また精度面でも限界あったりします。

How might advancements in physics-informed machine learning techniques like those used in BP-DeepONet impact other fields outside healthcare technology

物理情報付き機械学習技術（例：BP-DeepONet） の進歩は医療技術以外他分野へどんな影響 を及ぼす可能性 ？
物理情報付き機械学 研究成果応用範囲広く, 医 療 技 術 分野以外多数利用 可能. 特 别 工 学 開 発 設 計 自 動化 生産 シ ス テ ム 改 善 操作 最適化等 多岐 応用 可能. 特 别 安全 性向 上工事建設業界使用安全基準厳格要求 高まった今日 向上効率コスト削減目指す必要 不可欠.また気象災害管理エリア天気変動迅速対応必要地域自然災害被害最小限抑制方策打ち出せ 功能大幅貢 獻考え られ .更 新製品サーヴィ ス 提供企業競争力向上新市場開拓戦略展開需要増加先端技术導入企業成功道案内 影響与え 得 。