Purpose

This study deals with the computational simulation and inverse analysis of the cooling treatment of the hypoxic-ischemic encephalopathy in neonates. A reduced-order model is implemented for real-time monitoring of the internal body temperatures. The purpose of this study is to sequentially estimate the transient temperatures of the brain and other body regions with reduced uncertainties.

Design/methodology/approach

Pennes’ model was applied in each body element, and Fiala’s blood pool concept was used for the solution of the forward bioheat transfer problem. A state estimation problem was solved with the Sampling Importance Resampling (SIR) algorithm of the particle filter method.

Findings

The particle filter method was stable and accurate for the estimation of the internal body temperatures, even in situations involving large modeling and measurement uncertainties.

Research limitations/implications

The proposed reduced-order model was verified with the results of a high-fidelity model available in the literature. Validation of the proposed model and of the solution of the state estimation problem shall be pursued in the future.

Practical implications

The solution of the state estimation problem with the reduced-order model presented in this paper has great potential to perform as an observer of the brain temperature of neonates, for the analysis and control of the systemic cooling treatment of neonatal hypoxic-ischemic encephalopathy.

Social implications

The main treatment for hypoxic-ischemic encephalopathy in neonates is the cooling of affected regions. Accurate and fast models might allow the development of individualized protocols, as well as control strategies for the cooling treatment.

Originality/value

This paper presents the application of the SIR algorithm for the solution of a state problem during the systemic cooling of a neonate for the treatment of the hypoxic-ischemic encephalopathy.

中文翻译：

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粒子滤波法逆向分析新生儿大脑降温过程

目的

本研究涉及新生儿缺氧缺血性脑病降温治疗的计算模拟和逆向分析。实施降阶模型以实时监测体内温度。本研究的目的是按顺序估计大脑和其他身体区域的瞬态温度，同时降低不确定性。

设计/方法/途径

Pennes的模型被应用到每个身体元素，Fiala的血池概念被用于解决正向生物传热问题。利用粒子滤波法的采样重要性重采样（SIR）算法解决了状态估计问题。

发现

即使在涉及较大建模和测量不确定性的情况下，粒子滤波法也能稳定准确地估算体内温度。

研究局限性/影响

所提出的降阶模型已通过文献中可用的高保真模型的结果进行了验证。未来将继续验证所提出的模型和状态估计问题的解决方案。

实际影响

本文提出的用降阶模型解决状态估计问题具有很大的潜力，可以作为新生儿脑温度的观察者，用于分析和控制新生儿缺氧缺血性脑病的全身降温治疗。

社会影响

新生儿缺氧缺血性脑病的主要治疗方法是对患处降温。准确和快速的模型可能允许开发个性化协议，以及冷却处理的控制策略。

原创性/价值

本文介绍了 SIR 算法在解决新生儿全身降温治疗缺氧缺血性脑病过程中的状态问题的应用。