脑生理信号的多元线性时间序列建模和预测:统计模型的回顾及其对人类信号分析的影响。

Frontiers in network physiology Pub Date : 2025-04-16 eCollection Date: 2025-01-01 DOI:10.3389/fnetp.2025.1551043
Nuray Vakitbilir, Amanjyot Singh Sainbhi, Abrar Islam, Alwyn Gomez, Kevin Yuwa Stein, Logan Froese, Tobias Bergmann, Davis McClarty, Rahul Raj, Frederick Adam Zeiler
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引用次数: 0

摘要

大脑生理信号体现了复杂的神经、血管和代谢过程,为了解大脑的动态本质提供了有价值的见解。对这些信号的深刻理解和分析对于揭示大脑的复杂性,精确识别模式和异常是必不可少的。因此,脑生理学计算模型的进步对于探索可测量信号与潜在生理状态之间的联系至关重要。这篇综述提供了计算模型的详细解释,包括它们的数学公式,并讨论了它们与大脑生理动力学分析的相关性。它强调了线性多元统计模型的重要性,特别是自回归(AR)模型和卡尔曼滤波,在时间序列建模和预测大脑过程。本文重点介绍了AR模型和卡尔曼滤波等多元统计模型的分析和工作原理。这些模型被检验了它们捕捉大脑参数之间复杂关系的能力,提供了大脑功能的整体表征。多元统计模型的使用可以捕获大脑生理信号之间的复杂关系。这些模型通过表示复杂的神经、血管和代谢过程,为大脑的动态特性提供了有价值的见解。这篇综述强调了使用计算模型来理解大脑生理学的临床意义,同时也承认了固有的局限性,包括对固定数据的需求、高维挑战、计算复杂性和有限的预测范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multivariate linear time-series modeling and prediction of cerebral physiologic signals: review of statistical models and implications for human signal analytics.

Cerebral physiological signals embody complex neural, vascular, and metabolic processes that provide valuable insight into the brain's dynamic nature. Profound comprehension and analysis of these signals are essential for unraveling cerebral intricacies, enabling precise identification of patterns and anomalies. Therefore, the advancement of computational models in cerebral physiology is pivotal for exploring the links between measurable signals and underlying physiological states. This review provides a detailed explanation of computational models, including their mathematical formulations, and discusses their relevance to the analysis of cerebral physiology dynamics. It emphasizes the importance of linear multivariate statistical models, particularly autoregressive (AR) models and the Kalman filter, in time series modeling and prediction of cerebral processes. The review focuses on the analysis and operational principles of multivariate statistical models such as AR models and the Kalman filter. These models are examined for their ability to capture intricate relationships among cerebral parameters, offering a holistic representation of brain function. The use of multivariate statistical models enables the capturing of complex relationships among cerebral physiological signals. These models provide valuable insights into the dynamic nature of the brain by representing intricate neural, vascular, and metabolic processes. The review highlights the clinical implications of using computational models to understand cerebral physiology, while also acknowledging the inherent limitations, including the need for stationary data, challenges with high dimensionality, computational complexity, and limited forecasting horizons.

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