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相关概念视频

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

531
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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通过混合神经网络,经验波形变换和贝叶斯优化来估计有效的连接性.

Milad Esmaeil-Zadeh, Morteza Fattahi, Mohammad Soltani-Gol

    IEEE journal of biomedical and health informatics
    |October 26, 2023
    PubMed
    概括

    这项研究引入了一种新的混合神经网络模型,用于测量非线性有效的大脑连接,在准确性和噪声强度方面表现优于现有的方法. 该模型有效地处理非静止的EEG信号,推进大脑功能研究.

    科学领域:

    • 神经科学是一个神经科学.
    • 计算神经科学是一种神经科学.
    • 信号处理 信号处理

    背景情况:

    • 准确测量非线性有效的大脑连接对于理解大脑功能至关重要.
    • 现有的方法难以处理非静止信号,超参数选择和时间滞后的确定.
    • 电脑电图 (EEG) 信号本质上是非静止的,这给连接性分析带来了挑战.

    研究的目的:

    • 提出一种新的混合神经网络模型,用于增强非线性有效连接测量.
    • 解决现有方法在处理非静态数据和参数选择方面的局限性.
    • 评估模型在模拟和真实EEG数据上的性能,包括临床应用.

    主要方法:

    • 这是一个混合模型,结合了经验波纹变换 (EWT) 和长短期记忆 (LSTM) 网络.
    • 贝叶斯优化 (BO) 用于自动选择最佳超参数和时间滞后.
    • 一个新的算法来选择可概括的权重来提高模型的稳定性.
    • 使用模拟数据和来自ADHD和健康受试者的真实EEG数据进行评估.

    主要成果:

    • 拟议的EWT-LSTM模型与各种神经网络 (LSTM,CNN-LSTM,GRU,RNN,MLP) 和传统方法 (LGC,KGC,PDC,DTF) 相比显示出更高的性能.

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  • 该模型表现出显著的抗噪强度,在噪音条件下准确识别大脑连接.
  • 对ADHD患者有效连接的分析揭示了与以前的研究一致的模式.

    • 结论:

    • 开发的混合EWT-LSTM模型为非静态EEG数据中的非线性有效连接分析提供了强大而稳健的解决方案.
    • 这种方法推进了对大脑动态的研究,并且在临床神经科学中具有潜在的应用,特别是在理解像ADHD这样的障碍方面.
    • 该模型处理噪声和优化参数的能力使其成为未来大脑连接研究的宝贵工具.