Wu Menglu, Zhang Wei, Guo Zhenni, Song Jianing, Zeng Yuhong, Huang Yuyu, Yang Yi, Zhang Pandeng, Liu Jia
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China.
University of Chinese Academy of Sciences, Beijing, People's Republic of China.
Physiol Meas. 2021 Jul 28;42(7). doi: 10.1088/1361-6579/ac0e81.
. A previous study has shown that a data-driven approach can significantly improve the discriminative power of transfer function analysis (TFA) used to differentiate between normal and impaired cerebral autoregulation (CA) in two groups of data. The data was collected from both healthy subjects (assumed to have normal CA) and symptomatic patients with severe stenosis (assumed to have impaired CA). However, the sample size of the labeled data was relatively small, owing to the difficulty in data collection. Therefore, in this proof-of-concept study, we investigate the feasibility of using an unsupervised learning model to differentiate between normal and impaired CA on TFA variables without requiring labeled data for learning.. Continuous arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV), which were recorded simultaneously for approximately 10 min, were included from 148 subjects (41 healthy subjects, 31 with mild stenosis, 13 with moderate stenosis, 22 asymptomatic patients with severe stenosis, and 41 symptomatic patients with severe stenosis). Tiecks' model was used to generate surrogate data with normal and impaired CA. A recently proposed unsupervised learning model was optimized and applied to separate the normal and impaired CA for both the surrogate data and real data.. It achieved 98.9% and 74.1% accuracy for the surrogate and real data, respectively.. To our knowledge, this is the first attempt to employ an unsupervised data-driven approach to assess CA using TFA. This method enables the development of a classifier to determine the status of CA, which is currently lacking.
一项先前的研究表明，数据驱动方法可显著提高传递函数分析（TFA）的辨别力，该分析用于在两组数据中区分正常和受损的脑自动调节（CA）。数据收集自健康受试者（假定具有正常CA）和患有严重狭窄的有症状患者（假定具有受损CA）。然而，由于数据收集困难，标记数据的样本量相对较小。因此，在这项概念验证研究中，我们研究了使用无监督学习模型在TFA变量上区分正常和受损CA的可行性，而无需用于学习的标记数据。纳入了148名受试者同时记录约10分钟的连续动脉血压（ABP）和脑血流速度（CBFV），其中包括41名健康受试者、31名轻度狭窄患者、13名中度狭窄患者、22名无症状严重狭窄患者和41名有症状严重狭窄患者。使用蒂克斯模型生成具有正常和受损CA的替代数据。对最近提出的无监督学习模型进行了优化，并应用于区分替代数据和真实数据中的正常和受损CA。它在替代数据和真实数据上的准确率分别达到了98.9%和74.1%。据我们所知，这是首次尝试采用无监督数据驱动方法使用TFA评估CA。这种方法能够开发一种分类器来确定目前尚缺乏的CA状态。
