基于机器学习的多模态脑氧饱和度预测。

IF 2.2 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Kwang-Sig Lee, Su Jin Kim, Dong Cheol Kim, Sang-Hyun Park, Dong-Hyun Jang, Eung Hwi Kim, YoungShin Kang, Sijin Lee, Sung Woo Lee
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引用次数: 0

摘要

本研究利用多模态血氧饱和度传感器提供的近红外波形数据,开发了基于机器学习的算法,有助于准确预测脑血氧饱和度。数据来自常用脑血氧仪 Masimo O3™ 区域血氧仪(美国公司)和多模态脑血氧仪 Votem(韩国公司)的 150,000 次观测。在这些观测数据中,112,500 个(75%)和 37,500 个(25%)分别用于训练集和测试集。因变量是 Masimo O3™ 的大脑血氧饱和度值(0-100%)。自变量是测量时间(0-300,000 毫秒)和来自 Votem 的 16 位十进制振幅值(红外线和红外线)(0-65,535)。对于前额右侧部分,随机森林的均方根误差(0.06)远小于线性回归(1.22)和具有一个、两个或三个隐藏层的人工神经网络(2.58)。额头左侧的结果与此类似,即随机森林(0.05)与逻辑回归(1.22)和具有一个、两个或三个隐藏层的人工神经网络(2.97)的比较。利用多模态脑氧仪的数据,机器学习有助于准确预测脑氧饱和度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning-based prediction of cerebral oxygen saturation based on multi-modal cerebral oximetry data.

This study develops machine learning-based algorithms that facilitate accurate prediction of cerebral oxygen saturation using waveform data in the near-infrared range from a multi-modal oxygen saturation sensor. Data were obtained from 150,000 observations of a popular cerebral oximeter, Masimo O3™ regional oximetry (Co., United States) and a multi-modal cerebral oximeter, Votem (Inc., Korea). Among these observations, 112,500 (75%) and 37,500 (25%) were used for training and test sets, respectively. The dependent variable was the cerebral oxygen saturation value from the Masimo O3™ (0-100%). The independent variables were the time of measurement (0-300,000 ms) and the 16-bit decimal amplitudes values (infrared and red) from Votem (0-65,535). For the right part of the forehead, the root mean square error of the random forest (0.06) was much smaller than those of linear regression (1.22) and the artificial neural network with one, two or three hidden layers (2.58). The result was similar for the left part of forehead, that is, random forest (0.05) vs logistic regression (1.22) and the artificial neural network with one, two or three hidden layers (2.97). Machine learning aids in accurately predicting of cerebral oxygen saturation, employing the data from a multi-modal cerebral oximeter.

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来源期刊
Health Informatics Journal
Health Informatics Journal HEALTH CARE SCIENCES & SERVICES-MEDICAL INFORMATICS
CiteScore
7.80
自引率
6.70%
发文量
80
审稿时长
6 months
期刊介绍: Health Informatics Journal is an international peer-reviewed journal. All papers submitted to Health Informatics Journal are subject to peer review by members of a carefully appointed editorial board. The journal operates a conventional single-blind reviewing policy in which the reviewer’s name is always concealed from the submitting author.
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