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首页> 外文期刊>Informatics in Medicine Unlocked >A case-study of NIRS application for infant cerebral hemodynamic monitoring: A report of data analysis for feature extraction and infant classification into healthy and unhealthy
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A case-study of NIRS application for infant cerebral hemodynamic monitoring: A report of data analysis for feature extraction and infant classification into healthy and unhealthy

机译：NIRS在婴儿脑血流动力学监测中的应用案例研究：特征提取和婴儿分类为健康和不健康的数据分析报告

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This paper reports the results of cerebral hemodynamic data analysis gathered from infant foreheads in selected case-studies. These studies were utilized for extracting relevant features aimed for discriminatory classification of infants into healthy and unhealthy cases. The principal objective of the study was to examine the effectiveness and accuracy of using near-infrared spectroscopy for measuring cerebral blood flow in infants and for health monitoring purposes. A total of 41 infants from several age groups varying from 2?h to several days since birth were participated for experimental data recordings. Both healthy and unhealthy infants of similar age groups were selected for the study. Selection was made without consideration as to the type of disorder in unhealthy infants, or any consideration as to being under any particular monitoring action. Data were collected during the rest state with no external stimulus. Several data analysis approaches were applied including temporal and time-frequency analyses, which were used for feature extraction of hemodynamic data and for identifying selective features to be used during data classification. We utilized SVM for pattern recognition and feature extraction aimed at discriminatory classification of healthy infants from unhealthy cases. This was followed by the application of the t-test for statistical analysis and accuracy evaluation. The results show a 94% accuracy in classification. A clear relationship was also found between oxy- and deoxy-hemoglobin concentration data belonging to healthy infants as shown in 2D data clustering illustration that can be used for infant classification. Similar correlation results were also observed with other physiological data.
机译：本文报告了在选定病例研究中从婴儿额头收集的脑血流动力学数据分析结果。这些研究被用于提取旨在将婴儿区分为健康和不健康病例的相关特征。这项研究的主要目的是检验使用近红外光谱法测量婴儿脑血流量并进行健康监测的有效性和准确性。来自不同年龄段的2个小时至出生后几天内的41个婴儿参加了实验数据记录。选择年龄相似的健康和不健康婴儿作为研究对象。选择时未考虑到不健康婴儿的疾病类型，也未考虑正在采取任何特定的监测措施。在没有外部刺激的静止状态下收集数据。应用了几种数据分析方法，包括时态和时频分析，这些方法用于血液动力学数据的特征提取和识别在数据分类期间要使用的选择性特征。我们利用SVM进行模式识别和特征提取，旨在对健康婴儿与不健康病例进行区分。接下来是将t检验用于统计分析和准确性评估。结果表明分类准确度为94％。如2D数据聚类图所示，在属于健康婴儿的含氧血红蛋白和脱氧血红蛋白浓度数据之间也发现了明确的关系，可用于婴儿分类。与其他生理数据也观察到相似的相关结果。

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