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

Abstract 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.