TY - JOUR
T1 - Deep Analysis of EIT Dataset to Classify Apnea and Non-apnea Cases in Neonatal Patients
AU - Vahabi, Nafiseh
AU - Yerworth, Rebecca
AU - Miedema, Martijn
AU - van Kaam, Anton
AU - Bayford, Richard
AU - Demosthenous, Andreas
N1 - Funding Information: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/R513143/1. Publisher Copyright: © 2013 IEEE.
PY - 2021
Y1 - 2021
N2 - Electrical impedance tomography (EIT) is a non-invasive imaging modality that can provide information about dynamic volume changes in the lung. This type of image does not represent structural lung information but provides changes in regions over time. EIT raw datasets or boundary voltages are comprised of two components, termed real and imaginary parts, due to the nature of cell membranes of the lung tissue. In this paper, we present the first use of EIT boundary voltage data obtained from infants for the automatic detection of apnea using machine learning, and investigate which components contain the main features of apnea events. We selected 15 premature neonates with an episode of apnea in their breathing pattern and applied a hybrid classification model that combines two established methods; a pre-trained transfer learning method with a convolutional neural network with 50 layers deep (ResNet50) architecture, and a support vector machine (SVM) classifier. ResNet50 training was undertaken using an ImageNet dataset. The learnt parameters were fed into the SVM classifier to identify apnea and non-apnea cases from neonates’ EIT datasets. The performance of our classification approach on the real part, the imaginary part and the absolute value of EIT boundary voltage datasets were investigated. We discovered that the imaginary component contained a larger proportion of apnea features.
AB - Electrical impedance tomography (EIT) is a non-invasive imaging modality that can provide information about dynamic volume changes in the lung. This type of image does not represent structural lung information but provides changes in regions over time. EIT raw datasets or boundary voltages are comprised of two components, termed real and imaginary parts, due to the nature of cell membranes of the lung tissue. In this paper, we present the first use of EIT boundary voltage data obtained from infants for the automatic detection of apnea using machine learning, and investigate which components contain the main features of apnea events. We selected 15 premature neonates with an episode of apnea in their breathing pattern and applied a hybrid classification model that combines two established methods; a pre-trained transfer learning method with a convolutional neural network with 50 layers deep (ResNet50) architecture, and a support vector machine (SVM) classifier. ResNet50 training was undertaken using an ImageNet dataset. The learnt parameters were fed into the SVM classifier to identify apnea and non-apnea cases from neonates’ EIT datasets. The performance of our classification approach on the real part, the imaginary part and the absolute value of EIT boundary voltage datasets were investigated. We discovered that the imaginary component contained a larger proportion of apnea features.
KW - Apnea classification algorithm
KW - EIT data analysis
KW - electrical impedance tomography
KW - pre-trained ResNet50
KW - transfer learning algorithm
UR - http://www.scopus.com/inward/record.url?scp=85100786806&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/ACCESS.2021.3056558
DO - https://doi.org/10.1109/ACCESS.2021.3056558
M3 - Article
SN - 2169-3536
VL - 9
SP - 25131
EP - 25139
JO - IEEE Access
JF - IEEE Access
M1 - 9344679
ER -