Deep analysis of EIT dataset to classify apnea and non-apnea cases in neonatal patients

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

Unexpected Magnetism of Small Silver Clusters

The ground-state electronic, structural, and magnetic properties of small silver clusters, Ag$_n$ (2$\le$n$\le$22), have been studied using a linear combination of atomic Gaussian-type orbitals within the density functional theory. The results show that the silver atoms, which are diamagnetic in bulk environment, can be magnetic when they are grouped together in clusters. The Ag$_{13}$ cluster with icosahedral symmetry has the highest magnetic moment per atom among the studied silver clusters. The cluster symmetry and the reduced coordination number specific of small clusters reveal as a fundamental factor for the onset of the magnetism.Comment: 4 pages, 4 figure

Deep Analysis of EIT Dataset to Classify Apnea and Non-apnea Cases in Neonatal Patients

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

Replicating Nanostructures on Silicon by Low Energy Ion Beams

We report on a nanoscale patterning method on Si substrates using self-assembled metal islands and low-energy ion-beam irradiation. The Si nanostructures produced on the Si substrate have a one-to-one correspondence with the self-assembled metal (Ag, Au, Pt) nanoislands initially grown on the substrate. The surface morphology and the structure of the irradiated surface were studied by high-resolution transmission electron microscopy (HRTEM). TEM images of ion-beam irradiated samples show the formation of sawtooth-like structures on Si. Removing metal islands and the ion-beam induced amorphous Si by etching, we obtain a crystalline nanostructure of Si. The smallest structures emit red light when exposed to a UV light. The size of the nanostructures on Si is governed by the size of the self-assembled metal nanoparticles grown on the substrate for this replica nanopatterning. The method can easily be extended for tuning the size of the Si nanostructures by the proper choice of the metal nanoparticles and the ion energy in ion-irradiation. It is suggested that off-normal irradiation can also be used for tuning the size of the nanostructures.Comment: 12 pages, 7 figures, regular paper submitted to Nanotechnolog

Optimized breath detection algorithm in electrical impedance tomography

This paper defines a method for optimizing the breath delineation algorithms used in Electrical Impedance Tomography (EIT). In lung EIT the identification of the breath phases is central for generating tidal impedance variation images, subsequent data analysis and clinical evaluation. The optimisation of these algorithms is particularly important in neonatal care since the existing breath detectors developed for adults may give insufficient reliability in neonates due to their very irregular breathing pattern. Our approach is generic in the sense that it relies on the definition of a gold standard and the associated definition of detector sensitivity and specificity, an optimisation criterion and a set of detector parameters to be investigated. The gold standard has been defined by 11 clinicians with previous experience with EIT and the performance of our approach is described and validated using a neonatal EIT dataset acquired within the EU-funded CRADL project. Three different algorithms are proposed that are improving the breath detector performance by adding conditions on 1) maximum tidal breath rate obtained from zero-crossings of the EIT breathing signal, 2) minimum tidal impedance amplitude and 3) minimum tidal breath rate obtained from Time-Frequency (TF) analysis. As a baseline the zero crossing algorithm has been used with some default parameters based on the Swisstom EIT device. Based on the gold standard, the most crucial parameters of the proposed algorithms are optimised by using a simple exhaustive search and a weighted metric defined in connection with the Receiver Operating Characterics (ROC). This provides a practical way to achieve any desirable trade-off between the sensitivity and the specificity of the detectors. [Abstract copyright: © 2018 Institute of Physics and Engineering in Medicine.

Clinical performance of a novel textile interface for neonatal chest electrical impedance tomography

Objective: Critically ill neonates and infants might particularly benefit from continuous chest electrical impedance tomography (EIT) monitoring at the bedside. In this study a textile 32-electrode interface for neonatal EIT examination has been developed and tested to validate its clinical performance. The objectives were to assess ease of use in a clinical setting, stability of contact impedance at the electrode–skin interface and possible adverse effects. Approach: Thirty preterm infants (gestational age: 30.3 ± 3.9 week (mean ± SD), postnatal age: 13.8 ± 28.2 d, body weight at inclusion: 1727 ± 869 g) were included in this multicentre study. The electrode–skin contact impedances were measured continuously for up to 3 d and analysed during the initial 20-min phase after fastening the belt and during a 10 h measurement interval without any clinical interventions. The skin condition was assessed by attending clinicians. Main results: Our findings imply that the textile electrode interface is suitable for long-term neonatal chest EIT imaging. It does not cause any distress for the preterm infants or discomfort. Stable contact impedance of about 300 Ohm was observed immediately after fastening the electrode belt and during the subsequent 20 min period. A slight increase in contact impedance was observed over time. Tidal variation of contact impedance was less than 5 Ohm. Significance: The availability of a textile 32-electrode belt for neonatal EIT imaging with simple, fast, accurate and reproducible placement on the chest strengthens the potential of EIT to be used for regional lung monitoring in critically ill neonates and infants