Analysis and compensation for errors in electrical impedance tomography images and ventilation-­related measures due to serial data collection

Electrical impedance tomography (EIT) is increasingly being used as a bedside tool for monitoring regional lung ventilation. However, most clinical systems use serial data collection which, if uncorrected, results in image distortion, particularly at high breathing rates. The objective of this study was to determine the extent to which this affects derived parameters. Raw EIT data were acquired with the GOE­MF II EIT device (CareFusion, Höchberg, Germany) at a scan rate of 13 images/s during both spontaneous breathing and mechanical ventilation. Boundary data for periods of undisturbed tidal breathing were corrected for serial data collection errors using a Fourier based algorithm. Images were reconstructed for both the corrected and original data using the GREIT algorithm, and parameters describing the filling characteristics of the right and left lung derived on a breath by breath basis. Values from the original and corrected data were compared using paired t­ tests. Of the 33 data sets, 23 showed significant differences in filling index for at least one region, 11 had significant differences in calculated tidal impedance change and 12 had significantly different filling fractions (p = 0.05). We conclude that serial collection errors should be corrected before image reconstruction to avoid clinically misleading results

Ultrasound scanner—Teaching tool

Ultrasound imaging, one of the most widely used diagnostic modalities, anchors the fields of medicine, physics, and engineering. In university classrooms, however, ultrasound imaging is often taught passively with a lack of practical element as the clinical machines are not easily available and there are very few alternative teaching tools available on the market. As part of an undergraduate student project, we have developed a teaching toolkit featuring an inexpensive ultrasonic range finder to demonstrate the pulse-echo imaging process. The primary focus is the construction of equipment to enable known pedagogic principles (relating to active learning) to be applied to the subject area of ultrasound. Although operating at an acoustic frequency considerably lower than that employed clinically (and therefore achieving a much lower spatial resolution), the toolkit provides students with large observable effects while keeping cost to the minimum. Completed with an easy-to-use user interface and a set of carefully designed supplementary material (https://stacks.iop.org/EJP/42/055703/mmedia) including worksheets and lab technician guide, this toolkit aims to teach students the fundamental principles of ultrasound imaging via hands-on practice. We have designed it to be cheap, easy to set up, and portable. The effectiveness and impact of the toolkit were evaluated by ten undergraduate students who responded in the form of satisfaction questionnaires. To minimise the selection bias, we chose five students who had received no prior university-based instruction on ultrasound and five third-year biomedical engineering students who had learned about the topic previously. They demonstrated a strong interest in using the toolkit for a lab session and described it as user-friendly and highly engaging

An Imaged-Based Method for Universal Performance Evaluation of Electrical Impedance Tomography Systems

This paper describes a simple and reproducible methodology for universal evaluation of the performance of electrical impedance tomography (EIT) systems using reconstructed images. Based on objective full referencing (FR), the method provides a visually distinguishable hot colormap and two new FR metrics, the global and the more specific region of interest, to address the issues where common electrical parameters are not directly related to the quality of EIT images. A passive 16 electrode EIT system using an application specific integrated circuit front-end was used to evaluate the proposed method. The measured results show, both visually and in terms of the proposed FR metrics, the impact on recorded EIT images with different design parameters and non-idealities. The paper also compares the image results of a passive electrode system with a matched single variable active electrode system and demonstrates the merit of an active electrode system for noise interference

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

Co-ordinating assessment across a programme

Assessment within a degree programme is critical for providing summative grades and formative feedback on specific pieces of work. Incorporating different forms of assessment into a programme provides students with opportunities to develop a wide range of skills beyond core disciplinary knowledge. Examples include research-based assessment, outward facing assessments aimed at different audiences, and authentic assessment linked to professional workplace practice. The opportunity to develop relevant professional skills is particularly important in an accredited engineering programme. The modular approach to programme development, prevalent in the UK, where different modules are often developed autonomously and assessed independently can make it difficult to introduce broader, creative assessment practices and can lead to heavy reliance on one method. For example, all module organisers might feel that their module is best assessed through written reports. Even if this is the most appropriate assessment mechanism for each individual module, we postulate that over the programme as a whole, students might learn more if they are required to submit a range of different types of outputs. By spreading this assessment portfolio across modules, we can develop and test a wider range of skills even while reducing the total assessment load. We will give examples from a programme that uses a combination of traditional assessments, authentic workplace-like assessments, research-based assessments, and assessment for different audiences. The paper examines the individual module compromises which may need to be made if assessment is to be seen holistically, to create programme-wide balance to maximise student development

360° peer assessment: improving reliability and engagement

A new more rigorous method for peer-assessment was designed and trialled, aiming to improve the student’s learning experience at UCL and IOE. The “360 degree peer assessment” process involves peers firstly marking and giving feedback to pieces of work and secondly being assessed by the recipients of the feedback on the quality of their marking. The peer marking is anonymous. Tutors monitor the process, but only need to intervene and moderate marks if the recipient disputes the mark and feedback, or if there is disparity of marks for one given piece of work. This approach was trialled with two undergraduate Biomedical engineering modules over four assignments and with one postgraduate education module. The benefits we found for this method include formalising the process for dealing with disputes in peer assessment and improving reliability. Undergraduate students also reported dedicating more time to peer assessment, they learnt more about the assessment process and were motivated to read feedback. The postgraduate students were motivated to undertake peer assessment but remained concerned about reliability and they did not appreciate that teacher moderation would occur when needed. The system is still under development. We aim to develop this methodology and use it increasingly in other modules and disciplines, explore for which types of coursework this approach is most suitable, and assess impact on student and staff workload. The authors want to thank to the IOE/UCL Strategic Partnership Teaching and Learning Fund and the Medical Physics and Biomedical Engineering Department UCL who partially funded this project

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

Analysis and compensation for errors in electrical impedance tomography images and ventilation-­related measures due to serial data collection

Electrical impedance tomography (EIT) is increasingly being used as a bedside tool for monitoring regional lung ventilation. However, most clinical systems use serial data collection which, if uncorrected, results in image distortion, particularly at high breathing rates. The objective of this study was to determine the extent to which this affects derived parameters. Raw EIT data were acquired with the GOE­MF II EIT device (CareFusion, Höchberg, Germany) at a scan rate of 13 images/s during both spontaneous breathing and mechanical ventilation. Boundary data for periods of undisturbed tidal breathing were corrected for serial data collection errors using a Fourier based algorithm. Images were reconstructed for both the corrected and original data using the GREIT algorithm, and parameters describing the filling characteristics of the right and left lung derived on a breath by breath basis. Values from the original and corrected data were compared using paired t­ tests. Of the 33 data sets, 23 showed significant differences in filling index for at least one region, 11 had significant differences in calculated tidal impedance change and 12 had significantly different filling fractions (p = 0.05). We conclude that serial collection errors should be corrected before image reconstruction to avoid clinically misleading results

Compressive sensing in electrical impedance tomography for breathing monitoring

Continuous functional thorax monitoring using EIT has been extensively researched. A limiting factor in high temporal resolution, three dimensional, and fast EIT is the handling of the volume of raw impedance data produced for transmission and storage. Owing to the periodicity of breathing that may be reflected in EIT boundary measurements, data dimensionality may be reduced efficiently at the time of sampling using compressed sensing techniques. Measurements using a 32-electrode 48-frame-per-second EIT system from 30 neonates were post-processed to simulate random demodulation acquisition method on 2000 frames for compression ratios (CRs) ranging from 2-100. Sparse reconstruction was performed by solving the basis pursuit problem using SPGL1 package. The global impedance data was used in the subsequent studies. The signal to noise ratio (SNR) for the entire frequency band (0 Hz - 24 Hz) and three local frequency bands were analysed. A breath detection algorithm was applied to traces and the subsequent error-rates were calculated while considering the outcome of the algorithm applied to a down-sampled and linearly interpolated version of the traces as the baseline. SNR degradation was proportional with CR. The mean degradation for 0 Hz - 8 Hz was below ~15 dB for all CRs. The error-rates in the outcome of the breath detection algorithm in the case of decompressed traces were lower than those of the associated down-sampled traces for CR≥25, corresponding to sub-Nyquist rate for breathing. For instance, the mean error-rate associated with CR = 50 was ~60% lower than that of the corresponding down-sampled traces. To the best of our knowledge, no other study has evaluated compressive sensing on boundary impedance data in EIT. While further research should be directed at optimising the acquisition and decompression techniques for this application, this contribution serves as the baseline for future efforts. [Abstract copyright: Creative Commons Attribution license.

Effect of routine suction on lung aeration in critically ill neonates and young infants measured with electrical impedance tomography

Endotracheal suctioning is a widely used procedure to remove secretions from the airways of ventilated patients. Despite its prevalence, regional effects of this maneuver have seldom been studied. In this study, we explore its effects on regional lung aeration in neonates and young infants using electrical impedance tomography (EIT) as part of the large EU-funded multicenter observational study CRADL. 200 neonates and young infants in intensive care units were monitored with EIT for up to 72 h. EIT parameters were calculated to detect changes in ventilation distribution, ventilation inhomogeneity and ventilation quantity on a breath-by-breath level 5–10 min before and after suctioning. The intratidal change in aeration over time was investigated by means of regional expiratory time constants calculated from all respiratory cycles using an innovative procedure and visualized by 2D maps of the thoracic cross-section. 344 tracheal suctioning events from 51 patients could be analyzed. They showed no or very small changes of EIT parameters, with a dorsal shift of the center of ventilation by 0.5% of the chest diameter and a 7% decrease of tidal impedance variation after suctioning. Regional time constants did not change significantly. Routine suctioning led to EIT- detectable but merely small changes of the ventilation distribution in this study population. While still a measure requiring further study, the time constant maps may help clinicians interpret ventilationmechanics in specific cases