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29 results on '"Dunne, Eoghan"'

5. Implantable Pressure-Sensing Devices for Monitoring Abdominal Aortic Aneurysms in Post-Endovascular Aneurysm Repair.

Author

Silva, Nuno P., Amin, Bilal, Dunne, Eoghan, Hynes, Niamh, O'Halloran, Martin, and Elahi, Adnan

Subjects
ABDOMINAL aortic aneurysms, ENDOVASCULAR aneurysm repair, ENDOVASCULAR surgery, ARTIFICIAL implants, ANEURYSMS, REPAIRING
Abstract

Over the past two decades, there has been extensive research into surveillance methods for the post-endovascular repair of abdominal aortic aneurysms, highlighting the importance of these technologies in supplementing or even replacing conventional image-screening modalities. This review aims to provide an overview of the current status of alternative surveillance solutions for endovascular aneurysm repair, while also identifying potential aneurysm features that could be used to develop novel monitoring technologies. It offers a comprehensive review of these recent clinical advances, comparing new and standard clinical practices. After introducing the clinical understanding of abdominal aortic aneurysms and exploring current treatment procedures, the paper discusses the current surveillance methods for endovascular repair, contrasting them with recent pressure-sensing technologies. The literature on three commercial pressure-sensing devices for post-endovascular repair surveillance is analyzed. Various pre-clinical and clinical studies assessing the safety and efficacy of these devices are reviewed, providing a comparative summary of their outcomes. The review of the results from pre-clinical and clinical studies suggests a consistent trend of decreased blood pressure in the excluded aneurysm sac post-repair. However, despite successful pressure readings from the aneurysm sac, no strong link has been established to translate these measurements into the presence or absence of endoleaks. Furthermore, the results do not allow for a conclusive determination of ongoing aneurysm sac growth. Consequently, a strong clinical need persists for monitoring endoleaks and aneurysm growth following endovascular repair. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Feasibility Analysis of ECG-Based pH Estimation for Asphyxia Detection in Neonates.

Author

Hussain, Nadia Muhammad, Amin, Bilal, McDermott, Barry James, Dunne, Eoghan, O'Halloran, Martin, and Elahi, Adnan

Subjects
NEWBORN infants, ASPHYXIA, PULSE oximeters, ASPHYXIA neonatorum, PRINCIPAL components analysis, KRUSKAL-Wallis Test, BLOOD testing, AMPLITUDE estimation
Abstract

Birth asphyxia is a potential cause of death that is also associated with acute and chronic morbidities. The traditional and immediate approach for monitoring birth asphyxia (i.e., arterial blood gas analysis) is highly invasive and intermittent. Additionally, alternative noninvasive approaches such as pulse oximeters can be problematic, due to the possibility of false and erroneous measurements. Therefore, further research is needed to explore alternative noninvasive and accurate monitoring methods for asphyxiated neonates. This study aims to investigate the prominent ECG features based on pH estimation that could potentially be used to explore the noninvasive, accurate, and continuous monitoring of asphyxiated neonates. The dataset used contained 274 segments of ECG and pH values recorded simultaneously. After preprocessing the data, principal component analysis and the Pan–Tompkins algorithm were used for each segment to determine the most significant ECG cycle and to compute the ECG features. Descriptive statistics were performed to describe the main properties of the processed dataset. A Kruskal–Wallis nonparametric test was then used to analyze differences between the asphyxiated and non-asphyxiated groups. Finally, a Dunn–Šidák post hoc test was used for individual comparison among the mean ranks of all groups. The findings of this study showed that ECG features (T/QRS, T Amplitude, Tslope, Tslope/T, Tslope/|T|, HR, QT, and QTc) based on pH estimation differed significantly (p < 0.05) in asphyxiated neonates. All these key ECG features were also found to be significantly different between the two groups. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Design and Characterisation of a Read-Out System for Wireless Monitoring of a Novel Implantable Sensor for Abdominal Aortic Aneurysm Monitoring.

Author

Silva, Nuno P., Elahi, Adnan, Dunne, Eoghan, O'Halloran, Martin, and Amin, Bilal

Subjects
ABDOMINAL aortic aneurysms, ENDOVASCULAR aneurysm repair, ENDOVASCULAR surgery, ARTIFICIAL implants, WIRELESS sensor networks, TRANSMITTERS (Communication), DETECTORS, DISEASE progression
Abstract

Abdominal aortic aneurysm (AAA) is a dilation of the aorta artery larger than its normal diameter (>3 cm). Endovascular aneurysm repair (EVAR) is a minimally invasive treatment option that involves the placement of a graft in the aneurysmal portion of the aorta artery. This treatment requires multiple follow-ups with medical imaging, which is expensive, time-consuming, and resource-demanding for healthcare systems. An alternative solution is the use of wireless implantable sensors (WIMSs) to monitor the growth of the aneurysm. A WIMS capable of monitoring aneurysm size longitudinally could serve as an alternative monitoring approach for post-EVAR patients. This study has developed and characterised a three-coil inductive read-out system to detect variations in the resonance frequency of the novel Z-shaped WIMS implanted within the AAA sac. Specifically, the spacing between the transmitter and the repeater inductors was optimised to maximise the detection of the sensor by the transmitter inductor. Moreover, an experimental evaluation was also performed for different orientations of the transmitter coil with reference to the WIMS. Finally, the FDA-approved material nitinol was used to develop the WIMS, the transmitter, and repeater inductors as a proof of concept for further studies. The findings of the characterisation from the air medium suggest that the read-out system can detect the WIMS up to 5 cm, regardless of the orientation of the Z-shape WIMS, with the detection range increasing as the orientation approaches 0°. This study provides sufficient evidence that the proposed WIMS and the read-out system can be used for AAA expansion over time. [ABSTRACT FROM AUTHOR]

Published
2024
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15. Eliminating the geometric error in tissue conductivity measurements by increasing the sample size

Author

Ištuk, Niko, DeCaro, Cristoforo, Benchakroun, Hamza, Dunne, Eoghan, Elahi, Adnan, and O'Halloran, Martin

Subjects
tissue conductivity, comsol, electroporation
Abstract

In this study we simulated the tissue conductivity measurement with two-electrode probe in contact with the samples of different size. We found that after the sample reaches about twice the size of the probe, further increase in the size of the sample does not affect the resulting conductance. For smaller sizes of the sample we found that conductance decreases significantly. This decrease in conductance causes the underestimation of the conductivity of the sample. This error is caused by the limited geometry of the sample hence the geometric error. The geometric error can be avoided by ensuring the size of the sample is sufficiently larger than the probe, either by having large enough samples or by decreasing the size the probe.

Published
2022
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16. Pressure Dependency of Conductivity Measurements: The Specific Case of the Lung

Author

Ištuk, Niko, Benchakroun, Hamza, Dunne, Eoghan, and O'Halloran, Martin

Subjects
respiratory system, respiratory tract diseases
Abstract

In this paper, we examined how the applied pressure influences the measurement of conductivity in lung tissue. We found that the conductivity of lung increases with increased pressure. The increase in conductivity can be explained by the air being pushed out of the lung under the applied pressure. &nbsp

Published
2021
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17. Machine learning applied to electrical impedance tomography for the improved management of nocturnal enuresis

Author

Dunne, Eoghan, O'Halloran, Martin, Porter, Emily, H2020 European Research Council, and Higher Education Authority

Subjects
Machine Learning, Engineering, Bladder Monitoring, Science and Engineering, Electrical and Electronic Engineering, Electrical Impedance Tomography, Nocturnal Enuresis
Abstract

Nocturnal Enuresis (or bedwetting) is a condition that commonly affects children but can continue into adulthood. The condition can have serious implications on the quality of a child’s life. The current first-line medical treatment options are limited and none alert the user before voiding occurs. Electrical Impedance Tomography (EIT) is a low-cost, wearable and safe imaging modality that is investigated in this thesis as a potential proactive bedwetting alarm technology for nocturnal enuresis. In the literature, methods to estimate the bladder volume have been established in EIT. However, no research has been performed to determine when to alert the user in time to void their bladder. Consequently, this thesis investigates classification of bladder fullness in terms of not-full and full states using machine learning. After reviewing the literature, numerical forward models were critically investigated. Using anatomical information, a novel three-dimensional EIT pelvic numerical forward model was devised. Bladder state classification using machine learning was employed with both EIT measurement data and images. The classifiers were tested against varying noise sources previously established in the literature. A novel child pelvic numerical forward model (with realistic anatomical and electrical conductivity information) was then designed and implemented for bladder monitoring studies. Bladder state classification was then tested on child EIT data for the first time. Feature processing and classifier optimisation were also examined to improve classification performance and memory storage. Finally, the classifiers were investigated against the electrode misplacement noise source using EIT measurement data from the numerical child model. The overall findings indicated that high performance can be achieved with EIT bladder state classification using machine learning in the presence of established sources of error. Electrical impedance tomography measurement data was found to be the best input to the classifiers. Feature processing and classifier optimisation were found to improve the performance of an EIT-based bedwetting alarm solution. Electrode misplacement was found to greatly reduce classification performance. However, it has been shown that this error can be reduced by including data from varying electrode misplacements in the training dataset. Overall, this thesis has made significant strides towards the design and development of a non-invasive, proactive bladder monitor for children with nocturnal enuresis based on EIT.

Published
2021

19. EIT image-based bladder state classification for nocturnal enuresis

Author

Dunne, Eoghan, Santorelli, Adam, McGinley, Brian, O'Halloran, Martin, Leader, Geraldine, Porter, Emily, Boyle, Alistair, Polydorides, Nick, Jia, Jiabin, Horizon 2020, European Research Council, RESPECT, and FP7 People: Marie-Curie Actions

Subjects
Image Classification, Urinary Bladder, Bladder Fullness, Electrical Impedance Tomography, Nocturnal Enuresis
Abstract

In this paper, we propose the use of electrical impedance tomography (EIT) to support children with nocturnal enuresis. We perform the first image-based threshold classification for determining the bladder state of not full or full . The results demonstrate the strong promise for EIT as an aid for nocturnal enuresis. This research was supported by the European Research Council under the European Union’s Horizon 2020 Programme/ ERC Grant Agreement BioElecPro n. 637780 and the charity RESPECT and the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA Grant Agreement no. PCOFUND-GA-2013-608728. peer-reviewed

Published
2018

21. Gamma Band Light Stimulation in Human Case Studies: Groundwork for Potential Alzheimer’s Disease Treatment

Author

Jones, Marggie, primary, McDermott, Barry, additional, Oliveira, Bárbara Luz, additional, O’Brien, Aoife, additional, Coogan, Declan, additional, Lang, Mark, additional, Moriarty, Niamh, additional, Dowd, Eilis, additional, Quinlan, Leo, additional, Mc Ginley, Brian, additional, Dunne, Eoghan, additional, Newell, David, additional, Porter, Emily, additional, Elahi, Muhammad Adnan, additional, O’ Halloran, Martin, additional, and Shahzad, Atif, additional

Published
2019
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23. Realistic 3D finite element mesh of the adult human pelvis for electrical impedance tomography

Author

Dunne, Eoghan, Porter, Emily, McGinley, Brian, O'Halloran, Martin, Boyle, A, Halter, R, Murphy, E, Adler, A, European Research Council, Horizon 2020, FP7 People: Marie-Curie Actions, and RESPECT

Subjects
Realistic 3D, Adult human pelvis, Electrical impedance tomography, Finite element mesh
Abstract

The finite element method is a numerical technique used to solve forward models for electrical impedance tomography. To date, realistic finite element models (FEMs) have been created for the head and thorax, but pelvic models for bladder imaging are lacking. This paper presents a high quality, first-order, tetrahedral FEM of 25004 nodes and 129157 elements for the adult male pelvis, with mean and standard deviation stretch values of 0.752 and 0.082, respectively. This research was supported by funding from the European Research Council under the European Union’s Horizon 2020 Programme/ ERC Grant Agreement BioElecPro n. 637780 and the charity RESPECT and the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA Grant Agreement no. PCOFUND-GA-2013-608728. peer-reviewed

Published
2017

29. Brain Haemorrhage Detection Through SVM Classification of Electrical Impedance Tomography Measurements

Author

McDermott B, Dunne E, O’Halloran M, Porter E, Santorelli A, Makarov S, Horner M, and Noetscher G

Abstract

A brain haemorrhage constitutes a serious medical scenario with a need for rapid, accurate detection to facilitate treatment initiation. Machine learning (ML) techniques applied to such medical diagnostic problems can improve the rate and accuracy of bleed detection leading to improved patient outcomes. In this chapter we examine the potential role of support vector machine (SVM) type classifiers in detecting such haemorrhagic lesions (bleeds) using electrical impedance tomography (EIT) measurement frames as the source of training and test data. A two-layer computational model of the head is designed, with EIT frame generation simulated from electrodes placed on the surface of the head model. A wide variety of test scenarios are modelled, including variations in measurement noise, bleed size and location, electrode position, and anatomy. Initial results using a linear SVM classifier applied to test scenarios, with and without pre-processing of the EIT measurement frame, are summarised. The classifier returned detection accuracies >90% with signal-to-noise ratios of ≥60 dB; was independent of bleed location, capable of detecting bleeds as small as 10 ml; and was unaffected by slight variances of ±2 mm in electrode position. However, the performance was degraded with anatomical variations. Options for improvement of performance, including selection of a different kernel and pre-processing of the frames prior to implementing the classifier, are then examined. This analysis demonstrated that using the radial basis function as the kernel for the SVM classifier and principal component analysis (PCA) to select specific features leads to the most accurate and robust performance. The analysis and results indicate that the coupling of EIT with ML has potential for improvement in the detection of bleeds such as brain haemorrhages., (Copyright 2019, The Author(s).)

Published
2019
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