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Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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https://read.qxmd.com/read/30441746/embc-2018-keynote-plenary-speakers
#1
(no author information available yet)
Provides an abstract for each of the keynote presentations and may include a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441745/a-machine-learning-approach-to-reconstruction-of-heart-surface-potentials-from-body-surface-potentials
#2
Avinash Malik, Tommy Peng, Mark L Trew
Invasive cardiac catheterisation is a precursor to ablation therapy for ventricular tachycardia. Invasive cardiac diagnostics are fraught with risks. Decades of research has been conducted on the inverse problem of electrocardiography, which can be used to reconstruct Heart Surface Potentials (HSPs) from Body Surface Potentials (BSPs), for non-invasive cardiac diagnostics. State of the art solutions to the inverse problem are unsatisfactory, since the inverse problem is known to be ill-posed. In this paper we propose a novel approach to reconstructing HSPs from BSPs using a Time-Delay Artificial Neural Network (TDANN)...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441744/integrated-signal-encoding-and-analysis-system-for-actigraphy-based-long-term-monitoring-of-periodic-limb-movements-in-sleep
#3
Yashodhan Athavale, Sridhar Krishnan, Afsaneh Raissi, Nardin Kirolos, Brian J Murray, Mark I Boulos
There has been a boom in the development of wearable devices for wellness and healthcare applications. Numerous studies have been conducted on the utility of employing wearable devices for the long-term monitoring of biosignals. Despite their efficacy, the potential for practical implementation faces many hurdles such as memory usage, power consumption, denoising, and efficient data transmission. Of the many wearables being used, the actigraph has been a popular choice amongst experts for identifying motion abnormalities such as periodic leg movements (PLMs) in sleep and the activities of patients suffering from various medical illnesses...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441743/differences-between-morphological-and-electrophysiological-retinal-ganglion-cell-classes
#4
Syeda Zehra, G Damien Hicks, Alex Adjinicolaou, R Michael Ibbotson, Tatiana Kameneva
Retinal prostheses work by delivering electrical pulses to the surviving retinal neurons. A pattern of electrical stimulation can generate a perception of vision in blind patients. To improve efficacy of retinal implants, it is important to understand how different classes of retinal neurons respond to electrical stimulation and if a classification can be made based on the electrophysiological properties of neurons. We use previously recorded patch clamp data from retinal ganglion cells classified into morphological classes (A,B,C, D) and functional types (ON, OFF, ON-OFF)...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441742/improving-the-diagnostic-ability-of-oximetry-recordings-in-pediatric-sleep-apnea-hypopnea-syndrome-by-means-of-multi-class-adaboost
#5
Fernando Vaquerizo-Villar, Daniel Alvarez, Leila Kheirandish-Gozal, Gonzalo C Gutierrez-Tobal, Veronica Barroso-Garcia, Andrea Crespo, Felix Del Campo, David Gozal, Roberto Hornero
Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent respiratory disorder that may impose many negative effects on the health and development of children. Due to the drawbacks of overnight polysomnography (PSG), the gold standard diagnosis technique, automated analysis of nocturnal oximetry has emerged as a simplified alternative. In order to improve diagnosis ability of oximetry, we propose to evaluate the usefulness of AdaBoost, a classification boosting algorithm, in the context of pediatric SAHS...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441741/mathematical-modeling-of-bacteria-enabled-drug-delivery-system-penetration-into-multicellular-tumor-spheroids
#6
S Suh, E J Leaman, Ying Zhan, B Behkam
Bacteria-based cancer treatment is a promising approach to address the need for targeted tumor therapies in an effort to avoid the systemic toxicity inherent in conventional chemotherapy. A number of bacterial strains have been shown to preferentially colonize tumors and impart therapeutic benefits. However, the physical underpinnings of bacteria intratumoral transport remain poorly studied. It is hypothesized that cell Iysis in hypoxic and necrotic regions of tumors creates a niche in which some bacteria thrive...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441740/molecular-dynamics-simulation-study-of-electrokinetics-transport-of-aqueous-solution-through-functionalized-carbon-nanotubes-a-new-membrane-model-for-nanopours-based-biosensors
#7
Abbas Panahi, Ebrahim Ghafar-Zadeh, Sebastian Magiierowski, Mohammad Hossein Sabour
DNA sequencing is an essential process for determining the nucleotides on a DNA strands and this process is of high importance in medicine and biomedical research. Nanopore based DNA sequencing has been widely researched and analyzed during last years and this is very important to improve all parameters involved in the process of DNA translocating through these nanopours. Ionic current resolution is a key parameter in designing these nanopours for better measuring of this infinitesimal current that is in order of nano A or even pico A...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441739/unveiling-the-impact-of-distinct-melanosome-arrangements-on-the-attenuation-of-cancer-inducing-ultraviolet-radiation
#8
Gladimir V G Baranoski, Tenn F Chen, Spencer R Van Leeuwen
The exposure of human skin to ultraviolet radiation (UVR) can trigger a wide array of biological responses, including photocarcinogenesis. Melanin, either in colloidal form or encapsulated into melanosomes, is known to be the main UVR attenuation substance acting within the cutaneous tissues. Although many studies have addressed the protective role of this pigment against the harmful effects of UVR exposure, the impact of different melanosome arrangements on the mitigation of these effects remains to be quantitatively verified...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441738/brachytherapy-organ-dose-estimation-using-monte-carlo-simulations-of-realistic-patient-models
#9
S Morato, B Juste, S Peris, R Miro, G Verdu, F Ballester, J Vijande
Radiation Therapy Planning Systems (RTPS) currently used in hospitals contain algorithms based on deterministic simplifications that do not properly consider electrons lateral transport in the areas where there are changes of density, and as a result, erroneous dose predictions could be produced. According to this, the present work proposes the use of Monte Carlo method in brachytherapy planning systems, which could affect positively on the radiotherapy treatment planning, since it provides results that are more accurate and takes into account the in homogeneities density variations...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441737/influence-of-the-cochlear-implant-electrode-array-placement-on-the-current-spread-in-the-cochlea
#10
Friederike Schafer, Jorg Enke, Frank Bohnke, Werner Hemmert, Siwei Bai
Three-dimensional (3D) computational models of the inner ear have been utilised to assist in investigating the factors that influence cochlear implant (CI) outcomes. A volume conductor cochlear model with an implanted electrode array was reconstructed from X-ray microtomography $(\mu$ CT) scans of a cadaveric human temporal bone. To mimic an in-vivo setting, the cochlea was embedded in a head model. The finite element (FE) method was used to analyse the electrical potential $\varphi$ in the cochlear nerve as a result of CI stimulation...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441736/simulating-the-outcome-of-heart-allocation-policies-using-deep-neural-networks
#11
Dennis Medved, Pierre Nugues, Johan Nilsson
We created a system to simulate the heart allocation process in a transplant queue, using a discrete event model and a neural network algorithm, which we named the Lund Deep Learning Transplant Algorithm (LuDeLTA). LuDeLTA is utilized to predict the survival of the patients both in the queue and after transplant. We tried four different allocation policies: wait time, clinical rules and allocating the patients using either LuDeLTA or The International Heart Transplant Survival Algorithm (IHTSA) model. Both IHTSA and LuDeLTA were used to evaluate the results...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441735/decoding-position-to-analyze-spatial-information-encoding-in-a-large-scale-neuronal-network-model-of-rat-dentate-gyrus
#12
Gene J Yu, Jean-Marie C Bouteiller, Dong Song, Theodore W Berger
Spatial information is encoded by the hippocampus, and the factors that contribute to the amount of information that can be encoded and the transformation of spatial information through the trisynaptic circuit remain an important issue. A large-scale neuronal network model of the rat entorhinal-dentate system was developed with multicompartmental representations of the neurons within the dentate gyrus. Spatial information was introduced to the network via grid cell activity, and the spatial information encoding capabilities of the network were assessed using a recursive decoding algorithm to estimate the position of a virtual rat using the dentate activity...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441734/tractography-analysis-for-electroconvulsive-therapy
#13
Stefanie Riel, Mohammad Bashiri, Werner Hemmert, Siwei Bai
Computational human head models have been used in electrophysiological studies, and they have been able to provide useful information that is unable or difficult to acquire from experimental or imaging studies. However, most of these models are purely volume conductor models that overlooked the electric excitability of axons in the white matter of the brain. This study combined a finite element (FE) model of electroconvulsive therapy (ECT) with a whole-brain tractography analysis as well as the cable theory of neuronal excitation...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441733/modeling-nonlinear-synaptic-dynamics-a-laguerre-volterra-network-framework-for-improved-computational-efficiency-in-large-scale-simulations
#14
Eric Y Hu, Gene Yu, Dong Song, C Jean-Marie Bouteiller, W Theodore Berger
Synapses are key components in signal transmission in the brain, often exhibiting complex non-linear dynamics. Yet, they are often crudely modelled as linear exponential equations in large-scale neuron network simulations. Mechanistic models that use detailed channel receptor kinetics more closely replicate the nonlinear dynamics observed at synapses, but use of such models are generally restricted to small scale simulations due to their computational complexity. Previously, we have developed an ``input-output'' (IO) synapse model using the Volterra functional series to estimate nonlinear synaptic dynamics...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441732/clustering-based-kernel-reinforcement-learning-for-neural-adaptation-in-brain-machine-interfaces
#15
Xiang Zhang, Jose C Principe, Yiwen Wang
Reinforcement learning (RL) interprets subject's movement intention in Brain Machine Interfaces (BMIs) through trial-and-error with the advantage that it does not need the real limb movements. When the subjects try to control the external devices purely using brain signals without actual movements (brain control), they adjust the neural firing patterns to adapt to device control, which expands the state-action space for the RL decoder to explore. The challenge is to quickly explore the new knowledge in the sizeable state-action space and maintain good performance...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441731/a-cerebellar-spiking-neural-model-for-phase-reversal-of-vestibulo-ocular-reflex
#16
Zhanhong Zhou, Xiaolong Zhai, Chung Tin
Cerebellum possesses very rich motor control and learning capability which is critical for animals. In this study, we proposed a spiking neural network model of cerebellum for gain and phase adaptation in vestibulo-ocular reflex (VOR). VOR is a critical adaptive reflexive eye movement for maintaining a stable visual field. In this model (with neuron number at the order of 104), synaptic plasticity at parallel fiber-Purkinje cell synapses was considered. In particular, we have shown that the inhibitory inputs from molecular layer interneurons on Purkinje cells play a critical role in phase adaptation of VOR...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441730/a-multi-domain-continuum-model-of-electrical-stimulation-of-healthy-and-degenerate-retina
#17
Abdulrahman Alqahtani, Amr Al Abed, Emily E Anderson, Nigel H Lovell, Socrates Dokos
A continuum multi-domain model of electrical stimulation of the retina is presented and validated against retinal ganglion cell (RGC) excitation thresholds reported in a recently published in vitro experimental study. We applied our model to investigate the response of the RGC layer to electrical stimulation during mid-to-late stage retinal degeneration for both epiretinal and suprachoroidal configurations. Interestingly, our model predicted that suprachoroidal stimulation of the degenerate retina required increased current thresholds, mainly because of the presence of the glial scar layer...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441729/deploying-predictive-models-in-a-healthcare-environment-an-open-source-approach
#18
Dennis H Murphree, Daniel J Quest, Ryan M Allen, Che Ngufor, Curtis B Storlie
Despite dramatic progress in the application of predictive modeling and data mining techniques to problems in modern medicine, a major challenge facing technical practitioners is that of delivering models to clinicians. We have developed an easily implementable framework for publishing predictive models written in R or Python in a way that allows them to be consumed by practically any downstream clinical application, as well as allowing them to be reused in a wide variety of environments without modification...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441728/a-machine-learning-approach-for-the-prediction-of-the-progression-of-cardiovascular-disease-based-on-clinical-and-non-invasive-imaging-data
#19
Vassiliki I Kigka, Eleni I Georga, Antonis I Sakellarios, Nikolaos S Tachos, Ioannis Andrikos, Panagiota Tsompou, Silvia Rocchiccioli, Gualtiero Pelosi, Oberdan Parodi, Lampros K Michalis, Dimitrios I Fotiadis
Nowadays, cardiovascular diseases are very common and are considered as the main cause of morbidity and mortality worldwide. Coronary Artery Disease (CAD), the most typical cardiovascular disease is diagnosed by a variety of medical imaging modalities, which involve costs and complications. Therefore, several attempts have been undertaken to early diagnose and predict CAD status and progression through machine learning approaches. The purpose of this study is to present a machine learning technique for the prediction of CAD, using image-based data and clinical data...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30441727/key-marker-selection-for-the-detection-of-early-parkinson-s-disease-using-importance-driven-models
#20
Chun Xiao, Yingchao Liu, David Dagan Feng, Xiuying Wang
The detection of early Parkinson' s disease (PD) is crucial for PD management. Most of previous efforts on PD diagnosis focus more on improving PD detection accuracies by trying using features from more modalities, which results in a common question: is it true that the more features available, the better the performance of the diagnosis system? This paper proposes an importance-driven approach for the detection of PD. The importance of features based on gradient boosting is firstly learned. The ranked features based on feature importance are input to a progressive learning pipeline to find key features of PD...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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