Development and Evaluation of Automated Tools for Auditory-Brainstem and Middle-Auditory Evoked Potentials Waves Detection and Annotation

Auditory evoked potentials (AEPs) are brain-derived electrical signals, following an auditory stimulus, utilised to examine any obstructions along the brain neural-pathways and to diagnose hearing impairment. The clinical evaluation of AEPs is based on the measurements of the latencies and amplitudes of waves of interest; hence, their identification is a prerequisite for AEP analysis. This process has proven to be complex, as it requires relevant clinical experience, and the existing software for this purpose has little practical use. The aim of this study was the development of two automated annotation tools for ABR (auditory brainstem response)- and AMLR (auditory middle latency response)-tests. After the acquisition of 1046 raw waveforms, appropriate pre-processing and implementation of a four-stage development process were performed, to define the appropriate logical conditions and steps for each algorithm. The tools’ detection and annotation results, regarding the waves of interest, were then compared to the clinicians’ manual annotation, achieving match rates of at least 93.86%, 98.51%, and 91.51% respectively, for the three ABR-waves of interest, and 93.21%, 92.25%, 83.35%, and 79.27%, respectively, for the four AMLR-waves. The application of such tools in AEP analysis is expected to assist towards an easier interpretation of these signals

Box plots of selected texture features of experimental tissue (calf endometrium) for Angle 1 and Angle 2 views before and after gamma correction

<p><b>Copyright information:</b></p><p>Taken from "A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer"</p><p>http://www.biomedical-engineering-online.com/content/6/1/44</p><p>BioMedical Engineering OnLine 2007;6():44-44.</p><p>Published online 29 Nov 2007</p><p>PMCID:PMC2246140.</p><p></p> Plots (a) and (b) present SF variance and SGLDM contrast features before gamma correction respectively. Plots (c) and (d) present the same texture features after applying gamma correction. (The notched box shows the median, lower and upper quartiles and confidence interval around the median for each feature. The dotted lines connect the nearest observations within 1.5 of the inter-quartile range (IQR) of the lower and upper quartiles.

Histogram plots for R, G and B channels for calf endometrium for (a) angle 1 and (b) angle 2 views (after gamma correction)

<p><b>Copyright information:</b></p><p>Taken from "A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer"</p><p>http://www.biomedical-engineering-online.com/content/6/1/44</p><p>BioMedical Engineering OnLine 2007;6():44-44.</p><p>Published online 29 Nov 2007</p><p>PMCID:PMC2246140.</p><p></p

Texture feature value variability for the angle 1 and angle 2 views as a function of scale for SGLDM entropy and GLDS homogeneity

<p><b>Copyright information:</b></p><p>Taken from "A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer"</p><p>http://www.biomedical-engineering-online.com/content/6/1/44</p><p>BioMedical Engineering OnLine 2007;6():44-44.</p><p>Published online 29 Nov 2007</p><p>PMCID:PMC2246140.</p><p></p

Designing interoperable telehealth platforms: bridging IoT devices with cloud infrastructures

© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. A platform offering web technologies and interoperable components is proposed, allowing integration of different technologies into a robust system. Key modules are provided in home, to support integration of IoT devices, and in the cloud, offering centralised services and storage. Communication between the two is performed using the open FIWARE-Orion protocol. Data are not tied to methods and resources, so the platform can handle multiple types of requests and data formats. The platform is deployed in HOLOBALANCE, a tele-rehabilitation system for balance disorders, providing surrogate holographic physiotherapists, real time evaluations of task performance and cloud-based data analytics for personalised coaching

Multiparametric data analysis for diagnostic decision support in balance disorders

In this work we present a framework for the analysis and mining of multiparametric data related to balance disorders. The overall concept is to define the schema of the analysis that provides optimal results for diagnostic decision support in balance disorders. The work is part of the integrated EMBalance platform which targets the management of patients with balance disorders, from the diagnosis to treatment and evolution of the disease. The obtained results in four different balance disorders range from 76.4% to 92.1%