Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Intelligent classification models for food products basis on morphological, colour and texture features

The aim of this paper is to build a supervised intelligent classification model of food products such as Biscuits, Cereals, Vegetables, Edible nuts and etc., using digital images. The Correlation-based Feature Selection (CFS) algorithm and 2nd derivative pre-treatments of the Morphological, Colour and Texture features are used to train the models for classification and detection. The best prediction accuracy is obtained for the Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), Simple Logistic (SLOG) and Sequential Minimal Optimization (SMO) classifiers (more than 80% of the success rate for the training/test set and 80% for the validation set). The percentage of correctly classified instances is very high in these models and ranged from 80% to 96% for the training/test set and up to 95% for the validation set

Tracing the evolution of service robotics : Insights from a topic modeling approach

Acord transformatiu CRUE-CSICAltres ajuts: Helmholtz Association (HIRG-0069)Altres ajuts: Russian Science Foundation (RSF grant number 19-18-00262)Taking robotic patents between 1977 and 2017 and building upon the topic modeling technique, we extract their latent topics, analyze how important these topics are over time, and how they are related to each other looking at how often they are recombined in the same patents. This allows us to differentiate between more and less important technological trends in robotics based on their stage of diffusion and position in the space of knowledge represented by a topic graph, where some topics appear isolated while others are highly interconnected. Furthermore, utilizing external reference texts that characterize service robots from a technical perspective, we propose and apply a novel approach to match the constructed topics to service robotics. The matching procedure is based on frequency and exclusivity of words overlapping between the patents and the reference texts. We identify around 20 topics belonging to service robotics. Our results corroborate earlier findings, but also provide novel insights on the content and stage of development of application areas in service robotics. With this study we contribute to a better understanding of the highly dynamic field of robotics as well as to new practices of utilizing the topic modeling approach, matching the resulting topics to external classifications and applying to them metrics from graph theory

Tracing the evolution of service robotics : Insights from a topic modeling approach

Altres ajuts: Acord transformatiu CRUE-CSICAltres ajuts: Helmholtz Association (HIRG-0069)Altres ajuts: Russian Science Foundation (RSF grant number 19-18-00262)Taking robotic patents between 1977 and 2017 and building upon the topic modeling technique, we extract their latent topics, analyze how important these topics are over time, and how they are related to each other looking at how often they are recombined in the same patents. This allows us to differentiate between more and less important technological trends in robotics based on their stage of diffusion and position in the space of knowledge represented by a topic graph, where some topics appear isolated while others are highly interconnected. Furthermore, utilizing external reference texts that characterize service robots from a technical perspective, we propose and apply a novel approach to match the constructed topics to service robotics. The matching procedure is based on frequency and exclusivity of words overlapping between the patents and the reference texts. We identify around 20 topics belonging to service robotics. Our results corroborate earlier findings, but also provide novel insights on the content and stage of development of application areas in service robotics. With this study we contribute to a better understanding of the highly dynamic field of robotics as well as to new practices of utilizing the topic modeling approach, matching the resulting topics to external classifications and applying to them metrics from graph theory

New approach in multipurpose optical diagnostics : fluorescence based assay for simultaneous determination of physicochemical parameters

The development of sensors assays for comprehensive characterisation of biological samples and effective minimal-invasive diagnostics is highly prioritised. Last decade this research area has been actively developing due to possibility of simultaneous, real- time, in vivo detection and monitoring of diverse physicochemical parameters and analytes. The new approach which has been introduced in this thesis was to develop and examine an optical diagnostic assay consisting of a mixture of environmental-sensitive fluorescent dyes. The operating principle of the system has been inspired by electronic nose and tongue devices which combine nonspecific (or semispecific) sensing elements and chemometric techniques for multivariate data analysis. The performance of the optical assay was based on the analysis of the spectrum of selected dyes with discreet reading of their emission maxima. The variations in peaks intensities caused by environmental changes provided distinctive fluorescence patterns, which could be handled similar to the signals collected from nose/tongue devices. The analytical capability of the assay was engendered by changes in fluorescence signal of the dye mixture in response to changes in pH, temperature, ionic strength and the presence of oxygen. Further findings have also proved the ability of optical assay to estimate development phases and to discriminate between different strains of growing cell cultures as well as identify various gastrointestinal diseases in human. This novel fluorescence-based diagnostic tool offers a promising alternative to electrochemical systems providing high sensitive measurements with broad dynamic range, easy, inexpensive measurements and the possibility of remote sensing and extreme assay miniaturisation. Additionally it does not require reference signal. This new approach can impact on a number of applications such as routine minimal- invasive diagnostics for medical samples, biomedical analysis, pharmaceutical or cosmetic research, quality control and process monitoring of food or environmental samples.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Mimicking the human olfactory system: a portable e-­mucosa

The study of electronic noses has been an active area of research for over 25 years. Commercial instruments have been successfully deployed within niche application areas, for example, the food, beverage and pharmaceutical industries. However, these instruments are still inferior to their human counterparts and have not achieved mainstream success. Humans can distinguish and identify many thousands of different aromas, even at very low concentration levels, with relative ease. The human olfactory system is extremely sophisticated, which allows it to out-­perform artificial instruments. Though limited, artificial instruments can provide a lower cost option to specific problems and can be an alternative to the use of organoleptic panels. Most existing commercial electronic nose (e-­nose) instruments are expensive, bulky, desktop units, requiring a PC to operate. In addition, these instruments usually require a trained operator to gather and analyse the data. Motivated to improve the performance, size and cost of e-­nose instruments, this research aims to extract biological principles from the mammalian olfactory system to aid the implementation of a portable e-­nose instrument. This study has focused on several features of the biological system that may provide the key to its superior performance. Specifically, the large number of different olfactory receptors and the diversity of these receptors; the nasal chromatograph effect; stereo olfaction; sniff rate and odour conditioning. Based on these features, a novel, portable, cost effective instrument, called the Portable e-­Mucosa (PeM), has been designed, implemented and tested. The main components of the PeM are three sensor arrays each containing 200 carbon black composite chemoresistive sensors (totalling 600 sensors with 24 different tunings) mimicking the large number of olfactory receptors and two gas chromatographic columns (coated with non-­polar and polar compounds to maximise the discrimination) emulating the “nasal chromatograph” effect of the human mucus. A preconcentrator based on thermal desorption is also included as an odour collection system to further improve the instrument. The PeM provides USB and Multimedia Memory Card support for easy communication with a PC. The instrument weighs 700g and, with dimensions of 110 x 210 x 110 mm, is slightly larger than the commercial Cyranose 320 (produced by Smiths Detection). This novel instrument generates ‘spatio-­temporal’ data and when coupled with an appropriate pattern recognition algorithm, has shown an enhanced ability to discriminate between odours. The instrument successfully discriminates between simple odours (ethanol, ethyl acetate and acetone) and more complex odours (lavender, ylang ylang, cinnamon and lemon grass essential oils). This system can perhaps be seen as a foundation for a new generation of e-noses