A versatile wearable based on reconfigurable hardware for biomedical measurements

In this work a versatile hardware platform based on reconfigurable devices is presented. This platform it intended for the acquisition of multiple biosignals, only requiring a reconfiguration to switch applications. This prototype has been combined with graphene-based, flexible electrodes to cover the application to different biosignals presented in this paper, which are electrocardiogram, electrooculogram and electromyogram. The features of this system provide to the user and to medical personnel a complete set of diagnosis tools, available both at home and hospitals, to be used as a triage tool and for remote patient monitoring. Additionally, an Android application has been developed for signal processing and data presentation to the user. The results obtained demonstrate the wide range of possibilities in portable/wearable applications of the combination of reconfigurable devices and flexible electronics, especially for the remote monitoring of patients using multiple biosignals of interest. The versatility of this device makes it a complete set of monitoring tools integrated in a reduced size device

Heartbeat Classification in Wearables Using Multi-layer Perceptron and Time-Frequency Joint Distribution of ECG

Heartbeat classification using electrocardiogram (ECG) data is a vital assistive technology for wearable health solutions. We propose heartbeat feature classification based on a novel sparse representation using time-frequency joint distribution of ECG. Fundamental to this is a multi-layer perceptron, which incorporates these signatures to detect cardiac arrhythmia. This approach is validated with ECG data from MIT-BIH arrhythmia database. Results show that our approach has an average 95.7% accuracy, an improvement of 22% over state-of-the-art approaches. Additionally, ECG sparse distributed representations generates only 3.7% false negatives, reduction of 89% with respect to existing ECG signal classification techniques.Comment: 6 pages, 7 figures, published in IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE

Sistema vestible para medida de biomarcadores

Este Trabajo de Fin de Grado tiene como objetivo el desarrollo de un dispositivo que ofrezca suficiente flexibilidad y reprogramabilidad como para ser utilizado en diversas aplicaciones enfocadas a la adquisición de bioseñales y biomarcadores, es decir, se pretende crear un único dispositivo que mediante modificaciones de software y las mínimas posibles de hardware, se pueda utilizar en la adquisición de distintos parámetros y señales presentes en el cuerpo humano. En concreto, se va a ejemplificar como dicho dispositivo se puede emplear en la adquisición de la señal cardíaca y en el calculo del porcentaje del nivel de saturación de oxígeno en sangre, haciendo cambios mínimos al apartado físico. Se pretende que según la aplicación, el dispositivo diseñado pueda ser utilizado en su función de dispositivo vestible. Para lograr estos objetivos, el proyecto está basado en la tecnología SoC (System on a chip), la cual permite aglutinar gran cantidad de componentes electrónicos en un mismo empaquetado de reducido tamaño. Con la intencionalidad de simplificar el uso para un usuario final, se desarrolla una aplicación Android, la cual es capaz de comunicarse con el dispositivo creado vıa Bluetooth Low Energy, mostrar los resultados obtenidos para la medida concreta que se esté realizando y almacenar estos para su posible consulta a futuro.This Bachelor’s Thesis aims to develop a device that offers enough flexibility and reprogrammability to be used in various applications focused on the acquisition of biosignals and biomarkers, that is, it is intended to create a single device that through software modifications and the minimum possible modifications of hardware can be used in the acquisition of different parameters and signals present in the human body. Specifically, it is going to be exemplified how this device can be used in the acquisition of the cardiac signal and in the calculation of the percentage of oxygen saturation level in blood, making minimal changes to the physical section. It is intended that depending on the application, the designed device can be used in its function as a wearable device. To achieve these goals, the project is based on SoC (System on a chip) technology, which allows to agglutinate a large number of electronic components in the same small package. With the intention of simplifying the use for an end user, an Android application is developed, which is able to communicate with the device created via Bluetooth Low Energy, display the results obtained for the specific measurement being performed and store them for possible future reference

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Smoking and Second Hand Smoking in Adolescents with Chronic Kidney Disease: A Report from the Chronic Kidney Disease in Children (CKiD) Cohort Study

The goal of this study was to determine the prevalence of smoking and second hand smoking [SHS] in adolescents with CKD and their relationship to baseline parameters at enrollment in the CKiD, observational cohort study of 600 children (aged 1-16 yrs) with Schwartz estimated GFR of 30-90 ml/min/1.73m2. 239 adolescents had self-report survey data on smoking and SHS exposure: 21 [9%] subjects had “ever” smoked a cigarette. Among them, 4 were current and 17 were former smokers. Hypertension was more prevalent in those that had “ever” smoked a cigarette (42%) compared to non-smokers (9%), p\u3c0.01. Among 218 non-smokers, 130 (59%) were male, 142 (65%) were Caucasian; 60 (28%) reported SHS exposure compared to 158 (72%) with no exposure. Non-smoker adolescents with SHS exposure were compared to those without SHS exposure. There was no racial, age, or gender differences between both groups. Baseline creatinine, diastolic hypertension, C reactive protein, lipid profile, GFR and hemoglobin were not statistically different. Significantly higher protein to creatinine ratio (0.90 vs. 0.53, p\u3c0.01) was observed in those exposed to SHS compared to those not exposed. Exposed adolescents were heavier than non-exposed adolescents (85th percentile vs. 55th percentile for BMI, p\u3c 0.01). Uncontrolled casual systolic hypertension was twice as prevalent among those exposed to SHS (16%) compared to those not exposed to SHS (7%), though the difference was not statistically significant (p= 0.07). Adjusted multivariate regression analysis [OR (95% CI)] showed that increased protein to creatinine ratio [1.34 (1.03, 1.75)] and higher BMI [1.14 (1.02, 1.29)] were independently associated with exposure to SHS among non-smoker adolescents. These results reveal that among adolescents with CKD, cigarette use is low and SHS is highly prevalent. The association of smoking with hypertension and SHS with increased proteinuria suggests a possible role of these factors in CKD progression and cardiovascular outcomes