Sleep Spindle Detection by Using Merge Neural Gas

In this paper the Merge Neural Gas (MNG) model is applied to detect sleep spindles in EEG. Features are extracted from windows of the EEG by using short time Fourier transform. The total power spectrum is computed in six frequency bands and used as input to the MNG network. The results show that MNG outperforms simple neural gas in correctly detecting sleep spindles. In addition the temporal quantization results as well as sleep trajectories are visualized on two-dimensional maps by using the OVING projection method

Sleep Spindle Detection by Using Merge Neural Gas

In this paper the Merge Neural Gas (MNG) model is applied to detect sleep spindles in EEG. Features are extracted from windows of the EEG by using short time Fourier transform. The total power spectrum is computed in six frequency bands and used as input to the MNG network. The results show that MNG outperforms simple neural gas in correctly detecting sleep spindles. In addition the temporal quantization results as well as sleep trajectories are visualized on two-dimensional maps by using the OVING projection method

Aporte de un sistema predictivo de contraloría médica en la gestión de licencias médicas electrónicas

Introduction: The delay in the processing of sick leaves (SLs) is a public health problema in Chile, considering that this affects the payment of the subsidy to the individuals destined to perform the prescribed medical rest while unable to work. The aim of this study was to explore the differences in the processing time of electronic SLs (ESLs) evaluated by medical audit (MA) and the SLs evaluated by a predictive medical audit system (PMAS) based on artificial neural networks. Materials and methods: The processing time of the ESLs that were processed by PMAS was compared with the processing time of those that were examined only by MA, using Kaplan Meier curves, log-rank test, and multivariate Cox models. Results: The processing rate for PMAS was 1.7-fold to 5.5-fold faster than MA, after adjusting for potential confounding variables. Discussion: The implementation of the PMAS reduced the processing time of ESLs, which benefits the workers affiliated to the public insurance system in Chile.Introducción: El retraso del procesamiento de las licencias médicas (LMs) representa un problema de salud pública en Chile, considerando que esto afecta el pago del subsidio a las personas destinado a realizar el reposo médico prescrito mientras no se pueda trabajar. El objetivo de este estudio fue explorar las diferencias en el tiempo de procesamiento de las licencias médicas electrónicas (LMEs) evaluadas por contraloría médica (CM) y las evaluadas por un sistema predictivo de contraloría médica (SPCM) basado en redes neuronales artificiales. Materiales y métodos: El tiempo de procesamiento de LMEs procesadas con SPCM fue comparado con el tiempo de procesamiento de LMEs examinadas solo con CM, usando curvas de Kaplan Meier, prueba de log-rank y modelos multivariados de Cox. Resultados: La tasa de procesamiento del SPCM fue entre 1,7 a 5,5 veces más rápida que la tasa de procesamiento de la CM, ajustando por potenciales confusores. Discusión: La implementación del SPCM permitió disminuir el tiempo de procesamiento de las LMEs, beneficiando a los trabajadores afiliados al seguro público

AMPERE: exploiting Galileo for electrical asset mapping in emerging countries

The AMPERE project aims at engineering and starting to commercialize a dedicated solution, to be used for electrical power network information gathering. AMPERE will support decision making actors (e.g., institutions and public/ private companies in charge to manage electrical network) to collect all needed info to plan electrical network maintenance and upgrade. In particular, the need for such a solution comes in emerging countries (worldwide) where, despite global electrification rates are significantly progressing, the access to electricity is still far from being achieved in a reliable way, and, there is lack of knowledge about already deployed infrastructure. The AMPERE data collection solution relies on drones equipped with GNSS/INS units, LIDAR, optical and thermal cameras. In such a context, Galileo is a key enabler - especially, considering its free-of-charge High Accuracy Service (HAS) and its highly precise E5 AltBOC code measurements- as a core component to map electric utilities, optimize decision making process about the network development and therefore increase time and cost efficiency, offering more convenient way to manage energy distribution. The project is also aimed at investigating the potential of cooperative multi-UAV solutions