Alonso

Estimating complexity from EEG background activity of epileptic patients

We analyze the EEG background activities of five epileptic patients and compare them with the EEGs of five control subjects by means of methods from nonlinear dynamical system theory. We divided the EEG time-series data into frames to select those that fulfilled the stationarity condition, which is necessary in order to apply our methods. Then, we reconstructed the chaotic dynamic attractors expressed in the phase space and calculated their correlation dimensions, which are a measure of the complexity. Furthermore, we recorded an EEG during a petit mal epileptic seizure, whose attractor is a 3-D plot and the correlation dimension will also be presented.

Nonlinear analysis of time series generated by schizophrenic patients

Focusing on a particular feature, the ability to create random rhythms, our study is aimed at the analysis of cognitive performance in patients with schizophrenia, comparing them with normal subjects. We have developed a new cognitive test using methods from nonlinear dynamical systems theory, with the objective of measuring the subjects capacity of developing a random rhythm.

Man-Machine Interface System for Neuromuscular Training and Evaluation Based on EMG and MMG Signals

This paper presents the UVa-NTS (University of Valladolid Neuromuscular Training System), a multifunction and portable Neuromuscular Training System. The UVa-NTS is designed to analyze the voluntary control of severe neuromotor handicapped patients, their interactive response, and their adaptation to neuromuscular interface systems, such as neural prostheses or domotic applications. Thus, it is an excellent tool to evaluate the residual muscle capabilities in the handicapped. The UVa-NTS is composed of a custom signal conditioning front-end and a computer. The front-end electronics is described thoroughly as well as the overall features of the custom software implementation. The software system is composed of a set of graphical training tools and a processing core. The UVa-NTS works with two classes of neuromuscular signals: the classic myoelectric signals (MES) and, as a novelty, the myomechanic signals (MMS). In order to evaluate the performance of the processing core, a complete analysis has been done to classify its efficiency and to check that it fulfils with the real-time constraints. Tests were performed both with healthy and selected impaired subjects. The adaptation was achieved rapidly, applying a predefined protocol for the UVa-NTS set of training tools. Fine voluntary control was demonstrated to be reached with the myoelectric signals. And the UVa-NTS demonstrated to provide a satisfactory voluntary control when applying the myomechanic signals.

Electroencephalogram analysis with approximate entropy to help in the diagnosis of Alzheimer's disease

Alzheimers disease (AD) is the main cause of dementia in western countries. Although a definite diagnosis of this illness is only possible by necropsy, the analysis of nonlinear dynamics in electroencephalogram (EEG) signals could help physicians in this difficult task In this study we have applied approximate entropy (ApEn) to analyze the EEG background activity of patients with a clinical diagnosis of Alzheimers disease and control subjects. ApEn is a newly introduced statistic that can be used to quantify the complexity (or irregularity) of a time series. We have divided the EEG data into frames to calculate their ApEn. Our results show that the degree of complexity of EEGs from control subjects is higher. Applying the ANOVA test, we have verified that there was a significant difference (p < 0.05) between the EEGs of these groups.

A Robot Controlled by Blinking for Ambient Assisted Living

This article presents a system which allows interaction between a physically disabled person and his environment. It contributes to achieve an Ambient Assisted Living (AAL). This system is controlled by voluntary muscular movements, particularly the orbicular ones. These movements are translated into instructions which are sent by means of a wireless link to a mobile robot that executes them. This robot includes a video camera in order to show the environment of the route that the robot follows on its way to the user. It also includes a subsystem that contains light and sound signals. This system can aid people with reduced mobility, extending the time that older people and disabled people can live in their home environment, increasing their autonomy and their confidence.

Myo-Pong: A neuromuscular game for the UVa-Neuromuscular Training System platform

This paper presents the game Myo-Pong, a simple graphical table-tennis game included in the portable UVa Neuromuscular Training System (UVa-NTS). Myo-Pong demonstrates the capabilities of the UVa-NTS as a myoelectric real-time system for training and for playing by means of myoelectric control.

Programmable Gain Amplifiers with DC Suppression and Low Output Offset for Bioelectric Sensors

DC-offset and DC-suppression are key parameters in bioelectric amplifiers. However, specific DC analyses are not often explained. Several factors influence the DC-budget: the programmable gain, the programmable cut-off frequencies for high pass filtering and, the low cut-off values and the capacitor blocking issues involved. A new intermediate stage is proposed to address the DC problem entirely. Two implementations were tested. The stage is composed of a programmable gain amplifier (PGA) with DC-rejection and low output offset. Cut-off frequencies are selectable and values from 0.016 to 31.83 Hz were tested, and the capacitor deblocking is embedded in the design. Hence, this PGA delivers most of the required gain with constant low output offset, notwithstanding the gain or cut-off frequency selected.

Adaptive framework for uncertainty analysis in electromagnetic field measurements

Misinterpretation of uncertainty in the measurement of the electromagnetic field (EMF) strength may lead to an underestimation of exposure risk or an overestimation of required measurements. The Guide to the Expression of Uncertainty in Measurement (GUM) has internationally been adopted as a de facto standard for uncertainty assessment. However, analyses under such an approach commonly assume unrealistic static models or neglect relevant prior information, resulting in non-robust uncertainties. This study proposes a principled and systematic framework for uncertainty analysis that fuses information from current measurements and prior knowledge. Such a framework dynamically adapts to data by exploiting a likelihood function based on kernel mixtures and incorporates flexible choices of prior information by applying importance sampling. The validity of the proposed techniques is assessed from measurements performed with a broadband radiation meter and an isotropic field probe. The developed framework significantly outperforms GUM approach, achieving a reduction of 28% in measurement uncertainty.

The UVa-Neuromuscular Training System Platform

This paper presents the portable UVa Neuromuscular Training System (UVa-NTS). It is a myoelectric real-time system for research and upper-limb training. A set of training tools is included: this paper focuses on the game Myo-Pong, a simple graphical table-tennis game included in the UVa-NTS. To measure the performance, a set of control parameters is explained. Thus, Myo-Pong demonstrates the capabilities of the UVa-NTS as a myoelectric real-time system for training and for playing by means of myoelectric control.

Automatic UMTS system resource dimensioning based on service traffic analysis

Mobile network operators base their TDM network capacity dimensioning on Erlang B models. This approach was valid in legacy GSM networks. However, current Universal Mobile Telecommunications System networks deal with different resource consumption services such as voice, video call or data, and different limiting resources such as baseband processing capacity, transmission link capacity to the RNC, or spreading code tree. Operators need models to decide which resource must be upgraded, according to the demand of the services, in order to achieve expected overall service accessibility (i.e., the complementary of blocking probability). Network operation requires detecting when degradation is due to a lack of resources or to a hardware malfunction. Also, when operators need to prevent blockage in a high-capacity demanding event (for which they only have traffic predictions for each service) it is far from trivial to dimension resources. We have implemented a Kaufman Roberts approach to characterize the multiservice resource demand. Using real reported traffic Key Performance Indicators to calibrate the model, an estimated accessibility is obtained at a per-resource level and combined to find global estimated accessibility. The proposed model is intended to assist network operation, estimating individual resource shortage, differentiating congestion from hardware failures, and predicting the necessary resources to be deployed to tackle a high-capacity demanding event.