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As multiple sclerosis (MS) usually affects the visual pathway, visual electrophysiological tests can be used to diagnose it. The objective of this paper is to research methods for processing multifocal electroretinogram (mfERG) recordings to improve the capacity to diagnose MS. MfERG recordings from 15 early-stage MS patients without a history of optic neuritis and from 6 control subjects were examined. A normative database was built from the control subject signals. The mfERG recordings were filtered using empirical mode decomposition (EMD). The correlation with the signals in a normative database was used as the classification feature. Using EMD-based filtering and performance correlation, the mean area under the curve (AUC) value was 0.90. The greatest discriminant capacity was obtained in ring 4 and in the inferior nasal quadrant (AUC values of 0.96 and 0.94, respectively). Our results suggest that the combination of filtering mfERG recordings using EMD and calculating the correlation with a normative database would make mfERG waveform analysis applicable to assessment of multiple sclerosis in early-stage patients.

PURPOSE To explore the applicability of multifocal visual evoked potentials (mfVEPs) for research and clinical diagnosis in patients with optic disc drusen (ODD). This is the first assessment of mfVEP amplitude in patients with ODD. METHODS MfVEP amplitude and latency from 33 patients with ODD and 22 control subjects were examined. Mean amplitude, mean inner ring (IR) amplitude (0.87-5.67° of visual field) and mean outer ring amplitude (5.68-24° of visual field) were calculated using signal-to-noise ratio (SNR) and peak-to-peak analysis. Monocular latency was calculated using second peak analysis, while latency asymmetry was calculated using cross-correlation analysis. RESULTS Compared to normals, significantly decreased mean overall amplitude (p

The aim of this study was proposing gamma band activity (GBA) as an index of training-related brain plasticity in the motor cortex. Sixteen controls underwent an experimental session where electroencephalography (EEG) activity was recorded at baseline (resting) and during a motor task (hand movements). GBA was obtained from the EEG data at baseline and during the task. Index of plasticity (IP) was defined as the relationship between GBA at the end of the motor task (GBAM_FIN), divided by GBA at the beginning of the task (GBAM_INI) for movements of both hands. There was a significant increase in GBA at the end of the task, compared to the initial GBA for the motor task (GBAM_FIN > GBAM_INI). No differences were found at baseline (GBAB_FIN ≈ GBAB_INI). Individual IP values had a positive (r = 0.624) and significant correlation with subject’s handedness. Due to plastic changes, GBA could indirectly but objectively reveal changes in cerebral activity related to physical training. This method could be used as a future diagnostic test in the follow-up of patients undergoing rehabilitation. It could also have potential applications in the fields of sports medicine., Universidad de Alcalá, Ministerio de Economía y Competitividad

Current automotive electromagnetic compatibility (EMC) standards do not discuss the efiect of the driving proflle on real tra-c vehicular radiated emissions. This paper describes a modeling methodology to evaluate the radiated electromagnetic emissions of electric motorcycles in terms of the driving proflle signals such as the vehicle velocity remotely controlled by means of a CAN bus. A time domain EMI measurement system has been used to measure the temporal evolution of the radiated emissions in a semi-anechoic chamber. The CAN bus noise has been reduced by means of adaptive frequency domain cancellation techniques. Experimental results demonstrate that there is a temporal relationship between the motorcycle velocity and the radiated emission power in some speciflc frequency ranges. A Multilayer Perceptron (MLP) neural model has been developed to estimate the radiated emissions power in terms of the motorcycle velocity. Details of the training and testing of the developed neural estimator are described.

Objective:Propose a simplified method applicable in routine clinical practice that uses EEG to assess induced gamma-band activity (GBA) in the 30–90 Hz frequency range in cerebral motor areas.Design:EEG recordings (25 healthy subjects) of cerebral activity (at rest, motor task). GBA was obtained as power spectral density (PSD). GBA — defined as the gamma index (Iγ) — was calculated using the basal GBA (γB) and motor GBA (γMOV) PSD values.Results:The mean values of Iγ were (IγR (right hand) = 1.30, IγL (left hand) = 1.22). Manual laterality showed a correlation with Iγ.Conclusions:Iγ may provide a useful way of indirectly assessing operation of activated motor neuronal circuits. It could be applied to diagnosis of motor area pathologies and as follow up in rehabilitation processes. Likewise, Iγ could enable the assessment of motor capacity, physical training and manual laterality in sport medicine.

Glaucoma is a leading cause of permanent blindness. Glaucoma has an initial phase difficult to diagnose, so different measures are used to evaluate it. One of them is a retinography exam which is used to determinate the ratio between the cup and the disc areas, and vein shifting in disc perimeter. A system that allows different measures and calculate them automatically, could be useful to medical community. In this paper it shows a study about papilla and cup segmentation, their ratio and vein shifting ratio and their classification using neural networks. Our system is able to classify the glaucoma automatically with a sensitivity and specifity of 96,77% and 68,2% respectively.

This paper focuses on the developed methodology for the experimental evaluation of electrical vehicle-driver interaction and its effect on relative electromagnetic emissions, i.e. detecting in which way the driving style determines these pollutant emissions. Besides the theoretical aspects, specific instrumentation and set-up designed for this work, several results directly registered from electrical vehicles (fuel cell and battery powered) are shown. To made easier the results evaluation, maximum-average charts, spectrograms and 3D figures are included. The experimental tests allow deducing that the temporary association between the driver activity and the related electromagnetic emissions from electrical vehicles is more relevant at low frequencies (150 KHz-30MHz).