The purpose of this paper is to determine whether gamma-band activity detection is improved when a filter, based on empirical mode decomposition (EMD), is added to the pre-processing block of single-channel electroencephalography (EEG) signals. EMD decomposes the original signal into a finite number of intrinsic mode functions (IMFs). EEGs from 25 control subjects were registered in basal and motor activity (hand movements) using only one EEG channel. Over the basic signal, IMF signals are computed. Gamma-band activity is computed using power spectrum density in the 30–60 Hz range. Event-related synchronization (ERS) was defined as the ratio of motor and basal activity. To evaluate the performance of the new EMD based method, ERS was computed from the basic and IMF signals. The ERS obtained using IMFs improves, from 31.00% to 73.86%, on the original ERS for the right hand, and from 22.17% to 47.69% for the left hand. As EEG processing is improved, the clinical applications of gamma-band activity will expand.

Background: The aim of this paper is to implement a system to facilitate the diagnosis of multiple sclerosis (MS) in its initial stages. It does so using a convolutional neural network (CNN) to classify images captured with swept-source optical coherence tomography (SS-OCT). Methods: SS-OCT images from 48 control subjects and 48 recently diagnosed MS patients have been used. These images show the thicknesses (45 × 60 points) of the following structures: complete retina, retinal nerve fiber layer, two ganglion cell layers (GCL+, GCL++) and choroid. The Cohen distance is used to identify the structures and the regions within them with greatest discriminant capacity. The original database of OCT images is augmented by a deep convolutional generative adversarial network to expand the CNN’s training set. Results: The retinal structures with greatest discriminant capacity are the GCL++ (44.99% of image points), complete retina (26.71%) and GCL+ (22.93%). Thresholding these images and using them as inputs to a CNN comprising two convolution modules and one classification module obtains sensitivity = specificity = 1.0. Conclusions: Feature pre-selection and the use of a convolutional neural network may be a promising, nonharmful, low-cost, easy-to-perform and effective means of assisting the early diagnosis of MS based on SS-OCT thickness data.

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.

Multiple sclerosis (MS) and Alzheimer’s disease (AD) cause retinal thinning that is detectable in vivo using optical coherence tomography (OCT). To date, no papers have compared the two diseases in terms of the structural differences they produce in the retina. The purpose of this study is to analyse and compare the neuroretinal structure in MS patients, AD patients and healthy subjects using OCT. Spectral domain OCT was performed on 21 AD patients, 33 MS patients and 19 control subjects using the Posterior Pole protocol. The area under the receiver operating characteristic (AUROC) curve was used to analyse the differences between the cohorts in nine regions of the retinal nerve fibre layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL) and outer nuclear layer (ONL). The main differences between MS and AD are found in the ONL, in practically all the regions analysed (AUROC_FOVEAL = 0.80, AUROC_PARAFOVEAL = 0.85, AUROC_PERIFOVEAL = 0.80, AUROC__PMB = 0.77, AUROC_PARAMACULAR = 0.85, AUROC_{INFERO_NASAL} = 0.75, AUROC_{INFERO_TEMPORAL} = 0.83), and in the paramacular zone (AUROC_PARAMACULAR = 0.75) and infero-temporal quadrant (AUROC_{INFERO_TEMPORAL} = 0.80) of the GCL. In conclusion, our findings suggest that OCT data analysis could facilitate the differential diagnosis of MS and AD.

Background: The latest revision of the McDonald criteria for diagnosis of multiple sclerosis (MS) establishes that the optic nerve can serve as a fifth anatomical location within the central nervous system for diagnosis. Optical coherence tomography (OCT) images can serve as evidence for this purpose. Objective: To assess the accuracy of automated artificial-intelligence-based classification of MS patients using OCT data obtained from two different centers. Methods: OCT data were collected from two centers using standardized APOSTEL-based protocols and similar equipment. Retinal layer thicknesses—mean and standard deviation (STD) values—were analyzed in four layers and in six regions per layer per eye. A support vector machine classifier with recursive feature elimination and Shapley additive explanations value analysis was applied to identify the most relevant features and maximize classification accuracy between control subject and MS patient eyes. Results: The database drawn from two hospitals comprised 112 eyes with MS without prior history of optic neuritis and 193 eyes of control subjects. The classifier achieved maximum accuracy (0.8459) using 20 input features. The mean and STD metrics had similar importance, with the most influential layers being the ganglion cell layer, inner plexiform layer, and the inner retinal layer complex. Key regions included the papillomacular bundle and the superior temporal perimacular area. Conclusions: OCT data facilitates highly accurate MS diagnosis across different centers. Artificial intelligence assessment could facilitate automated classification. These findings provide evidence of the important role of the optic nerve in MS diagnosis.