EEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer's disease

Recent experimental evidence has suggested a neuromodulatory deficit in Alzheimer's disease (AD). In this paper, we present a new electroencephalogram (EEG) based metric to quantitatively characterize neuromodulatory activity. More specifically, the short-term EEG amplitude modulation rate-of-change (i.e., modulation frequency) is computed for five EEG subband signals. To test the performance of the proposed metric, a classification task was performed on a database of 32 participants partitioned into three groups of approximately equal size: healthy controls, patients diagnosed with mild AD, and those with moderate-to-severe AD. To gauge the benefits of the proposed metric, performance results were compared with those obtained using EEG spectral peak parameters which were recently shown to outperform other conventional EEG measures. Using a simple feature selection algorithm based on area-under-the-curve maximization and a support vector machine classifier, the proposed parameters resulted in accuracy gains, relative to spectral peak parameters, of 21.3% when discriminating between the three groups and by 50% when mild and moderate-to-severe groups were merged into one. The preliminary findings reported herein provide promising insights that automated tools may be developed to assist physicians in very early diagnosis of AD as well as provide researchers with a tool to automatically characterize cross-frequency interactions and their changes with disease.

Cognitive, Mood, and Electroencephalographic Effects of Noninvasive Cortical Stimulation With Weak Electrical Currents

Objectives: The use of noninvasive cortical electrical stimulation with weak currents has significantly increased in basic and clinical human studies. Initial, preliminary studies with this technique have shown encouraging results; however, the safety and tolerability of this method of brain stimulation have not been sufficiently explored yet. The purpose of our study was to assess the effects of direct current (DC) and alternating current (AC) stimulation at different intensities in order to measure their effects on cognition, mood, and electroencephalogram. Methods: Eighty-two healthy, right-handed subjects received active and sham stimulation in a randomized order. We conducted 164 ninety-minute sessions of electrical stimulation in 4 different protocols to assess safety of (1) anodal DC of the dorsolateral prefrontal cortex (DLPFC); (2) cathodal DC of the DLPFC; (3) intermittent anodal DC of the DLPFC and; (4) AC on the zygomatic process. We used weak currents of 1 to 2 mA (for DC experiments) or 0.1 to 0.2 mA (for AC experiment). Results: We found no significant changes in electroencephalogram, cognition, mood, and pain between groups and a low prevalence of mild adverse effects (0.11% and 0.08% in the active and sham stimulation groups, respectively), mainly, sleepiness and mild headache that were equally distributed between groups. Conclusions: Here, we show no neurophysiological or behavioral signs that transcranial DC stimulation or AC stimulation with weak currents induce deleterious changes when comparing active and sham groups. This study provides therefore additional information for researchers and ethics committees, adding important results to the safety pool of studies assessing the effects of cortical stimulation using weak electrical currents. Further studies in patients with neuropsychiatric disorders are warranted.

Introduction to the Software-defined Radio Approach

Telecommunications have been in constant evolution during past decades. Among the technological innovations, the use of digital technologies is very relevant. Digital communication systems have proven their efficiency and brought a new element in the chain of signal transmitting and receiving, the digital processor. This device offers to new radio equipments the flexibility of a programmable system. Nowadays, the behavior of a communication system can be modified by simply changing its software. This gave rising to a new radio model called Software Defined Radio (or Software-Defined Radio - SDR). In this new model, one moves to the software the task to set radio behavior, leaving to hardware only the implementation of RF front-end. Thus, the radio is no longer static, defined by their circuits and becomes a dynamic element, which may change their operating characteristics, such as bandwidth, modulation, coding rate, even modified during runtime according to software configuration. This article aims to present the use of GNU Radio software, an open-source solution for SDR specific applications, as a tool for development configurable digital radio.

Automatic aspect discrimination in data clustering

The attributes describing a data set may often be arranged in meaningful subsets, each of which corresponds to a different aspect of the data. An unsupervised algorithm (SCAD) that simultaneously performs fuzzy clustering and aspects weighting was proposed in the literature. However, SCAD may fail and halt given certain conditions. To fix this problem, its steps are modified and then reordered to reduce the number of parameters required to be set by the user. In this paper we prove that each step of the resulting algorithm, named ASCAD, globally minimizes its cost-function with respect to the argument being optimized. The asymptotic analysis of ASCAD leads to a time complexity which is the same as that of fuzzy c-means. A hard version of the algorithm and a novel validity criterion that considers aspect weights in order to estimate the number of clusters are also described. The proposed method is assessed over several artificial and real data sets.

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

Enhancement of optical absorption by modulation of the oxygen flow of TiO2 films deposited by reactive sputtering

Oxygen-deficient TiO2 films with enhanced visible and near-infrared optical absorption have been deposited by reactive sputtering using a planar diode radio frequency magnetron configuration. It is observed that the increase in the absorption coefficient is more effective when the O-2 gas supply is periodically interrupted rather than by a decrease of the partial O-2 gas pressure in the deposition plasma. The optical absorption coefficient at 1.5 eV increases from about 1 x 10(2) cm(-1) to more than 4 x 10(3) cm(-1) as a result of the gas flow discontinuity. A red-shift of similar to 0.24 eV in the optical absorption edge is also observed. High resolution transmission electron microscopy with composition analysis shows that the films present a dense columnar morphology, with estimated mean column width of 40nm. Moreover, the interruptions of the O-2 gas flow do not produce detectable variations in the film composition along its growing direction. X-ray diffraction and micro-Raman experiments indicate the presence of the TiO2 anatase, rutile, and brookite phases. The anatase phase is dominant, with a slight increment of the rutile and brookite phases in films deposited under discontinued O-2 gas flow. The increase of optical absorption in the visible and near-infrared regions has been attributed to a high density of defects in the TiO2 films, which is consistent with density functional theory calculations that place oxygen-related vacancy states in the upper third of the optical bandgap. The electronic structure calculation results, along with the adopted deposition method and experimental data, have been used to propose a mechanism to explain the formation of the observed oxygen-related defects in TiO2 thin films. The observed increase in sub-bandgap absorption and the modeling of the corresponding changes in the electronic structure are potentially useful concerning the optimization of efficiency of the photocatalytic activity and the magnetic doping of TiO2 films. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4724334]

Supervised classification of basaltic aggregate particles based on texture properties

The strength and durability of materials produced from aggregates (e.g., concrete bricks, concrete, and ballast) are critically affected by the weathering of the particles, which is closely related to their mineral composition. It is possible to infer the degree of weathering from visual features derived from the surface of the aggregates. By using sound pattern recognition methods, this study shows that the characterization of the visual texture of particles, performed by using texture-related features of gray scale images, allows the effective differentiation between weathered and nonweathered aggregates. The selection of the most discriminative features is also performed by taking into account a feature ranking method. The evaluation of the methodology in the presence of noise suggests that it can be used in stone quarries for automatic detection of weathered materials.