This work proposes the application of a new electroencephalogram (EEG) signal processing tool - the lacsogram - to characterize the Alzheimer's disease (AD) activity and to assist on its diagnosis at different stages: Mild Cognitive Impairment (MCI), Mild and Moderate AD (ADM) and Advanced AD (ADA). Statistical analyzes are performed to lacstral distances between conventional EEG subbands to find measures capable of discriminating AD in all stages and characterizing the AD activity in each electrode. Cepstral distances are used for comparison. Comparing all AD stages and Controls (C), the most important significances are the lacstral distances between subbands θ and α (p = 0.0014 $<; $ 0.05). The topographic maps show significant differences in parietal, temporal and frontal regions as AD progresses. Machine learning models with a leave-one-out cross-validation process are applied to lacstral/cepstral distances to develop an automatic method for diagnosing AD. The following classification accuracies are obtained with an artificial neural network: 95.55% for All vs All, 98.06% for C vs MCI, 95.99% for C vs ADM, 93.85% for MCI vs ADM-ADA. In C vs MCI, C vs ADM and MCI vs ADM-ADA, the proposed method outperforms the state-of-art methods by 5%, 1%, and 2%, respectively. In All vs All, it outperforms the state-of-art EEG and non-EEG methods by 6% and 2%, respectively. These results indicate that the proposed method represents an improvement in diagnosing AD.

Purpose:: Alzheimer’s disease (AD) has the highest worldwide prevalence of all neurodegenerative disorders, no cure, and low ratios of diagnosis accuracy at its early stage where treatments have some effect and can give some years of life quality to patients. This work aims to develop an automatic method to detect AD in 3 different stages, namely, control (CN), mild-cognitive impairment (MCI), and AD itself, using structural magnetic resonance imaging (sMRI). Methods:: A set of co-occurrence matrix and texture statistical measures (contrast, correlation, energy, homogeneity, entropy, variance, and standard deviation) were extracted from a two-level discrete wavelet transform decomposition of sMRI images. The discriminant capacity of the measures was analyzed and the most discriminant ones were selected to be used as features for feeding classical machine learning (cML) algorithms and a convolution neural network (CNN). Results:: The cML algorithms achieved the following classification accuracies: 93.3% for AD vs CN, 87.7% for AD vs MCI, 88.2% for CN vs MCI, and 75.3% for All vs All. The CNN achieved the following classification accuracies: 82.2% for AD vs CN, 75.4% for AD vs MCI, 83.8% for CN vs MCI, and 64% for All vs All. Conclusion:: In the evaluated cases, cML provided higher discrimination results than CNN. For the All vs All comparison, the proposed method surpasses by 4% the discrimination accuracy of the state-of-the-art methods that use structural MRI.

The occlusion effect is a common complaint from users of hearing aids with narrow or unvented earmolds. This phenomenon makes the user hear his own voice muffled. In the scientific literature, fixed and adaptive controllers have been proposed for occlusion effect reduction. This work proposes a cepstral method to estimate the stable optimal solution for feedforward occlusion cancellation and a fixed controller that utilizes this estimate to reduce the occlusion effect in hearing aids. The cepstral method operates on a feedback structure at a calibration process. Simulations have shown that the performance of the cepstral method improves as the length of the signal uttered by the hearing aid user increases, resulting in estimates with average normalized misalignment less than -19 and -34 dB for signals lasting 1.5 and 5 s. The estimates are significantly more accurate below 500 Hz, which is the frequency range common to the occlusion effect. In addition, results have pointed out that the controller attenuated the occlusion effect, averagely decreasing by 0.17 dB the distortion power and increasing by 0.13 the objective perceptual quality MOS-LQO score.

This work aims to detect Alzheimer's disease (AD) through non-linear features of speech signals. The features are extracted from signal subbands, which are obtained through the wavelet transform, and some of its descriptive statistics are used as input for several classifiers. Accuracies of 100, 77.8 and 85.2% are obtained in detecting AD among women, men and all, respectively, using logistic regression classifiers.