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Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease (AD) from electroencephalography (EEG). However, choosing suitable measures is a challenging task. Among other measures, frequency relative power (RP) and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency RP on EEG signals, examining the changes found in different frequency ranges. Approach .W efirst explore the use of a single feature for computing the classification rate (CR), looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing mild cognitive impairment (MCI) and healthy subjects (patients age: 71.9±10.2, healthy subjects age: 71.7±8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6±10.0; healthy subjects age: 69.4±11.5). Main results. Using a single feature to compute CRs we achieve a performance of 78.33% for the MCI data set and of 97.56% for Mild AD. Results are clearly improved using the multiple feature classification, where a CR of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using four features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

Despite recent advances, early diagnosis of Alzheimer’s disease (AD) from electroencephalography (EEG) remains a difficult task. In this paper, we offer an added measure through which such early diagnoses can potentially be improved. One feature that has been used for discriminative classification is changes in EEG synchrony. So far, only the decrease of synchrony in the higher frequencies has been deeply analyzed. In this paper, we investigate the increase of synchrony found in narrow frequency ranges within the θ band. This particular increase of synchrony is used with the well-known decrease of synchrony in the band to enhance detectable differences between AD patients and healthy subjects. We propose a new synchrony ratio that maximizes the differences between two populations. The ratio is tested using two different data sets, one of them containing mild cognitive impairment patients and healthy subjects, and another one, containing mild AD patients and healthy subjects. The results presented in this paper show that classification rate is improved, and the statistical difference between AD patients and healthy subjects is increased using the proposed ratio.

Monitoring the breathing is required in many applications of medical and health fields, but it can be used also in new game applications, for example. In this work, an automatic system for monitoring the breathing is presented. The system uses the acoustic signal recorded by a standard microphone placed in the area of the nostrils. The system is based on a low-complex signal parameterization performed on non-overlapped frames. From this parameterization, a reduced set of real parameters is obtained frame-to-frame. These parameters feed a classifier that performs a classification in three stages: inspiration, transition or retention and expiration providing a fine monitoring of the respiration process. As all of those algorithms are of low complexity and the auxiliary equipment required could only be a standard microphone from a conventional Bluetooth Headset, the system could be able to run in a smartphone device. © 2013 Springer-Verlag Berlin Heidelberg.

BIOSIGNALS 2015 In this study the use of a new fast and stable decomposition technique, bi-dimensional empirical mode decomposition, is used for face recognition tasks. Images are decomposed individually, and then the distance with reference images is computed. Three different types of distances are tested. Then class association is based on minimum distance and by using a classifier. Preliminary results (90.0% of classification rate) are satisfactory and will justify a deep investigation on how to apply this bi- dimensional decomposition technique for face recognition.

Several clinical studies have reported that EEG synchrony is affected by Alzheimer's disease (AD). In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD using EEG signals. In this paper, multiple synchrony measures are assessed through statistical tests (Mann-Whitney U test), including correlation, phase synchrony and Granger causality measures. Moreover, linear discriminant analysis (LDA) is conducted with those synchrony measures as features. For the data set at hand, the frequency range (5-6 Hz) yields the best accuracy for diagnosing AD, which lies within the classical theta band (4-8 Hz). The corresponding classification error is 4.88% for directed transfer function (DTF) Granger causality measure. Interestingly, results show that EEG of AD patients is more synchronous than in healthy subjects within the optimized range 5-6 Hz, which is in sharp contrast with the loss of synchrony in AD EEG reported in many earlier studies. This new finding may provide new insights about the neurophysiology of AD. Additional testing on larger AD datasets is required to verify the effectiveness of the proposed approach.