J. Mateo

A five-year follow-up study of neurocognitive functioning in bipolar disorder.

Cognitive dysfunction in bipolar disorder has been well‐established in cross‐sectional studies; however, there are few data regarding the longitudinal course of cognitive performance in bipolar disorder. The aim of this study was to examine the course of cognitive function in a sample of euthymic patients with bipolar disorder during a five‐year follow‐up period.

Anesthesia with propofol slows atrial fibrillation dominant frequencies

The mechanisms responsible for the maintenance of atrial fibrillation (AF) are not completely understood yet. It has been demonstrated that AF can be modulated by several cardiac diseases, the autonomic nervous system and even drugs with purportedly no antiarrhythmic properties. We evaluated the effects of a widely used anaesthetic agent (propofol) in the fibrillation patterns. Spectral analysis was performed over atrial electrograms at baseline and immediately after a propofol bolus. Only after performing principal component analysis (PCA), we were able to significantly detect that propofol slows AF.

A new method for removal of powerline interference in ECG and EEG recordings

Display Omitted A method based on radial basis function and Wiener filter system is proposed for filtering powerline in biomedical recordings.The proposed solution addresses both ECG and EEG recordings.Several simulations have demonstrated the enhancement of the proposed method in comparison to other techniques.The results suggest that clinical information can be maintained.This method provides the best approach for obtaining both more signal reduction and low distortion of the signal results. Advanced medical diagnosing and research requires precise information which can be obtained from measured electrophysiological data, e.g., electroencephalogram (EEG) and electrocardiograph (ECG). However, they are often contaminated with noise and a variety of bioelectric signals called artefacts, e.g., electromyography (EMG). These noise and artefacts which need to be reduced make it difficult to distinguish normal from abnormal physiological activity. Electromagnetic contamination of recorded signals represents a major source of noise in electrophysiology and impairs the use of recordings for research. In this paper we present an effective method for cancelling 50Hz (or 60Hz) interference using a radial basis function (RBF) Wiener hybrid filter. One of the main points of this paper is the hybridization of the RBF filter and a Wiener filter to make full use of both merits. The effectiveness and validity of those filters are verified by applying them to ECG and EEG recordings. The results show that the proposed method is able to reduce powerline interference (PLI) from the noisy ECG and EEG signals more accurately and consistently in comparison to some of the state of-the-art methods and this method can be efficiently used with very low signal-to-noise ratios, while preserving original signal waveform.

Robust Volterra Filter Design for Enhancement of Electroencephalogram Signal Processing

Electroencephalogram (EEG) recordings often experience interference by different kinds of noise, including white, muscle, and baseline, severely limiting its utility. The recent research has demonstrated that discrete-time Volterra models can be successfully applied to reduce the broadband and narrowband noise. Their usefulness is mainly because of their ability to approximate to an arbitrary precision any fading memory system and their property of linearity with respect to parameters, the kernels coefficients. The main drawback of these models is their parametric complexity implying the need to estimate a huge number of parameters. Numerical results show that the developed algorithm achieves performance improvement over the standard filtered algorithm. This paper presents a Volterra filter (VF) algorithm based on a multichannel structure for noise reduction. Several methods have been developed, but the VF appears to be the most effective for reducing muscle and baseline noise, especially when the contamination is greater in amplitude than the brain signal. The present study introduces a new method of reducing noise in EEG signals in one step with low EEG distortion and high noise reduction. Applications with different real and synthetic signals are discussed, showing the validity of the proposed method.

Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation

The most extended noninvasive technique for medical diagnosis and analysis of atrial fibrillation (AF) relies on the surface elctrocardiogram (ECG). In order to take optimal profit of the ECG in the study of AF, it is mandatory to separate the atrial activity (AA) from other cardioelectric signals. Traditionally, template matching and subtraction (TMS) has been the most widely used technique for single-lead ECGs, whereas multi-lead ECGs have been addressed through statistical signal processing techniques, like independent component analysis. In this contribution, a new QRST cancellation method based on a radial basis function (RBF) neural network is proposed. The system is able to provide efficient QRST cancellation and can be applied both to single and multi-lead ECG recordings. The learning algorithm used for training the RBF makes use of a special class of network, known as cosine RBF, by updating selected adjustable parameters to minimize the class-conditional variances at the outputs of the network. The experiments verify that RBFs trained by the proposed learning algorithm are capable of reducing the QRST complex dramatically, a property that is not shared by other methods and conventional feed-forward neural networks. Average Results (mean ± std) for the RBF method in cross-correlation (CC) between original and estimated AA are CC=0.95±0.038 being the mean square error (MSE) for the same signals, MSE=0.311±0.078. Regarding spectral parameters, the dominant amplitude (DA) and the mean power spectral (MP) were DA=1.15±0.18 and MP=0.31±0.07, respectively. In contrast, traditional TMS-based methods yielded, for the best case, CC=0.864±0.041, MSE=0.577±0.097, DA=0.84±0.25 and MP=0.24±0.07. The results prove that the RBF based method is able to obtain a remarkable reduction of ventricular activity and a very accurate preservation of the AA, thus providing high quality dissociation between atrial and ventricular activities in AF recordings.

Eye interference reduction in electroencephalogram recordings using a radial basic function

The electroencephalogram (EEG) signal is the manifestation of brain activity recorded as changes in electrical potentials at multiple locations over the scalp and it can be distorted by many other sources of electrical activity, called eye artefacts. It is important to remove these artefact signals before analysing the EEG signal, to obtain accurate information about brain activity and avoid mistakes in its interpretation. To deal with this problem, the present study proposes an artificial neural network, as a filter to remove ocular artefacts. In the proposed method, the number of radial basis function (RBF) neurons and input output space clustering are adaptively determined. Furthermore, the structure of the system and the parameters of the corresponding RBF units are trained automatically and relatively fast adaptation is attained. By the least-square error estimator techniques, the proposed system is suitable for real EEG applications. The proposed system improves results yielded by conventional techniques of ocular reduction, such as principal component analysis, support vector machines and independent component analysis systems. As a consequence, the algorithm could serve as an effective framework to reduce substantially eye interference in EEG recordings.

A method for removing noise from continuous brain signal recordings

The electroencephalogram (EEG) is the most widely used method for diagnosis of brain diseases, where a good quality of recordings allows the proper interpretation and identification of physiological and pathological phenomena. However, EEG recordings are often contaminated by different kinds of noise. These annoying signals limit severely brain recording utility and, hence, have to be removed. To deal with this problem, in this paper an adaptive filtering framework is proposed for the enhancing of brain signal recordings. This new method is capable of reducing muscle and baseline noise in EEG signals with low EEG distortion and high noise cancellation. The advantages of the proposed method are demonstrated on real and synthetic brain signals with comparisons made to several benchmark methods. Results show that the proposed approach is preferable to the other systems by achieving a better trade-off between deleting noises and preserving inherent brain activities.

Noise removal in electroencephalogram signals using an artificial neural network based on the simultaneous perturbation method

Abstract Electroencephalogram (EEG) recordings often experience interference by different kinds of noise, including white, muscle and baseline, severely limiting its utility. Artificial neural networks (ANNs) are effective and powerful tools for removing interference from EEGs. Several methods have been developed, but ANNs appear to be the most effective for reducing muscle and baseline contamination, especially when the contamination is greater in amplitude than the brain signal. An ANN as a filter for EEG recordings is proposed in this paper, developing a novel framework for investigating and comparing the relative performance of an ANN incorporating real EEG recordings. This method is based on a growing ANN that optimized the number of nodes in the hidden layer and the coefficient matrices, which are optimized by the simultaneous perturbation method. The ANN improves the results obtained with the conventional EEG filtering techniques: wavelet, singular value decomposition, principal component analysis, adaptive filtering and independent components analysis. The system has been evaluated within a wide range of EEG signals. The present study introduces a new method of reducing all EEG interference signals in one step with low EEG distortion and high noise reduction.

An efficient method for ECG beat classification and correction of ectopic beats

A method based on radial basis function system for cancelling out ectopic beat by classification between normal and abnormal beats is proposed.The proposed solution addresses for ECG recordings.Several experiments have demonstrated the enhancement of the proposed method in comparison to other techniques.The results suggest that clinical information can be maintained.This method provides the best approach for obtaining both ectopic beat reduction and low distortion of the signal recordings. Display Omitted The analysis of the surface Electrocardiogram (ECG) is the most extended non-invasive technique in cardiological diagnosis. The ectopic beats are heart beats remarkably different to the normal beat morphology that provoke serious disturbances in electrocardiographic analysis. These beats are very common in atrial fibrillation (AF), causing important residua when ventricular activity has to be removed for atrial activity (AA) analysis. These beats may occur in both normal subjects and patients with heart disease, and their presence represents an important source of error which must be handled before any other analysis. In this work, a method is proposed to cancel out ectopics by classification between normal and abnormal beats. The systems is based on Radial Basis Function Neural Network (RBFNN). This new approach is compared to state-of-the-art techniques for the ectopic classification and cancellation in the MIT database. The results clearly demonstrated the improved ECG beats classification accuracy compared with other alternatives and a very accurate reduction of ectopic beats together with low distortion of the QRST complex.

Prepulse inhibition in euthymic bipolar disorder patients in comparison with control subjects.

Deficient prepulse inhibition (PPI) of the startle response, indicating sensorimotor gating deficits, has been reported in schizophrenia and other neuropsychiatric disorders. This study aimed to assess sensorimotor gating deficits in patients with euthymic bipolar. Furthermore, we analysed the relationships between PPI and clinical and cognitive measures.