A method for detection of Alzheimer's disease using ICA-enhanced EEG measurements.

OBJECTIVE: Many researchers have studied automatic EEG classification and recently a lot of work has been done on artefact-removal from EEG data using independent component analyses (ICA). However, demonstrating that a ICA-processed multichannel EEG measurement becomes more interpretable compared to the raw data (as is usually done in work on ICA-processing of EEG data) does not yet prove that detection of (incipient) anomalies is also better possible after ICA-processing. The objective of this study is to show that ICA-preprocessing is useful when constructing a detection system for Alzheimer's disease. METHODS AND MATERIAL: The paper describes a method for detection of EEG patterns indicative of Alzheimer's disease using automatic pattern recognition techniques. Our method incorporates an artefact removal stage based on ICA prior to automatic classification. The method is evaluated on measurements of a length of 8s from two groups of patients, where one group is in an initial stage of the disease (28 patients), whereas the other group is in a more progressed stage (15 patients). Both setups include a control group that should be classified as normal (10 and 21, respectively). RESULTS: Our final classification results for the group with severe Alzheimer's disease are comparable to the best results from literature. We show that ICA-based reduction of artefacts improves classification results for patients in an initial stage. CONCLUSION: We conclude that a more robust detection of Alzheimer's disease related EEG patterns may be obtained by employing ICA as ICA based pre-processing of EEG data can improve classification results for patients in an initial stage of Alzheimer's disease.

Hyperkinetic disorder in the ICD-10: EEG evidence for a definitional widening?

This study investigated EEG differences between children with Hyperkinetic Disorder (HKD), HKD sub-threshold attention deficit (HKDsub), and control children, in order to determine from an EEG perspective whether children with HKDsub represent a valid clinical disorder. Twenty-four boys were included in each of the three age-matched groups. The HKD group had greater total power and absolute delta and theta, more relative theta, and less relative alpha and beta than the control group. The HKDsub group had EEG profiles which were different from both control children and children with HKD, with the HKDsub group having EEG results generally between the HKD and control group. Additionally, a number of topographic differences were found in the frontal regions which suggest that the two HKD groups have independent EEG components. These results support the inclusion of a diagnostic category of attention deficit in future editions of the ICD.

Sources of EEG activity in learning disabled children.

The sources of different EEG frequencies were studied in 25 normal children and 46 learning disabled (not otherwise specified) children between 7 and 11 years old. The EEG sources were computed using Frequency-domain Variable Resolution Electromagnetic Tomography which produces a three dimensional picture of the currents at each EEG frequency. Significant differences between groups were observed. LD children showed more theta activity (3.5 to 7.02 Hz) in the frontal lobes and control children more alpha (9.75 to 12.87 Hz) in occipital areas. These results may support the maturational lag hypothesis, as the neurobiological cause of learning deficiencies not otherwise specified.

El ritmo Theta del hipocampo: ¿Un organizador temporal de la información sensorial y autonómica en la vigilia y el sueño? / Hippocampal Theta rhythm: is it a temporal organizer of sensory and autonomic information during wakefulness and sleep?

El ritmo theta (0) del hipocampo, presente en todos los estados del comportamiento, ha sido relacionado temporalmente con movimientos voluntarios, automáticos y reflejos, tanto durante la vigilia como en el sueño. Nuestra investigación se basa en la hipótesis de que los procesamientos sensoriales necesitan de un ordenamiento, una organización temporal de la información a ser procesada y almacenada, y que el ritmo del hipocampo podría contribuir a dicho ordenamiento temporal. Hemos demostrado que las descargas unitarias de neuronas auditivas y visuales en cobayas presentan correlación temporal con el ritmo hipocámpico (phase-locking). Esta relación temporal aparece tanto en las descargas espontáneas como en las provocadas por los estímulos específicos para cada modalidad sensorial. Las descargas neuronales fluctúan entre la situación de correlación y no correlación de fase con el ritmo . Este estado cambiante depende de distintas situaciones, conocidas y desconocidas. Entre las conocidas son destacables el cambio de estado comportamental (vigilia, sueño lento, sueño paradójico), cambios en las características del estímulo y cambios atencionales. Hemos encontrado, además, una importante correlación temporal entre el ritmo y la frecuencia cardíaca, fundamentalmente durante el sueño paradójico, situación en que los sistemas de control vegetativos se encuentran en su mínima expresión (AU)

Autoassociative MLP in sleep spindle detection.

Spindles are one of the most important short-lasting waveforms in sleep EEG. They are the hallmarks of the so-called Stage 2 sleep. Visual spindle scoring is a tedious workload, since there are often a thousand spindles in one all-night recording of some 8 hr. Automated methods for spindle detection typically use some form of fixed spindle amplitude threshold, which is poor with respect to inter-subject variability. In this work a spindle detection system allowing spindle detection without an amplitude threshold was developed. This system can be used for automatic decision making of whether or not a sleep spindle is present in the EEG at a certain point of time. An Autoassociative Multilayer Perceptron (A-MLP) network was employed for the decision making. A novel training procedure was developed to remove inconsistencies from the training data, which was found to improve the system performance significantly.

Age-related changes in rat hippocampal theta rhythms: a difference between type 1 and type 2 theta.

The age-related changes in two types of theta rhythms recorded from the hippocampus in young (4 months-old), mature (12-13 months-old) and aged (22-25 months-old) rats were investigated. The type 1 theta rhythm was measured from hippocampal EEG recorded from walking rats and the type 2 theta was measured from the EEG induced by reticular pontin oralis nucleus (PON) stimulation in urethane anesthetized rats. The peak frequency and the peak power were detected from power spectra calculated on each theta sample by fast Fourier transformation (FFT). No age-related alteration was observed on the peak frequency of type 1 theta rhythm. However, on type 2 theta rhythm, the peak frequency was decreased in the aged rats compared with the young and the mature rats. The type 2 theta rhythm is cholinergic, and therefore this result suggests that age-related deterioration can be clearly observed in the cholinergic system including the hippocampus in rats.

Possibilities of classification of topographically distributed neurophysiological multi-channel data.

Progress in quantifying states of cerebral function and in the further development of automated EEG processing demands the application of suitable methods for the reduction of neurophysiological multi-channel data as well as their automatic classification. The method used here for reducing multi-channel data was to gain distributions of parametric descriptors from EEG data from computer-aided topographic electroencephalometry (CATEEM), for example the relative and absolute band power in the frequency bands delta, theta, alpha 1, alpha 2, beta 1, beta 2, total power, median and mode frequency, and other parameters. These values were subjected to cluster analysis. The classification of EEG parameters was carried out by means of discrimination analysis and neural networks. The practicability of both procedures was demonstrated in the reduction and classification of EEG data in the context of a normed study involving 104 healthy adults. These data have been used as the basis for a new evaluation study of 60 additional intraoperative EEG recordings obtained with CATEEM. In that newly started study, the effects of sedative and anaesthetic drugs on EEG behavior and psychophysiologic behavior remain to be investigated.