New-Onset Alzheimer's Disease and Normal Subjects 100% Differentiated by P300.

Previous work has suggested that evoked potential analysis might allow the detection of subjects with new-onset Alzheimer's disease, which would be useful clinically and personally. Here, it is described how subjects with new-onset Alzheimer's disease have been differentiated from healthy, normal subjects to 100% accuracy, based on the back-projected independent components (BICs) of the P300 peak at the electroencephalogram electrodes in the response to an oddball, auditory-evoked potential paradigm. After artifact removal, clustering, selection, and normalization processes, the BICs were classified using a neural network, a Bayes classifier, and a voting strategy. The technique is general and might be applied for presymptomatic detection and to other conditions and evoked potentials, although further validation with more subjects, preferably in multicenter studies is recommended.

Waveform analysis of non-oscillatory independent components in single-trial auditory event-related activity in healthy subjects and Alzheimer's disease patients.

The objective was to characterize the non-oscillatory independent components (ICs) of the auditory event-related potential (ERP) waveform of an oddball task for normal and newly diagnosed Alzheimer's disease (AD) subjects, and to seek biomarkers for AD. Single trial ERP waveforms were analysed using independent components analysis (ICA) and k-means clustering. Two stages of clustering depended upon the magnitudes and latencies, and the scalp topographies of the non-oscillatory back-projected ICs (BICs) at electrode Cz. The electrical current dipole sources of the BICs were located using Low Resolution Electromagnetic Tomography (LORETA). Generally 3-10 BICs, of different latencies and polarities, occurred in each trial. Each peak was associated with positive and negative BICs. The trial-to-trial variations in their relative numbers and magnitudes may explain the variations in the averaged ERP reported, and the delay in the averaged P300 for AD patients. The BIC latencies, topographies and electrical current density maximum locations varied from trial-to-trial. Voltage foci in the BIC topographies identify the BIC source locations. Since statistical differences were found between the BICs in healthy and AD subjects, the method might provide reliable biomarkers for AD, if these findings are reproduced in a larger study, independently of other factors influencing the comparison of the two populations. The method can extract artefact- and EEG-free single trial ERP waveforms, offers improved ERP averages by selecting the trials on the basis of their BICs, and is applicable to other evoked potentials, conditions and diseases.

Nonlinear methods for biopattern analysis: role and challenges.

An important trend in medical technology is towards support for personalised healthcare, fuelled by developments in genomic-based medicine. New computational intelligent techniques for biodata analysis will be needed to fully exploit the vast amounts of data that are being generated. Non-linear signal processing methods will form an important part of such computational intelligent techniques. This paper introduces some non-linear methods which are likely to play a role in the emerging area of biopattern and bioprofile analysis that will underpin personalized healthcare. We highlight their application to clinical problems involving EEG and fetal ECG and heart rate analysis, and issues that arise when they are applied to real world problems. The clinical problems include dementia assessment, drug administration and fetal monitoring. The potential role and challenges in the application of non-linear signal analysis of biopattern and bioprofile are highlighted within the context of a major EU project, BIOPATTERN.

Independent component extraction methods in biosignal processing.

The paper discusses methods for independent source identification within multiple channels electroencephalographical (EEG) signals recordings. The focus is to compare the independent component analysis (ICA) technique to a novel proposed method for individual components separation - the phase space method (PSM). Methods are suitable to be used for any multi-lead signal especially within biomedical signals processing area where independence is a key issue.

The use of Matlab for colour fuzzy representation of multichannel EEG short time spectra.

During the last years, a lot of EEG research efforts was directed to intelligent methods for automatic analysis of data from multichannel EEG recordings. However, all the applications reported were focused on specific single tasks like detection of one specific "event" in the EEG signal: spikes, sleep spindles, epileptic seizures, K complexes, alpha or other rhythms or even artefacts. The aim of this paper is to present a complex system being able to perform a representation of the dynamic changes in frequency components of each EEG channel. This representation uses colours as a powerful means to show the only one frequency range chosen from the shortest epoch of signal able to be processed with the conventional "Short Time Fast Fourier Transform" (S.T.F.F.T.) method.

Analysing the spectral properties of EEG ictal events with time-frequency transform.

Analysis of EEG events, as e.g. the epileptic seizures, is a challenge for a lot of research that has been carried out during the last five years. New methods are required to better analyse the epileptic transients occurring during seizures. This paper discusses a model for features extraction from EEG signal to determine a specific signature of the seizure (inter-ictal) and to detect it using an artificial neural network, or just to provide a better representation of the frequency changes to the clinician.