Introducing a novel approach of network oriented analysis of ERPs, demonstrated on adult attention deficit hyperactivity disorder.

OBJECTIVE: Introducing a network-oriented analysis method (brain network activation [BNA]) of event related potential (ERP) activities and evaluating its value in the identification and severity-grading of adult ADHD patients. METHODS: Spatio-temporal interrelations and synchronicity of multi-sited ERP activity peaks were extracted in a group of 13 ADHD patients and 13 control subjects for the No-go stimulus in a Go/No-go task. Participants were scored by cross-validation against the most discriminative ensuing group patterns and scores were correlated to neuropsychological evaluation scores. RESULTS: A distinct frontal-central-parietal pattern in the delta frequency range, dominant at the P3 latency, was unraveled in controls, while central activity in the theta and alpha frequency ranges predominated in the ADHD pattern, involving early ERP components (P1-N1-P2-N2). Cross-validation based on this analysis yielded 92% specificity and 84% sensitivity and individual scores correlated well with behavioral assessments. CONCLUSIONS: These results suggest that the ADHD group was more characterized by the process of exerting attention in the early monitoring stages of the No-go signal while the controls were more characterized by the process of inhibiting the response to that signal. SIGNIFICANCE: The BNA method may provide both diagnostic and drug development tools for use in diverse neurological disorders.

Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering.

Dynamic state recognition and event-prediction are fundamental tasks in biomedical signal processing. We present a new, electroencephalogram (EEG)-based, brain-state identification method which could form the basis for forecasting a generalized epileptic seizure. The method relies on the existence in the EEG of a preseizure state, with extractable unique features, a priori undefined. We exposed 25 rats to hyperbaric oxygen until the appearance of a generalized EEG seizure. EEG segments from the preexposure, early exposure, and the period up to and including the seizure were processed by the fast wavelet transform. Features extracted from the wavelet coefficients were imputed to the unsupervised optimal fuzzy clustering (UOFC) algorithm. The UOFC is useful for classifying similar discontinuous temporal patterns in the semistationary EEG to a set of clusters which may represent brain-states. The unsupervised selection of the number of cluster overcomes the a priori unknown and variable number of states. The usually vague brain state transitions are naturally treated by assigning each temporal pattern to one or more fuzzy clusters. The classification succeeded in identifying several, behavior-backed, EEG states such as sleep, resting, alert and active wakefulness, as well as the seizure. In 16 instances a preseizure state, lasting between 0.7 and 4 min was defined. Considerable individual variability in the number and characteristics of the clusters may postpone the realization of an early universal epilepsy warning. University may not be crucial if using a dynamic version of the UOFC which has been taught the individual's normal vocabulary of EEG states and can be expected to detect unspecified new states.