A novel wavelet-based index to detect epileptic seizures using scalp EEG signals.

In this paper, we propose a novel wavelet-based algorithm for the detection of epileptic seizures. The algorithm is based on the recognition of rhythmic activities associated with ictal states in surface EEG recordings. Using a moving-window analysis, we first decomposed each EEG segment into a wavelet packet tree. Then, we extracted the coefficients corresponding to the frequency band of interest defined for rhythmic activities. Finally, a normalized index sensitive to both the rhythmicity and energy of the EEG signal was derived, based on the resulting coefficients. In our study, we evaluated this combined index for real-time detection of epileptic seizures using a dataset of approximately 11.5 hours of multichannel scalp EEG recordings from three patients and compared it to our previously proposed wavelet-based index. In this dataset, the novel combined index detected all epileptic seizures with a false detection rate of 0.52/hr.

Electroconvulsive therapy: a model for seizure detection by a wavelet packet algorithm.

Electroconvulsive therapy (ECT) is an effective treatment for severe depression. In this paper, we have used an algorithm based on wavelet packet (WP) analysis of EEG signals to detect seizures induced by ECT. After determining dominant frequency bands in the ictal period during ECT, the energy ratio of these bands was computed using the corresponding WP coefficients. This ratio was then used as an index to recognize seizure periods. Four different approaches to detect ECT seizures were employed in 41 EEG recordings from nine patients. Sensitivity in ECT seizure detection ranged from 76 to 95% while the false detection rate ranged from 6 to 13.

Optimizing fluctuation analysis of GABAAergic IPSCs for accurate unitary currents.

Nonstationary fluctuation analysis of synaptic currents requires division of currents into bins of time, with little agreement on how to select an optimal bin width. We used simulated inhibitory postsynaptic currents (simIPSCs) in an empirical approach to establish the optimal bin width needed for estimation of the unitary current, ie. We found acceptable accuracy (< or = 5%) at bin widths shorter than the length of the stationary segment of simIPSCs that persisted when Gaussian noise was added to the simulated currents. We also studied evoked and spontaneous IPSCs mediated by receptors for gamma-aminobutyrate (GABA) in thalamic neurons. Similar to simIPSCs, analysis of the IPSCs yielded saturating relationships between bin width and accuracy of unitary current estimate. Whereas standard error decreased, the accuracy of ie estimates increased with decreasing bin width, forming a plateau at bins below 2-3 ms in duration. Using this approach, one can reliably determine the optimal bin width for nonstationary noise analysis.

Seizure detection by a novel wavelet packet method.

We describe a novel wavelet-based method for the detection of seizure in patients with temporal lobe epilepsy. This method uses local discriminant bases and cross- data entropy algorithms to identify nodes of a wavelet packet dictionary that best discriminate preictal from ictal EEG signals. The algorithms are based on relative entropy criterion as a measure of discrepancy between different classes of signals. The frequency bands associated with these nodes were in the range of interest for seizure events. After selecting two bands, we determined the ratio of energies at the level of wavelet packet chosen by the cross-data entropy algorithm. Preliminary results demonstrate that the wavelet packet energy ratio could serve as a robust criterion in seizure detection.