Comparison of the distributions of classical and adaptively aligned EEG power spectra

The distributions of eyes-closed resting electroencephalography (EEG) power spectra and their residuals were described and compared using classically averaged and adaptively aligned averaged spectra. Four minutes of eyes-closed resting EEG was available from 69 participants. Spectra were calculated with 0.5-Hz resolution and were analyzed at this level. It was shown that power in the individual 0.5 Hz frequency bins can be considered normally distributed when as few as three or four 2-second epochs of EEG are used in the average. A similar result holds for the residuals. Power at the peak Alpha frequency has quite different statistical behaviour to power at other frequencies and it is considered that power at peak Alpha represents a relatively individuated process that is best measured through aligned averaging. Previous analyses of contrasts in upper and lower alpha bands may be explained in terms of the variability or distribution of the peak Alpha frequency itself.

The epileptology of William Aldren Turner

William Aldren Turner (1864-1945), in his day Physician to the National Hospital, Queen Square, and to King's College Hospital, London, was one of the major figures in the world of epileptology in the period between Hughlings Jackson in the latter part of the 19th century and the advent of electroencephalography in the 1930s. Although he also made contributions to knowledge in other areas of neurology, and with Grainger Stewart wrote a competent textbook on that subject, Turner's main professional interest throughout his career seems to have been epilepsy. On the basis of a series of earlier, rather heavily statistical, personal publications dealing with various aspects of the disorder, he authored what became a well-accepted monograph entitled Epilepsy-a study of the idiopathic disorder, which appeared in 1907, and he also gave the 1910 Morison lectures in Edinburgh on the topic. His writings on epilepsy over a period of three decades consolidated knowledge rather than led to significant advances, but helped maintain interest in the disorder during a rather long fallow phase in the development of the understanding of its nature. (C) 2005 Elsevier Ltd. All rights reserved.

Seizure detection algorithm for neonates based on wave-sequence analysis

Objective: The description and evaluation of the performance of a new real-time seizure detection algorithm in the newborn infant. Methods: The algorithm includes parallel fragmentation of EEG signal into waves; wave-feature extraction and averaging; elementary, preliminary and final detection. The algorithm detects EEG waves with heightened regularity, using wave intervals, amplitudes and shapes. The performance of the algorithm was assessed with the use of event-based and liberal and conservative time-based approaches and compared with the performance of Gotman's and Liu's algorithms. Results: The algorithm was assessed on multi-channel EEG records of 55 neonates including 17 with seizures. The algorithm showed sensitivities ranging 83-95% with positive predictive values (PPV) 48-77%. There were 2.0 false positive detections per hour. In comparison, Gotman's algorithm (with 30 s gap-closing procedure) displayed sensitivities of 45-88% and PPV 29-56%; with 7.4 false positives per hour and Liu's algorithm displayed sensitivities of 96-99%, and PPV 10-25%; with 15.7 false positives per hour. Conclusions: The wave-sequence analysis based algorithm displayed higher sensitivity, higher PPV and a substantially lower level of false positives than two previously published algorithms. Significance: The proposed algorithm provides a basis for major improvements in neonatal seizure detection and monitoring. Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology.