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PubMed · 7231229

Automated EEG analysis with microcomputers.

J R Smith
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Abstract

The application of microcomputers for the detection of individual waveforms in the electroencephalogram is described. The waveform detection, which is completely implemented in a microcomputer, consists of filtering the EEG, determining the time between zero-crossing of each wave, measuring the amplitude of each wave, and then determining if the individual waves occur with a specified pattern. The method provides for the detection and quantification of the waveforms normally occurring in the sleep EEG and in petit mal epilepsy. The same algorithm is used for all waveforms, except that the filter, wave, and criteria are changed. The microcomputer can also be used to obtain a quantitative description of the individual waveforms.

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ElectroencephalographyHumansMedical Laboratory ScienceMicrocomputersResearchSleepStatus EpilepticusJournal ArticleResearch Support, U.S. Gov't, P.H.S.

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J R Smith. Automated EEG analysis with microcomputers.. https://pubmed.ncbi.nlm.nih.gov/7231229/

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Improvement in the performance of automated spike detection using dipole source features for artefact rejection.

OBJECTIVE: We evaluated the use of an efficient dipole source algorithm to improve performance of automated spike detection by identifying false detections caused by artefacts. METHODS: Automated spike detections were acquired from 26 patients undergoing prolonged electroencephalograph (EEG) monitoring. Data from 6 patients were used to develop the method and data from 20 patients were used to test the method. To provide a standard against which to evaluate the results, an electroencephalographer (EEGer) visually categorized all automated detections before the dipole models were calculated for all events. The event categories (as defined by the EEGer) were then combined with properties of the dipole model and features were identified that differentiated spike and artefact detections. The resulting method was then applied to the testing data set. RESULTS: Residual variance and eccentricity of the dipole models differentiated artefact and spike detections. A separate set of rules defining eye blink artefact was also developed. The combined criteria removed a mean of 53.2% of artefact from the testing data set. Some spike detections (4.3%) were also lost. CONCLUSIONS: The features of the dipole source of a detected event can be used to differentiate artefacts from spikes. This algorithm is computationally light and could be implemented on-line.

Electroencephalography↗