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Objectives: The purpose of this study was to evaluate a structured questionnaire for improving a medical students' ability to identify, describe and interpret a witnessed seizure. Methods: Ninety two 3rd year medical students, blinded to seizure diagnosis, viewed videos of a primary generalized seizure and a complex partial seizure. Students next completed an unstructured questionnaire that asked the students to describe the seizure video recordings. The students then completed a structured questionnaire that asked the student to respond to 17 questions regarding specific features occurring during the seizures. We determined the number and types of correct responses for each questionnaire. Results: Overall, the structured questionnaire was more effective in eliciting an average of 9.25 correct responses compared to the unstructured questionnaire eliciting an average of 5.30 correct responses (p < 0.001). Additionally, 10 of the 17 seizure features were identified more effectively with the structured questionnaire. Potentially confounding factors, prior knowledge of someone with epilepsy or a prior experience of viewing a seizure, did not predict the student's ability to correctly identify any of the 17 features. Conclusions: A structured questionnaire significantly improves a medical student's ability to provide an accurate clinical description of primary generalized and complex partial witnessed seizures. Our analysis identified the 10 specific features improved by using the structured questionnaire. [ABSTRACT FROM AUTHOR]

Background: The diagnosis of epilepsy primarily depends on description of the observed seizure. The aim of this study was to determine the reliability of witness' description among groups with different medical education. Methods: A group of 44 respondents (15 laymen, 15 medical students, and 14 doctors at neurology residency program) were shown video footages of focal epileptic seizure (ES) with secondary generalization and psychogenic non-epileptic seizure (PNES) of the same patient. The ability to describe ES and PNES characteristics, to estimate duration of seizures, and to detect of accurate seizure type was evaluated using a questionnaire. For the analysis of primary data obtained from questionnaires, we used descriptive statistical methods and methods for testing statistical hypotheses. Results: The sensitivity (Sn) and specificity (Sp) for accurate recognition of ES are different in the examined groups (laymen Sn = 53.3%, Sp = 33.3%; medical students Sn = 100%, Sp = 13.3%; neurology residents Sn = 100%, Sp = 71.4%). Evaluated duration of PNES and ES do not differ between examined groups. The impression that ES and PNES are distinct events is reciprocal for medical students and neurology residents, but not in laymen group. Neurology residents notice the essential characteristics of ES in high percentage. Conclusion: Accurate classification of the attacks is associated with the observers' level of medical knowledge. Witnesses with specific, neurological knowledge with higher probability, compared to the laity and medical students, differentiate ES from PNES. [ABSTRACT FROM AUTHOR]

Whereas the visual EEG-inspection of epileptic seizures draws the attention to the waxing and waning of specific graphoelements in multi-channel recordings, the domain of computerized EEG-analysis for epilepsy diagnosis is detection of transients (i.e., spikes) and the quantification of background activity (i.e., mapping procedures). We present an approach to identify relatively fast changes of background activity by use of an automatic classifier. This algorithm is independent of the occurrence of any specific single type of graphoelement. The EEG is segmentated into short epochs of 0.64 sec duration each. For every segment a set of parameters (Hjorth, spectral power in classical frequency bands) is extracted, which taken together build elements of a vector-space. The elements are clustered in an automatic and unsupervised manner by use of a cosine-classifier, such that every EEG-epoch belongs to one class. Changes of brain activity as seen with the EEG are marked by transitions from one class to another. The class occurrence density is defined as the number of different classes that occur within a pre-defined number of EEG-epochs. It gives a new measure of variability of the EEG-signal. Comparing the epochs when class transitions take place in different channels, the class transitions coincidence between two channels is a measure of functional coupling of brain areas. [ABSTRACT FROM AUTHOR]