Seppo Lammi

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This study concerns the detection of epileptic seizures from electroencephalogram (EEG) data using computational methods. Using short sliding time windows, a set of features is computed from the data. The feature set includes time domain, frequency domain and nonlinear features. Discriminant analysis is used to determine the best seizure-detecting features(More)
We evaluated parameters for an expert system which will be designed to aid the differential diagnosis of female urinary incontinence by using knowledge discovered from data. To allow the statistical analysis, we applied means, regression and Expectation-Maximization (EM) imputation methods to fill in missing values. In addition, complete-case analysis was(More)
The usefulness of imputation in the treatment of missing values of an otoneurologic database for the discriminant analysis was evaluated on the basis of the agreement of imputed values and the analysis results. The data consisted of six patient groups with vertigo (N=564). There were 38 variables and 11% of the data was missing. Missing values were filled(More)
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