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Background Epilepsy is a neurological disorder that is usually detected by electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a laborious and time-consuming process, lots of automatic epilepsy detection algorithms have been proposed. However, most of the available classification algorithms for epilepsy EEG signals adopted a single feature extraction, in turn to result in low classification accuracy. Although a small account of studies have carried out feature fusion, the computational efficiency is reduced due to too many features, because there are also some poor features that interfere with the classification results. Methods In order to solve the above problems, an automatic recognition method of epilepsy EEG signals based on feature fusion and selection is proposed in this paper. Firstly, the Approximate Entropy (ApEn), Fuzzy Entropy (FuzzyEn), Sample Entropy (SampEn), and Standard Deviation (STD) mixed features of the subband obtained by the Discrete Wavelet Transform (DWT) decomposition of EEG signals are extracted. Secondly, the random forest algorithm is used for feature selection. Finally, the Convolutional Neural Network (CNN) is used to classify epilepsy EEG signals. Results The empirical evaluation of the presented algorithm is performed on the benchmark Bonn EEG datasets and New Delhi datasets. In the interictal and ictal classification tasks of Bonn datasets, the proposed model achieves an accuracy of 99.9%, a sensitivity of 100%, a precision of 99.81%, and a specificity of 99.8%. For the interictal-ictal case of New Delhi datasets, the proposed model achieves a classification accuracy of 100%, a sensitivity of 100%, a specificity of 100%, and a precision of 100%. Conclusion The proposed model can effectively realize the high-precision automatic detection and classification of epilepsy EEG signals. This model can provide high-precision automatic detection capability for clinical epilepsy EEG detection. We hope to provide positive implications for the prediction of seizure EEG.

Geobacillus, a bacterial genus, is represented by over 25 species of Gram-positive isolates from various man-made and natural thermophilic areas around the world. An isolate of this genus (M-7) has been acquired from a thermal area near Yellowstone National Park, MT and partially characterized. The cells of this organism are globose (ca. 0.5 mu diameter), and they are covered in a matrix capsule which gives rise to elongate multicelled bacilliform structures (ranging from 3 to 12 mum) as seen by light and atomic force microscopy, respectively. The organism produces unique petal-shaped colonies (undulating margins) on nutrient agar, and it has an optimum pH of 7.0 and an optimum temperature range of 55-65 degrees C. The partial 16S rRNA sequence of this organism has 97% similarity with Geobacillus stearothermophilus, one of its closest relatives genetically. However, uniquely among all members of this genus, Geobacillus sp. (M-7) produces volatile organic substances (VOCs) that possess potent antibiotic activities. Some of the more notable components of the VOCs are benzaldehyde, acetic acid, butanal, 3-methyl-butanoic acid, 2-methyl-butanoic acid, propanoic acid, 2-methyl-, and benzeneacetaldehyde. An exposure of test organisms such as Aspergillus fumigatus, Botrytis cinerea, Verticillium dahliae, and Geotrichum candidum produced total inhibition of growth on a 48-h exposure to Geobacillus sp.(M-7) cells (ca.10(7)) and killing at a 72-h exposure at higher bacterial cell concentrations. A synthetic mixture of those available volatile compounds, at the ratios occurring in Geobacillus sp. (M-7), mimicked the bioactivity of this organism.