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Automated detection of the preseizure state in EEG signal using neural networks

Sudalaimani C., Sivakumaran N., Elizabeth Thomas T., Rominus Valsalam S.

Biocybernetics and Biomedical Engineering

2019

Vol. 39, no. 1

160--175

The life-threatening neural syndrome epilepsy is elicited by seizure which affects over 50 million people in the universal. A seizure is a brain condition made by excessive, unusual exoneration by nerve cells of the brain. Contemporary seizure forecast research works exhibited worthy results in both undersized and lengthy electroencephalography (EEG) signal; however it is essential to formulate superior epileptic seizure forecast system; that shall be steady, constant and less resource intensive for effectively employed to heading for evolving a convenient and easily manageable ictal or seizure forewarning prearrangement or devices. Based on our exploration, we have found a novel seizure prediction method which we evaluated by producing ten sub-frequency EEG data from initially recorded signal. Simple, robust and computationally less-intense EEG characteristics are mined using the generated sub-frequency signals and applied the extracted features to computationally less intense generalized regression neural network (GRNN) to segregate EEG signal clips into normal or preseizure files. In this research work, we have engendered 10 sub-frequency bands of signals from original EEG recordings, extracted various meaningful features from those sub-frequency band signals, created 10 GRNN neural networks to categorize feature files as normal or preseizure, and then applied post-processing techniques with 10 thresholding mechanisms to each classifier output. As such, we determined that seizure fore-warning may function better in various sub-frequency bands for many patients in a subject-specific manner. We also found that epileptic-seizure forecast performed superior at '60 Hz high pass' filtered sub-frequency band EEG signal for all subjects or canines data.

Artificial intelligence as a tool for enhancing work efficiency in transportation enterprises

Cisko Š., Tomáš K.

Logistyka

2009

nr 6

CD-CD

This article dealt with artificial intelligence. The neural networks application in economic sciences sprang out of two substantial facts. First of them was significant progress in exploring of this technology and its possible usage. Further significant impuls was triggered by rapid development of computing machinery and subsequent availability of sufficient computing performance it brought about to ordinary users)

Celem niniejszego artykułu jest przedstawienie aktualnego stanu zastosowań systemów elektronicznej wymiany informacji (EDI) w przedsiębiorstwach sektora TSL (Transport, Spedycja i Logistyka) oraz zasugerowanie przyszłych zastosowań w zakresie. Elementem składowym niniejszego artykułu są zaprezentowane wyniki ankiety internetowej wśród przedsiębiorstw sektora TSL dotyczące zastosowań systemów EDI w tych przedsiębiorstwach.