Wyniki wyszukiwania - Biblioteka Nauki

Ten serwis zostanie wyłączony 2025-02-11.

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

2 Biocybernetics and Biomedical Engineering

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: generalized regression neural network

Sortuj według:

Ogranicz wyniki do:

An epileptic seizure detection system based on cepstral analysis and generalized regression neural network

100%

Yavuz E. , Kasapbaşı M. C. , Eyüpoğlu C. , Yazıcı R.

tom Vol. 38, no. 2

201--216

This study introduces a new and effective epileptic seizure detection system based on cepstral analysis utilizing generalized regression neural network for classifying electroen-cephalogram (EEG) recordings. The EEG recordings are obtained from an open database which has been widely studied with many different combinations of feature extraction and classification techniques. Cepstral analysis technique is mainly used for speech recognition, seismological problems, mechanical part tests, etc. Utility of cepstral analysis based features in EEG signal classification is explored in the paper. In the proposed study, mel frequency cepstral coefficients (MFCCs) are computed in the feature extraction stage and used in neural network based classification stage. MFCCs are calculated based on a frequency analysis depending on filter bank of approximately critical bandwidths. The experimental results have shown that the proposed method is superior to most of the previous studies using the same dataset in classification accuracy, sensitivity and specificity. This achieved success is the result of applying cepstral analysis technique to extract features. The system is promising to be used in real time seizure detection systems as the neural network adopted in the proposed method is inherently of non-iterative nature.

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

100%

Sudalaimani C. , Sivakumaran N. , Elizabeth T. T. , Rominus V. S.

Biocybernetics and Biomedical Engineering

2019

tom 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.