Classification of Lactate Level Using Resting-State EEG Measurements
| Autor: | Adil Deniz Duru, Osman N. Ucan, Saad Abdulazeez Shaban |
|---|---|
| Přispěvatelé: | Shaban, Saad Abdulazeez, Ucan, Osman Nuri, Duru, Adil Deniz |
| Rok vydání: | 2021 |
| Předmět: |
Accuracy and precision
Article Subject 020205 medical informatics QH301-705.5 Computer science Feature extraction Fast Fourier transform Biomedical Engineering Medicine (miscellaneous) Bioengineering 02 engineering and technology Electroencephalography 03 medical and health sciences 0302 clinical medicine Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering medicine Biology (General) Brain–computer interface medicine.diagnostic_test business.industry Deep learning Pattern recognition ComputingMethodologies_PATTERNRECOGNITION Resting state eeg Artificial intelligence business TP248.13-248.65 030217 neurology & neurosurgery Research Article Biotechnology |
| Zdroj: | Applied Bionics and Biomechanics Applied Bionics and Biomechanics, Vol 2021 (2021) |
| ISSN: | 1754-2103 1176-2322 |
| Popis: | The electroencephalography (EEG) signals have been used widely for studying the brain neural information dynamics and behaviors along with the developing impact of using the machine and deep learning techniques. This work proposes a system based on the fast Fourier transform (FFT) as a feature extraction method for the classification of human brain resting-state electroencephalography (EEG) recorded signals. In the proposed system, the FFT method is applied on the resting-state EEG recordings and the corresponding band powers were calculated. The extracted relative power features are supplied to the classification methods (classifiers) as an input for the classification purpose as a measure of human tiredness through predicting lactate enzyme level, high or low. To validate the suggested method, we used an EEG dataset which has been recorded from a group of elite-level athletes consisting of two classes: not tired, the EEG signals were recorded during the resting-state task before performing acute exercise and tired, the EEG signals were recorded in the resting-state after performing an acute exercise. The performance of three different classifiers was evaluated with two performance measures, accuracy and precision values. The accuracy was achieved above 98% by the K-nearest neighbor (KNN) classifier. The findings of this study indicated that the feature extraction scheme has the ability to classify the analyzed EEG signals accurately and predict the level of lactate enzyme high or low. Many studying fields, like the Internet of Things (IoT) and the brain computer interface (BCI), can utilize the findings of the proposed system in many crucial decision-making applications. |
| Databáze: | OpenAIRE |
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