Multi-Feature Fusion Method Based on EEG Signal and its Application in Stroke Classification
| Autor: | Wenhui Jia, Hui Haisheng, Fengyun Hu, Fan Yuzhou, Can Wang, Xueying Zhang, Fenglian Li |
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| Rok vydání: | 2019 |
| Předmět: |
020205 medical informatics
Computer science Quantitative Biology::Tissues and Organs Decision tree Wavelet Analysis Medicine (miscellaneous) Health Informatics 02 engineering and technology Electroencephalography Approximate entropy Pattern Recognition Automated Wavelet Health Information Management Fuzzy Logic 0202 electrical engineering electronic engineering information engineering medicine Humans Entropy (energy dispersal) Quantitative Biology::Neurons and Cognition medicine.diagnostic_test business.industry Pattern recognition Signal Processing Computer-Assisted Random forest Sample entropy Support vector machine Stroke ComputingMethodologies_PATTERNRECOGNITION Artificial intelligence business Algorithms Information Systems |
| Zdroj: | Journal of medical systems. 44(2) |
| ISSN: | 1573-689X |
| Popis: | Electroencephalogram (EEG) analysis has been widely used in the diagnosis of stroke diseases for its low cost and noninvasive characteristics. In order to classify the EEG signals of stroke patients with cerebral infarction and cerebral hemorrhage, this paper proposes a novel EEG stroke signal classification method. This method has two highlights. The first is that a multi-feature fusion method is given by combining wavelet packet energy, fuzzy entropy and hierarchical theory. The second highlight is that a suitable classification model based on ensemble classifier is constructed for perfectly classification stroke signals. Entropy is an accessible way to measure information and uncertainty of time series. Many entropy-based methods have been developed these years. By comparing with the performances of permutation entropy, sample entropy, approximate entropy in measuring the characteristic of stroke patient's EEG signals, it can be found that fuzzy entropy has best performance in characterization stroke EEG signal. By combining hierarchical theory, wavelet packet energy and fuzzy entropy, a multi-feature fusion method is proposed. The method first calculates wavelet packet energy of EEG stroke signal, then extracts hierarchical fuzzy entropy feature by combining hierarchical theory and fuzzy entropy. The experimental results show that, compared with the fuzzy entropy feature, the classification accuracy based on the fusion feature of wavelet packet energy and hierarchical fuzzy entropy is much higher than benchmark methods. It means that the proposed multi-feature fusion method based on stroke EEG signal is an efficient measure in classifying ischemic and hemorrhagic stroke. Support vector machine (SVM), decision tree and random forest are further used as the stroke signal classification models for classifying ischemic stroke and hemorrhagic stroke. Experimental results show that, based on the proposed multi-feature fusion method, the ensemble method of random forest can get the best classification performance in accuracy among three models. |
| Databáze: | OpenAIRE |
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