Features of Hierarchical Fuzzy Entropy of Stroke Based on EEG Signal and Its Application in Stroke Classification
| Autor: | Fengyun Hu, Fan Yuzhou, Wenhui Jia, Xueying Zhang, Can Wang, Fenglian Li |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Quantitative Biology::Tissues and Organs Feature extraction 02 engineering and technology Electroencephalography Approximate entropy 03 medical and health sciences symbols.namesake 0302 clinical medicine Robustness (computer science) 0202 electrical engineering electronic engineering information engineering medicine Gaussian function Quantitative Biology::Neurons and Cognition medicine.diagnostic_test business.industry Pattern recognition Support vector machine Sample entropy ComputingMethodologies_PATTERNRECOGNITION symbols 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) 030217 neurology & neurosurgery |
| Zdroj: | BigDataService |
| DOI: | 10.1109/bigdataservice.2019.00050 |
| 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 electroencephalogram (EEG) signals of stroke patients with cerebral infarction and cerebral hemorrhage, this paper proposed a novel EEG stroke signal feature extraction method by combining fuzzy entropy and hierarchical theory. Fuzzy entropy not only took the advantages of sample entropy, but also had less dependence on the length of time series and possessed better robustness to noise signals. It measured the similarity of two vectors based on Gaussian function instead of Heaviside function, avoiding discontinuity problems of sample entropy and approximate entropy. Hierarchical theory efficiently took advantages of the approximation information in low-frequency and the detail information in high-frequency. This was benefit for capturing a wealth of dynamic information and retaining redundant components. Support vector machine (SVM) was further used as the stroke signal classification model for classifying ischemic stroke and hemorrhagic stroke. The experimental results showed that, compared with other benchmarks, the classification accuracy based on the features of hierarchical fuzzy entropy is much higher than those benchmarks methods. Compared with the features of fuzzy entropy without using hierarchical theory, the classifier based on the features of hierarchical fuzzy entropy gave a much more improvement in classification performance by increasing accuracy from 68.03% to 96.72%. It meant that the proposed EEG stroke signal hierarchical fuzzy entropy feature extraction method was an efficient measure in classifying ischemic and hemorrhagic stroke. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|