中国科学院深圳先进技术研究院机构知识库(SIAT OpenIR): EEG Detection Method Based on Wavelet Transform and SVM
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EEG Detection Method Based on Wavelet Transform and SVM
Chun Chen; Zhichao Liu; Hongna Li; Rong Zhou; Yong Zhang
2016
Conference NameThe IEEE International Conference on Smart Cloud (SmartCloud 2016)
Conference Place美国,纽约
AbstractEpilepsy refers to a set of chronic neurological syndromes characterized by transient and unexpected electrical disturbances of the brain. Scalp Electroencephalogram (EEG) is a common test that measures and records the electrical activity of the brain, and is widely used in the detection and analysis of epileptic seizures. However, it is often difficult to identify the subtle changes in the EEG waveform by visual inspection. Then, emerge in large numbers of research for biomedical engineers to develop and implement several intelligent algorithms for the identification of such subtle changes. This paper presents a EEG signal analysis and forecasting technique based on wavelet transform and support vector machine classification method. The main procedure is a dynamic circulation. The technique first train the given datasets, obtain the value of the parameter, then automatically multi-time decompose for a new person’s brain signals, predict whether the person has a characteristic wave of epilepsy, add the person’s EEG data into the SVM training model if the person has epilepsy abnormal signal, combined with abnormal data before retraining and learning.The method has a very large potential uses, such as application for the initial diagnosis of patients, improving the efficiency for doctors.
Department高性能中心
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.siat.ac.cn/handle/172644/10289
Collection数字所
Affiliation2016
Recommended Citation
GB/T 7714
Chun Chen,Zhichao Liu,Hongna Li,et al. EEG Detection Method Based on Wavelet Transform and SVM[C],2016.
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