QQ客服 官方微博 反馈留言

	EEG Detection Method Based on Wavelet Transform and SVM
	Chun Chen; Zhichao Liu; Hongna Li; Rong Zhou; Yong Zhang
	2016
会议名称	The IEEE International Conference on Smart Cloud (SmartCloud 2016)
会议地点	美国，纽约
英文摘要	Epilepsy 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.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/10289]
专题	深圳先进技术研究院_数字所
作者单位	2016
推荐引用方式 GB/T 7714	Chun Chen,Zhichao Liu,Hongna Li,et al. EEG Detection Method Based on Wavelet Transform and SVM[C]. 见:The IEEE International Conference on Smart Cloud (SmartCloud 2016). 美国，纽约.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。