重庆邮电大学学报（自然科学版）2024，Vol.36Issue(4)：675-686,12.DOI:10.3979/j.issn.1673-825X.202308290286
基于时延嵌入式隐马尔科夫模型的癫痫脑电分类算法
EEG classification algorithm for epilepsy based on time-delayed embedded hidden Markov model
摘要
Abstract
Electroencephalogram(EEG)classification for epilepsy can offer powerful technical assistance for both its early warning and progression monitoring.However,traditional recognition methods for epilepsy EEG need to extract features from long-term time series,which cannot characterize the transient changes of brain and result in lower efficiency for epilepsy recognition and higher time consumption.These shortages further restrict the effectiveness of early warning for epilepsy.To address these problems,we proposed a novel epilepsy classification method based on hidden Markov model(HMM),which adopted the time-delay embedded HMM(TDE-HMM)to extract features of state transformation from estimated state series and utilized multiple layer perceptron(MLP)to further identify different seizure stages.The experimental results proved that compared with discrete wavelet transformation,power spectral density and differential entropy,our proposed method holds higher classification and capability of characterizing the state transformations of different seizure stages,which offers a novel alternative for the epilepsy classification and state analysis.关键词
癫痫检测/脑电信号(EEG)/时延嵌入式隐马尔科夫模型(TDE-HMM)/多层感知机(MLP)Key words
epilepsy recognition/electroencephalogram(EEG)/time-delay embedded HMM(TDE-HMM)/multiple layer perceptron(MLP)分类
信息技术与安全科学引用本文复制引用
李沛洋,赵贯一,刘宇轩,张伊诺,李存波,汪露,田银..基于时延嵌入式隐马尔科夫模型的癫痫脑电分类算法[J].重庆邮电大学学报（自然科学版）,2024,36(4):675-686,12.基金项目
国家自然科学基金项目(61901077,62171074) （61901077,62171074）
重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX1171)The National Natural Science Foundation of China(61901077,62171074) （CSTB2022NSCQ-MSX1171）
The Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX1171) （CSTB2022NSCQ-MSX1171）
