Dementia is a syndrome causing a progressive loss of brain function and mainly includes subtypes,such as Alzheimer’s Disease(AD),FrontoTemporal Dementia(FTD),and Vascular Cognitive Impairment(VCI).Electroencephalogra...Dementia is a syndrome causing a progressive loss of brain function and mainly includes subtypes,such as Alzheimer’s Disease(AD),FrontoTemporal Dementia(FTD),and Vascular Cognitive Impairment(VCI).Electroencephalography(EEG)is widely used in dementia diagnosis to detect brain electrophysiological signals efficiently.However,the small number of samples available in EEG-based dementia diagnosis results in poor performance of existing methods.To address this issue,we propose a Multi-scale Adaptive Graph Learning based on Multi-wave EEG data(MAGLM)for dementia diagnosis.Firstly,we extract both time-domain and frequency-domain features of multi-wave EEG data.Secondly,to reliably expand the insufficient samples,we propose a multi-wave EEG data augmentation model based on generative learning.Finally,to explore the rich patterns between scales,waves,and samples,we propose a multi-scale adaptive graph learning model to perform dementia diagnosis based on augmented EEG data.MAGLM is validated on an in-house EEG dataset,including AD,FTD,and VCI.The experimental and visualization results show the superiority of the proposed MAGLM over the state-of-the-art methods.In conclusion,MAGLM is not only effective in dementia diagnosis,but also provides experience for EEG-based brain science research.展开更多
基金supported by the National Key R&D Program of China(No.2023YFC3603700)the National Natural Science Foundation of China(No.62172444)+3 种基金the Shenzhen Science and Technology Program(No.KQTD20200820113106007)the Xinjiang Key Research and Development project(No.2023B01032)the Central South University Innovation-Driven Research Programme(No.2023CXQD018)the High Performance Computing Center of Central South University,China.
文摘Dementia is a syndrome causing a progressive loss of brain function and mainly includes subtypes,such as Alzheimer’s Disease(AD),FrontoTemporal Dementia(FTD),and Vascular Cognitive Impairment(VCI).Electroencephalography(EEG)is widely used in dementia diagnosis to detect brain electrophysiological signals efficiently.However,the small number of samples available in EEG-based dementia diagnosis results in poor performance of existing methods.To address this issue,we propose a Multi-scale Adaptive Graph Learning based on Multi-wave EEG data(MAGLM)for dementia diagnosis.Firstly,we extract both time-domain and frequency-domain features of multi-wave EEG data.Secondly,to reliably expand the insufficient samples,we propose a multi-wave EEG data augmentation model based on generative learning.Finally,to explore the rich patterns between scales,waves,and samples,we propose a multi-scale adaptive graph learning model to perform dementia diagnosis based on augmented EEG data.MAGLM is validated on an in-house EEG dataset,including AD,FTD,and VCI.The experimental and visualization results show the superiority of the proposed MAGLM over the state-of-the-art methods.In conclusion,MAGLM is not only effective in dementia diagnosis,but also provides experience for EEG-based brain science research.
