目的探讨颅脑结构性病变所致癫痫的伽玛刀(γ-刀)治疗方法,评价治疗效果及视频脑电图(VEEG)随访的意义。方法回顾性分析山东省戴庄医院伽玛刀治疗科和神经科2017-01—2021-06确诊的60例颅脑结构性病变所致癫痫患者的临床资料,根据是否...目的探讨颅脑结构性病变所致癫痫的伽玛刀(γ-刀)治疗方法,评价治疗效果及视频脑电图(VEEG)随访的意义。方法回顾性分析山东省戴庄医院伽玛刀治疗科和神经科2017-01—2021-06确诊的60例颅脑结构性病变所致癫痫患者的临床资料,根据是否采用γ-刀治疗将其分为单纯药物治疗组(对照组)和γ-刀联合药物治疗组(γ-刀组),每组30例,进行随访观察,分析γ-刀治疗的效果及随访VEEG检测结果。结果在1~2 a的随访期内,γ-刀组患者癫痫总改善率76.67%,对照组为36.67%,差异有统计学意义(P<0.05);γ-刀组EEG痫样放电检出率30.00%,对照组为66.67%;γ-刀组癫痫控制显效组EEG正常率为50%,明显高于有效组的11.11%及无效组的0,显效组EEG痫样波检出率(7.14%)明显低于有效组(22.22%)和无效组(85.71%)。γ-刀治疗后随访期内癫痫发作改善率分别为0.5~1 a 63.33%,>1~2 a 76.67%,>2~3 a 80.77%,3 a以上82.61%;VEEG痫样波检出率分别为0.5~1 a63.33%,>1~2 a 30.00%,>2~3 a 23.08%,3 a以上21.74%。术后并发放射性脑水肿3例(10.00%)。结论γ-刀治疗颅内结构性病变所致的难治性癫痫效果明确,VEEG有助于γ-刀治疗方案的设计、评估治疗效果和指导治疗后用药。展开更多
Sleep is an essential integrant in everyone’s daily life;therefore,it is an important but challenging problem to characterize sleep stages from electroencephalogram(EEG)signals.The network motif has been developed as...Sleep is an essential integrant in everyone’s daily life;therefore,it is an important but challenging problem to characterize sleep stages from electroencephalogram(EEG)signals.The network motif has been developed as a useful tool to investigate complex networks.In this study,we developed a multiplex visibility graph motif-based convolutional neural network(CNN)for characterizing sleep stages using EEG signals and then introduced the multiplex motif entropy as the quantitative index to distinguish the six sleep stages.The independent samples t-test shows that the multiplex motif entropy values have significant differences among the six sleep stages.Furthermore,we developed a CNN model and employed the multiplex motif sequence as the input of the model to classify the six sleep stages.Notably,the classification accuracy of the six-state stage detection was 85.27%.Results demonstrated the effectiveness of the multiplex motif in characterizing the dynamic features underlying different sleep stages,whereby they further provide an essential strategy for future sleep-stage detection research.展开更多
Emotion recognition is one of the most important research directions in the field of brain–computer interface(BCI).However,to conduct electroencephalogram(EEG)-based emotion recognition,there exist difficulties regar...Emotion recognition is one of the most important research directions in the field of brain–computer interface(BCI).However,to conduct electroencephalogram(EEG)-based emotion recognition,there exist difficulties regarding EEG signal processing;moreover,the performance of classification models in this regard is restricted.To counter these issues,the 2022 World Robot Contest successfully held an affective BCI competition,thus promoting the innovation of EEG-based emotion recognition.In this paper,we propose the Transformer-based ensemble(TBEM)deep learning model.TBEM comprises two models:a pure convolutional neural network(CNN)model and a cascaded CNN-Transformer hybrid model.The proposed model won the abovementioned affective BCI competition’s final championship in the 2022 World Robot Contest,demonstrating the effectiveness of the proposed TBEM deep learning model for EEG-based emotion recognition.展开更多
文摘目的探讨颅脑结构性病变所致癫痫的伽玛刀(γ-刀)治疗方法,评价治疗效果及视频脑电图(VEEG)随访的意义。方法回顾性分析山东省戴庄医院伽玛刀治疗科和神经科2017-01—2021-06确诊的60例颅脑结构性病变所致癫痫患者的临床资料,根据是否采用γ-刀治疗将其分为单纯药物治疗组(对照组)和γ-刀联合药物治疗组(γ-刀组),每组30例,进行随访观察,分析γ-刀治疗的效果及随访VEEG检测结果。结果在1~2 a的随访期内,γ-刀组患者癫痫总改善率76.67%,对照组为36.67%,差异有统计学意义(P<0.05);γ-刀组EEG痫样放电检出率30.00%,对照组为66.67%;γ-刀组癫痫控制显效组EEG正常率为50%,明显高于有效组的11.11%及无效组的0,显效组EEG痫样波检出率(7.14%)明显低于有效组(22.22%)和无效组(85.71%)。γ-刀治疗后随访期内癫痫发作改善率分别为0.5~1 a 63.33%,>1~2 a 76.67%,>2~3 a 80.77%,3 a以上82.61%;VEEG痫样波检出率分别为0.5~1 a63.33%,>1~2 a 30.00%,>2~3 a 23.08%,3 a以上21.74%。术后并发放射性脑水肿3例(10.00%)。结论γ-刀治疗颅内结构性病变所致的难治性癫痫效果明确,VEEG有助于γ-刀治疗方案的设计、评估治疗效果和指导治疗后用药。
文摘Sleep is an essential integrant in everyone’s daily life;therefore,it is an important but challenging problem to characterize sleep stages from electroencephalogram(EEG)signals.The network motif has been developed as a useful tool to investigate complex networks.In this study,we developed a multiplex visibility graph motif-based convolutional neural network(CNN)for characterizing sleep stages using EEG signals and then introduced the multiplex motif entropy as the quantitative index to distinguish the six sleep stages.The independent samples t-test shows that the multiplex motif entropy values have significant differences among the six sleep stages.Furthermore,we developed a CNN model and employed the multiplex motif sequence as the input of the model to classify the six sleep stages.Notably,the classification accuracy of the six-state stage detection was 85.27%.Results demonstrated the effectiveness of the multiplex motif in characterizing the dynamic features underlying different sleep stages,whereby they further provide an essential strategy for future sleep-stage detection research.
基金National Key Research and Development Program of China“Biology and Information Fusion”Key Project(Grant No.2021YFF1200600)National Natural Science Foundation of China(Grant Nos.61906132 and 81925020)Key Project&Team Program of Tianjin City(Grant No.XC202020)。
文摘Emotion recognition is one of the most important research directions in the field of brain–computer interface(BCI).However,to conduct electroencephalogram(EEG)-based emotion recognition,there exist difficulties regarding EEG signal processing;moreover,the performance of classification models in this regard is restricted.To counter these issues,the 2022 World Robot Contest successfully held an affective BCI competition,thus promoting the innovation of EEG-based emotion recognition.In this paper,we propose the Transformer-based ensemble(TBEM)deep learning model.TBEM comprises two models:a pure convolutional neural network(CNN)model and a cascaded CNN-Transformer hybrid model.The proposed model won the abovementioned affective BCI competition’s final championship in the 2022 World Robot Contest,demonstrating the effectiveness of the proposed TBEM deep learning model for EEG-based emotion recognition.
