探讨右美托咪定在脑电双频指数(BIS)指导及全身麻醉下帕金森病脑深部电刺激术(DBS)中的应用效果。选取2018年12月至2020年5月中国医科大学附属盛京医院择期全身麻醉下DBS患者40例,随机分为右美托咪定组(D组)和对照组(C组),每组20例,术...探讨右美托咪定在脑电双频指数(BIS)指导及全身麻醉下帕金森病脑深部电刺激术(DBS)中的应用效果。选取2018年12月至2020年5月中国医科大学附属盛京医院择期全身麻醉下DBS患者40例,随机分为右美托咪定组(D组)和对照组(C组),每组20例,术中均采用微电极记录技术。麻醉诱导前10 min D组泵注右美托咪定(0.8μg/kg),C组泵注相同体积生理盐水。全身麻醉维持采用七氟醚复合丙泊酚及瑞芬。进行微电极记录时,调整全身麻醉药物用量使BIS达到75~80。记录患者麻醉前(T1)、全身麻醉插管后(T2)、麻醉减浅时(T3)、微电极记录出现典型丘脑底核信号时(T4)、微电极记录15 min时(T5)、麻醉加深时(T6)的平均动脉压(MAP)、心率(HR)、BIS,诱导出典型丘脑底核放电信号所用时间及围术期不良反应情况。结果显示,与同组T1时比较,D组T2~T4时HR明显减慢(P<0.05),C组T5、T6时MAP明显升高(P<0.05)。与C组相同时间点比较,D组T4、T5、T6时MAP明显降低(P<0.05),T2、T3、T4时HR明显减慢(P<0.05)。与C组比较,D组诱导细胞电位时间明显减少(P<0.05),不良反应发生率明显降低(P<0.05)。因此,术前输注右美托咪定(0.8μg/kg)可有效稳定血流动力学,不影响微电极记录神经核团放电信号质量以及结果。展开更多
Kernel function is the function which computes dot product in feature spaces. Both the SVMs and kernelPCA are kernel-based learning methods. In this paper, the SVMs and kernel PCA are used to tackle the face recogni-t...Kernel function is the function which computes dot product in feature spaces. Both the SVMs and kernelPCA are kernel-based learning methods. In this paper, the SVMs and kernel PCA are used to tackle the face recogni-tion problem. SVMs are classifiers which have demonstrated high generalization capabilities. Kernel PCA is a featureextraction technique which is proposed as a nonlinear extension of a PCA. We illustrate the potential of SVMs andkernel PCA on the Yale database and compare with a PCA based algorithm. The experiments indicate that SVMs andkernel PCA are superior to the PCA method.展开更多
Statistical learning theory(SLT) and support vector machine(SVM) are effective to solve problems of machine learning under the condition of finite samples. It is known that the performance of support vector machine is...Statistical learning theory(SLT) and support vector machine(SVM) are effective to solve problems of machine learning under the condition of finite samples. It is known that the performance of support vector machine is often better than that of some neural networks in pattern recognition,especially in high dimensional space, and they are well used in many domains for recognition. This paper at first introduces the basic theory of SLT and SVM,then points out the key problems of SVM and its research situation in recent years,and at last describes some applications of SVM in the field of pattern recognition.展开更多
文摘探讨右美托咪定在脑电双频指数(BIS)指导及全身麻醉下帕金森病脑深部电刺激术(DBS)中的应用效果。选取2018年12月至2020年5月中国医科大学附属盛京医院择期全身麻醉下DBS患者40例,随机分为右美托咪定组(D组)和对照组(C组),每组20例,术中均采用微电极记录技术。麻醉诱导前10 min D组泵注右美托咪定(0.8μg/kg),C组泵注相同体积生理盐水。全身麻醉维持采用七氟醚复合丙泊酚及瑞芬。进行微电极记录时,调整全身麻醉药物用量使BIS达到75~80。记录患者麻醉前(T1)、全身麻醉插管后(T2)、麻醉减浅时(T3)、微电极记录出现典型丘脑底核信号时(T4)、微电极记录15 min时(T5)、麻醉加深时(T6)的平均动脉压(MAP)、心率(HR)、BIS,诱导出典型丘脑底核放电信号所用时间及围术期不良反应情况。结果显示,与同组T1时比较,D组T2~T4时HR明显减慢(P<0.05),C组T5、T6时MAP明显升高(P<0.05)。与C组相同时间点比较,D组T4、T5、T6时MAP明显降低(P<0.05),T2、T3、T4时HR明显减慢(P<0.05)。与C组比较,D组诱导细胞电位时间明显减少(P<0.05),不良反应发生率明显降低(P<0.05)。因此,术前输注右美托咪定(0.8μg/kg)可有效稳定血流动力学,不影响微电极记录神经核团放电信号质量以及结果。
文摘Kernel function is the function which computes dot product in feature spaces. Both the SVMs and kernelPCA are kernel-based learning methods. In this paper, the SVMs and kernel PCA are used to tackle the face recogni-tion problem. SVMs are classifiers which have demonstrated high generalization capabilities. Kernel PCA is a featureextraction technique which is proposed as a nonlinear extension of a PCA. We illustrate the potential of SVMs andkernel PCA on the Yale database and compare with a PCA based algorithm. The experiments indicate that SVMs andkernel PCA are superior to the PCA method.
文摘Statistical learning theory(SLT) and support vector machine(SVM) are effective to solve problems of machine learning under the condition of finite samples. It is known that the performance of support vector machine is often better than that of some neural networks in pattern recognition,especially in high dimensional space, and they are well used in many domains for recognition. This paper at first introduces the basic theory of SLT and SVM,then points out the key problems of SVM and its research situation in recent years,and at last describes some applications of SVM in the field of pattern recognition.
