CAJ | 학술논문

目的针对体感诱发电位(SEP)的特征,设计基于径向基函数网络的复合自适应滤波器,实现体感诱发电位的快速提取.方法通过径向基函数网络的关键参数优化选择,对基于径向基函数网络的复合自适应滤波器与以自适应信号增强器和自适应噪声消除器为基础构造的复合自适应滤波器提取体感诱发电位的性能进行比较分析.结果仿真实验表明:基于径向基函数神经网络的复合自适应滤波器拟合出的SEP信号,在波形上基本与模板信号相似,并且比已有复合自适应滤波器拟合出的波形更为平滑.结论基于径向基函数网络的复合自适应滤波器新算法可实现从强噪声背景中快速提取体感诱发电位,能更快地识别体感诱发电位的潜伏期及幅值,实现单次提取,并且系统性能稳定.
목적 침대체감유발전위(SEP)적특정,설계기우경향기함수망락적복합자괄응려파기,실현체감유발전위적쾌속제취.방법통과경향기함수망락적관건삼수우화선택,대기우경향기함수망락적복합자괄응려파기여이자괄응신호증강기화자괄응조성소제기위기출구조적복합자괄응려파기제취체감유발전위적성능진행비교분석.결과방진실험표명:기우경향기함수신경망락적복합자괄응려파기의합출적SEP신호,재파형상기본여모판신호상사,병차비이유복합자괄응려파기의합출적파형경위평활.결론 기우경향기함수망락적복합자괄응려파기신산법가실현종강조성배경중쾌속제취체감유발전위,능경쾌지식별체감유발전위적잠복기급폭치,실현단차제취,병차계통성능은정.
Objective To design multi-adaptive filter based on radial basis function (MAF-RBF) for efficiently extracting somatosensory evoked potential (SEP) in real-time SEP monitoring.Methods With the optimization of important parameters that influence the performance of radial basis function neural network,the performance of extracting SEP was compared to that of a multi-adaptive filter (MAF),which developed from the combination of well-developed adaptive noise canceller and adaptive signal enhancer.Results In this simulation study,the outputs of MAF-RBF showed a similar waveform with SEP template signals,and a smoother waveform than the.output of MAF.Conclusion With appropriate parameter values,MAF-RBFNN is able to extract the latency and amplitude of SEP from the extremely noisy background rapidly and reliably without averaging.