一种基于深度学习的时序病变数据段分类方法  

A Classification Method of Time Series Pathological Data Segments Based on Deep Learning

作　　者：袁傲 齐金鹏[1] 贾灿 薛宇鑫 郭阳阳 YUAN Ao;QI Jinpeng;JIA Can;XUE Yuxin;GUO Yangyang(College of Information Science&Technology,Donghua University,Shanghai 201620,China)

机构地区：[1]东华大学信息科学与技术学院,上海201620

出　　处：《电子科技》2024年第6期84-91,共8页Electronic Science and Technology

基　　金：国家自然科学基金(61305081,61104154);上海市自然科学基金(16ZR1401300,16ZR1401200)。

摘　　要：针对在大规模时序医疗数据的分析中现有检测方法检测精度低、检测速度慢等问题,文中提出了一种基于深度学习的时序病变数据段分类方法。该方法在TSTKS(Ternary Search Trees and modified Kolmogorov-Smirnov)算法和滑动窗口理论的基础上,利用深度学习技术实现了对病变数据段的快速准确分类。文中以利用该方法对病变数据段进行分类的结果作为依据,实现了滑动窗口大小的动态调整。通过对真实癫痫脑电信号(Electroencephalogram,EEG)进行分析,证明了所提病变数据段分类方法和基于该分类方法的滑动窗口动态调整机制具有检测速度快、精度较高等优点,可以为大规模时序数据的快速分析研究提供一种新选择。In view of the problems of low detection accuracy and slow detection speed in the analysis of large-scale time-series medical data,this study proposes a time-series pathological data segment method based on deep learning.On the basis of TSTKS(Ternary Search Trees and modified Kolmogorov-Smirnov)algorithm and sliding window theory,this method can realize fast and accurate classification of pathological data segments using deep learning technology.Based on the results of classification of pathological data segments by the proposed method,the dynamic adjustment of sliding window size is realized.Through the analysis of real epileptic EEG(Electroencephalogram)signals,the experimental results show that the proposed classification method of pathological data segment and the sliding window dynamic adjustment mechanism based on this classification method have the advantages of fast detection speed and high accuracy,which can provide a new choice for the rapid analysis of large-scale time series data.

关 键 词：大数据分析 时序数据 动态滑动窗口 多突变点检测 深度学习 癫痫脑电信号 BP神经网络 TSTKS算法 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]