一种基于深度学习的时序病变数据段分类方法

doi:10.16180/j.cnki.issn1007-7820.2024.06.011

Abstract

Abstract:

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.

Key words: big data analysis, time series data, dynamic sliding window, multiple mutation point detection, deep learning, epileptic EEG, BP neural network, TSTKS algorithm

CLC Number:

TP311

YUAN Ao, QI Jinpeng, JIA Can, XUE Yuxin, GUO Yangyang. A Classification Method of Time Series Pathological Data Segments Based on Deep Learning[J].Electronic Science and Technology, 2024, 37(6): 84-91.

Figures/Tables 11

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References 20

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固定窗口大小	时耗/s	分类准确率/%
2⁶	0.253	90.7
2⁸	0.089	94.4
2¹⁰	0.026	91.7

固定窗口大小	时耗/s	分类准确率/%
2⁶	0.672	89.4
2⁸	0.244	98.8
2¹⁰	0.066	100.0

使用策略	时耗/s	命中率/%
动态滑动窗口	0.119	91.3
窗口长度为2¹¹的固定滑动窗口	0.225	83.2
窗口长度为2¹³的固定滑动窗口	0.057	65.2

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

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