一种基于波动向量分级技术的病变数据快速分析方法

doi:10.16180/j.cnki.issn1007-7820.2022.07.001

Abstract

Abstract:

In large-scale time series data analysis, traditional mathematical statistics and analysis techniques have problems such as time-consuming, low accuracy, and weak anti-interference ability. In view of the deficiency, based on the wave vector grading technology, this study presents a fast analysis method for the time series data of lesions. Based on the TSTKS mutation point detection algorithm and sliding window theory, this method uses multi-threshold segmentation technology to realize the multi-level classification strategy of fluctuation vectors, and then realizes the state analysis and rapid diagnosis of large-scale lesion time series data. The result of simulation experiments and brain epilepsy lesion signal analysis show that the proposed method has the advantages of faster speed and higher efficiency, and can provide a new method for the rapid analysis and research of large-scale time series data.

Key words: time series data, data analysis, TSTKS algorithm, mutation point detection, sliding window theory, fluctuation vector, threshold segmentation, multi-level classification

CLC Number:

TP311

KONG Fanshu,QI Jinpeng,GONG Hanxin,ZHU Junjun,CAO Yitong. A Fast Analysis Method of Pathological Data Based on Wave Vector Grading Technology[J].Electronic Science and Technology, 2022, 35(7): 1-6.

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A Fast Analysis Method of Pathological Data Based on Wave Vector Grading Technology

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