Hand Tremor Recognition Using Bispectrum Analysis of Acceleration Signals and Support Vector Machine

AI Ling-mei; WANG Jue

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Hand Tremor Recognition Using Bispectrum Analysis of Acceleration Signals and Support Vector Machine

更新时间：2025-07-16

- Hand Tremor Recognition Using Bispectrum Analysis of Acceleration Signals and Support Vector Machine
- Acta Electronica Sinica Vol. 36, Issue 11, Pages: 2165-2170(2008)
- 作者机构：
  
  西安交通大学生物医学信息工程教育部重点实验室,陕西,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Published：2008
- 稿件说明：
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AI Ling-mei, WANG Jue. Hand Tremor Recognition Using Bispectrum Analysis of Acceleration Signals and Support Vector Machine[J]. Acta Electronica Sinica, 2008, 36(11): 2165-2170.
DOI：

AI Ling-mei, WANG Jue. Hand Tremor Recognition Using Bispectrum Analysis of Acceleration Signals and Support Vector Machine[J]. Acta Electronica Sinica, 2008, 36(11): 2165-2170. DOI：

摘要

针对目前临床上对特发性、帕金森病、生理性等3种常见震颤误诊断的问题

本文提出了一种基于双谱分析和支持向量机识别3种不同类型震颤的新方法.首先测量3种震颤类型志愿受试者手震颤的加速度信号并分别对其用Hinich方法检验

发现该类信号具有非高斯、非线性特性

然后用适合处理该类信号的双谱分析方法提取手震颤加速度信号的双谱对角切片的特征信息

最后采用"一对一"和"一对多"两种多分类的支持向量机算法对受试人的手震颤特征进行分类.交叉验证表明"一对一"算法的平均分类正确率高于"一对多"算法

分类正确率最高达到93.13%.该方法为临床医生提供了辅助识别不同类型震颤的新途径.

Abstract

With respect to three kinds of familiar tremor

including essential tremor

parkinsonism disease tremor and physiological tremor

which are subjected to frequent clinical misdiagnosis

a new recognition approach for tremor based on bispectrum analysis and support vector machine is proposed in this paper.At first

the acceleration signals of hand tremor from voluntary subjects were recorded and were tested by Hinich method respectively

we found that the signals possess the properties of non-gaussian and non-linearity.Then the features of diagonal slice of bispectrum which adapts to process non-gaussian and non-linearity signals of hand tremor accelerating signals were extracted.Finally

multiclassification support vector machine algorithm of "one against one" and "one against rest" are adopted to carry out recognition of three types tremor.Cross-validation test results show that the mean correct rate of classification with "one against one" algorithm is better than that of "one against rest"

its correct rate of classification can be readod at 93.13%

and provides a new a assistant approach to classify tremor for clinical neurosurgeon.

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