[1]付美红,李楚敏,陈仲本. 早期高血压激光、电刺激疗法的信号自动识别研究[J].中国医学物理学杂志,2018,35(10):1203-1209.[doi:DOI:10.3969/j.issn.1005-202X.2018.10.018]
　FU Meihong,LI Chumin,CHEN Zhongben. Automatic identification of the signal for early hypertension treatment with laser and electrical stimulations[J].Chinese Journal of Medical Physics,2018,35(10):1203-1209.[doi:DOI:10.3969/j.issn.1005-202X.2018.10.018]
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 早期高血压激光、电刺激疗法的信号自动识别研究()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
35卷
期数:
2018年第10期
页码:
1203-1209
栏目:
医学信号处理与医学仪器
出版日期:
2018-10-25

文章信息/Info

Title:
 Automatic identification of the signal for early hypertension treatment with laser and electrical stimulations
文章编号:
1005-202X（2018）10-1203-07
作者:
 付美红; 李楚敏; 陈仲本
 中山大学新华学院生物医学工程学院， 广东 广州 510520
Author(s):
 FU Meihong;  LI Chumin;  CHEN Zhongben
 School of Biomedical Engineering, Xinhua College of Sun Yat-sen University, Guangzhou 510520, China
关键词:
 ; 早期高血压; 光电刺激信号源; 音频信号; 模板库匹配; 自动识别
Keywords:
 ; Key words: early hypertension;  signal source of laser and electrical stimulations;  audio signal;  template library match;  automatic identification
分类号:
R318.5；TN912.3
DOI:
DOI:10.3969/j.issn.1005-202X.2018.10.018
文献标志码:
A
摘要:
 目的：针对早期高血压激光、电刺激治疗中需要多种模式刺激信号以解决患者个体化差异以及适应性的问题，提出了一种刺激信号的自动识别方法，为临床选择有效的治疗信号提供了方便、快捷的筛选手段。 方法：用已被临床试验证实有疗效的数字音频信号作为原始信号，提取该类信号的MFCC特征参数建立HMM模型库；将实验用的信号分组，有效信号识别后定出判别标准；对识别信号提取特征参数，利用维特比（Viterbi）算法计算识别信号与模型匹配程度，将识别信号与模板库中的信号自动匹配，根据匹配结果判断识别信号在模板库中所属的类别。 结果和结论：待识别信号采用HMM模型能有效地分类识别，并且可从大量的待识别信号中准确、快捷地分拣出识别信号与原始信号输出概率对数的相对值小的信号，待临床验证后用作早期高血压激光、电刺激疗法的信号源。
Abstract:
 Abstract: Objective Based on the situation that simulation signals of different models are needed to solve the problems of individual differences and adaptability in early hypertension treatment with laser and electrical simulations, to propose a method of automatically identifying the simulation signals for providing a convenient and efficient means to choose effective treatment signals in clinic. Methods The digital audio signals which have been clinically proven to be effective were taken as the original signals, and the Mel frequency cepstral coefficients of this kind of signal were extracted to establish the HMM model library. After grouping the experimental signals and identifying the effective signals, the criterion of automatic identification was determined. Then the characteristic parameters of the test signals were extracted, and the matching probability of the test signals and model was calculated by Viterbi algorithm, and the test signals were automatically matched with the signals in the template library. Finally, according to the matching results, the test signals were classified into different categories in template library. Results and Conclusion HMM model could be used to classified test signals effectively. The signals with small relative value of the output probability logarithm between test signals and original signals could be sorted out accurately and effectively from a large number of test signals. After the clinical validation, the selected signals can be used as the signal source for early hypertension treatment with laser and electrical simulations.

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备注/Memo

备注/Memo:
 【收稿日期】2018-07-16
【基金项目】广东省科技计划项目(B030801162)
【作者简介】付美红，研究方向：生物医学仪器，E-mail: 1520858943@-
qq.com
【通信作者】陈仲本，教授，硕士生导师，研究方向:数字化医学电子仪器，E-mail: chenzhb@mail.sysu.edu.cn
更新日期/Last Update: 2018-10-23