EI / SCOPUS / CSCD 收录

中文核心期刊

CHEN Xi, WANG Yuanyuan, ZHANG Yu, WANG Weiqi. The ARMA model's pole characteristics of Doppler signals from the carotid artery and their classification application[J]. ACTA ACUSTICA, 2002, 27(6): 549-553. DOI: 10.15949/j.cnki.0371-0025.2002.06.014
Citation: CHEN Xi, WANG Yuanyuan, ZHANG Yu, WANG Weiqi. The ARMA model's pole characteristics of Doppler signals from the carotid artery and their classification application[J]. ACTA ACUSTICA, 2002, 27(6): 549-553. DOI: 10.15949/j.cnki.0371-0025.2002.06.014

The ARMA model's pole characteristics of Doppler signals from the carotid artery and their classification application

More Information
  • PACS: 
  • Received Date: April 17, 2001
  • Revised Date: May 22, 2001
  • Available Online: July 31, 2022
  • In order to diagnose the cerebral infarction, a classification system based on the ARMA model and BP neural network is presented to analyze blood flow Doppler signals from the carotid artery. In this system, an ARMA model was firstly used to analyze the audio Doppler blood flow signals from the carotid artery. Then several characteristic parameters of the pole's distribution were estimated. After studies of these characteristic parameters' sensitivity to the cerebral infarction diagnosis, a BP neural network using sensitive parameters was established to classify the normal or abnormal state of the cerebral vessel. With 474 cases used to establish the appropriate neural network, and 52 cases used to test the network, results showed that the correct classification rate of both training and testing were over 94%. Thus this system is useful to diagnose the cerebral infarction.
  • Related Articles

    [1]TAN Xiaofeng, LI Xihai, NIU Chao, ZENG Xiaoniu, LI Hongru, LIU Tianyou. Infrasound event classification with multi-channel multi-scale convolutional attention network[J]. ACTA ACUSTICA, 2025, 50(4): 892-898. DOI: 10.12395/0371-0025.2023286
    [2]LIANG Yinian, LI Jie, LONG Lirong, CHEN Fangjiong. Incoherently distributed sources localization using convolutional neural network[J]. ACTA ACUSTICA, 2024, 49(1): 38-48. DOI: 10.12395/0371-0025.2022138
    [3]DU Shuanping, CHEN Yuechao, LUO Zhaorui. Line spectrum extraction of underwater acoustic target using deep convolution network and adaptive enhancement learning[J]. ACTA ACUSTICA, 2023, 48(4): 699-714. DOI: 10.15949/j.cnki.0371-0025.2023.04.008
    [4]TANG Guichen, LIANG Ruiyu, KONG Fanliu, XIE Yue, JU Mengjie. A non-invasive speech quality evaluation algorithm combining auxiliary target learning and convolutional recurrent network[J]. ACTA ACUSTICA, 2022, 47(5): 692-702. DOI: 10.15949/j.cnki.0371-0025.2022.05.003
    [5]SUN Xingwei, LI Junfeng, YAN Yonghong. Speech dereverberation method with convolutional neural network and reverberation time attention[J]. ACTA ACUSTICA, 2021, 46(6): 1234-1241. DOI: 10.15949/j.cnki.0371-0025.2021.06.043
    [6]WANG Wenbo, SU Lin, JIA Yuqing, REN Qunyan, MA Li. Convolution neural network ranging method in the deep-sea direct-arrival zone[J]. ACTA ACUSTICA, 2021, 46(6): 1081-1092. DOI: 10.15949/j.cnki.0371-0025.2021.06.027
    [7]XUE Cheng, GONG Zaixiao, GU Yiming, WANG Yu, LIN Peng, LI Zhenglin. Channel matching of shallow water active detection combined with convolutional neural network[J]. ACTA ACUSTICA, 2021, 46(6): 800-812. DOI: 10.15949/j.cnki.0371-0025.2021.06.003
    [8]LIAN Hailun, ZHOU Jian, HU Yuting, ZHENG Wenming. Whisper to normal speech conversion using deep convolutional neural networks[J]. ACTA ACUSTICA, 2020, 45(1): 137-144. DOI: 10.15949/j.cnki.0371-0025.2020.01.017
    [9]LU Cheng, TIAN Meng, ZHOU Jian, WANG Huabin, TAO Liang. A single-channel speech enhancement approach using convolutive non-negative matrix factorization with L1/2 sparse constraint[J]. ACTA ACUSTICA, 2017, 42(3): 377-384. DOI: 10.15949/j.cnki.0371-0025.2017.03.016
    [10]LÜ Zhao, WU Xiaopei, ZHANG Chao, LI Mi. Robust speech features extraction in convolutional noise environment[J]. ACTA ACUSTICA, 2010, 35(4): 465-470. DOI: 10.15949/j.cnki.0371-0025.2010.04.013
  • Cited by

    Periodical cited type(3)

    1. 黄辉波,邵玉斌,龙华,杜庆治. 低信噪比下基于B-Wave-U-Net特征增强的音素识别. 北京邮电大学学报. 2025(01): 100-106 .
    2. 丁惜瀛,付直刚,马少华. 基于声纹识别的永磁同步电机运行状态监测. 沈阳工业大学学报. 2025(02): 145-151 .
    3. 解元,邹涛,孙为军,谢胜利. 基于混合混响模型的多通道语音增强算法. 通信学报. 2024(11): 15-26 .

    Other cited types(1)

Catalog

    Article Metrics

    Article views (21) PDF downloads (4) Cited by(4)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return