Support Vector Machine Algorithm for Real-Time Detection of VF Signals

被引:3
|
作者
Zhang, Chunyun [1 ]
Zhao, Jie [1 ]
Li, Fei [1 ]
Jia, Huilin [1 ]
Tian, Jie [1 ]
机构
[1] Shandong Normal Univ, Coll Phys & Elect, Jinan, Peoples R China
来源
2011 INTERNATIONAL CONFERENCE ON ENVIRONMENT SCIENCE AND BIOTECHNOLOGY (ICESB 2011) | 2011年 / 8卷
关键词
ventricular fibrillation (VF); electrocardiogram (ECG); Time-Delay algorithm; Support vector machine (SVM);
D O I
10.1016/j.proenv.2011.10.093
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
An algorithm for detecting ventricular fibrillation (VF) by the method of support vector machine is presented. The algorithm first extracts the feature of electrocardiogram in every 4s sliding window by the improved time delay method and the parameter d is obtained as feature; the support vector machine method is used to realize the discrimination of VF and non-VF signals. For evaluating the new algorithm, the complete BIH-MIT arrhythmia database and the CU database were used to simulate without any pre-selection. The sensitivity, specificity, positive predictability and accuracy were calculated and compared these values with results from an earlier investigation of several different ventricular fibrillation detection algorithms. It shows that the new algorithm has good performance and has greater advantages in real-time execution. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Asia-Pacific Chemical, Biological & Environmental Engineering Society (APCBEES)
引用
收藏
页码:602 / 608
页数:7
相关论文
共 50 条
  • [31] A memetic algorithm with support vector machine for feature selection and classification
    Nekkaa, Messaouda
    Boughaci, Dalila
    MEMETIC COMPUTING, 2015, 7 (01) : 59 - 73
  • [32] An Improved Algorithm for the Solution of the Regularization Path of Support Vector Machine
    Ong, Chong-Jin
    Shao, Shiyun
    Yang, Jianbo
    IEEE TRANSACTIONS ON NEURAL NETWORKS, 2010, 21 (03): : 451 - 462
  • [33] Chaotic antlion algorithm for parameter optimization of support vector machine
    Tharwat, Alaa
    Hassanien, Aboul Ella
    APPLIED INTELLIGENCE, 2018, 48 (03) : 670 - 686
  • [34] A memetic algorithm with support vector machine for feature selection and classification
    Messaouda Nekkaa
    Dalila Boughaci
    Memetic Computing, 2015, 7 : 59 - 73
  • [35] Chaotic antlion algorithm for parameter optimization of support vector machine
    Alaa Tharwat
    Aboul Ella Hassanien
    Applied Intelligence, 2018, 48 : 670 - 686
  • [36] Equatorial plasma bubble detection based on GNSS Doppler index using support vector machine algorithm
    Zhang, Fenkai
    Chen, Wu
    Xu, Fan
    Zou, Fang
    Deng, Yuanfan
    Tang, Long
    GPS SOLUTIONS, 2025, 29 (02)
  • [37] CHART: Intelligent Crime Hotspot Detection and Real-Time Tracking Using Machine Learning
    Ahmad, Rashid
    Nawaz, Asif
    Mustafa, Ghulam
    Ali, Tariq
    Tlija, Mehdi
    El-Meligy, Mohammed A.
    Ahmed, Zohair
    CMC-COMPUTERS MATERIALS & CONTINUA, 2024, 81 (03): : 4171 - 4194
  • [38] Comparison of Genetic Algorithm Optimization on Artificial Neural Network and Support Vector Machine in Intrusion Detection System
    Dastanpour, Amin
    Ibrahim, Suhaimi
    Mashinchi, Reza
    Selamat, Ali
    2014 IEEE CONFERENCE ON OPEN SYSTEMS (ICOS), 2014, : 72 - 77
  • [39] A Multiwavelet Support Vector Machine Prediction Algorithm for Avionics PHM
    Zhou, Xinzhou
    Xiang, Zheng
    Liu, Meng
    Xiang, Jiang
    INTELLIGENT COMPUTING THEORIES, 2013, 7995 : 295 - 304
  • [40] A real-valued genetic algorithm to optimize the parameters of support vector machine for classification of multiple faults in NPP
    Amer, Fathy Z.
    El-Garhy, Ahmed M.
    Awadalla, Medhat H.
    Rashad, Samia M.
    Abdien, Asmaa K.
    NUKLEONIKA, 2011, 56 (04) : 323 - 332