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
  • [1] Real-time Pedestrian Detection Using a Support Vector Machine and Stixel Information
    Mi Thi-Tra Nguyen
    Vinh Dinh Nguyen
    Jeon, Jae Wook
    2017 17TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS), 2017, : 1350 - 1355
  • [2] Electromyography signal analysis with real-time support vector machine
    Murshid, Mohammad Manzur
    Salehi, Hassan S.
    SIGNAL PROCESSING, SENSOR/INFORMATION FUSION, AND TARGET RECOGNITION XXIX, 2020, 11423
  • [3] Detection of ventricular fibrillation by support vector machine algorithm
    Li, Qun
    Zhao, Jie
    Zhao, Yan-Na
    2009 INTERNATIONAL ASIA CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION, AND ROBOTICS, PROCEEDINGS, 2009, : 287 - 290
  • [4] A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine
    Hao Zhang
    Yongdan Li
    Zhihan Lv
    Arun Kumar Sangaiah
    Tao Huang
    IEEE/CAA Journal of Automatica Sinica, 2020, 7 (03) : 790 - 799
  • [5] Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques
    Dardas, Nasser H.
    Georganas, Nicolas D.
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2011, 60 (11) : 3592 - 3607
  • [6] Real-time seismic structural response prediction system based on support vector machine
    Lin, Kuang Yi
    Lin, Tzu Kang
    Lin, Yo
    EARTHQUAKES AND STRUCTURES, 2020, 18 (02) : 163 - 170
  • [7] Real-time detection of driver cognitive distraction using support vector machines
    Liang, Yulan
    Reyes, Michelle L.
    Lee, John D.
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2007, 8 (02) : 340 - 350
  • [8] Real-time Multimedia Quality Assessment Based on Media Delay Index and Support Vector Machine
    Rao, Wenbi
    Li, Yingshu
    PROCEEDINGS OF THE 2009 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND NATURAL COMPUTING, VOL I, 2009, : 111 - 114
  • [9] A multi-class support vector machine real-time detection system for surface damage of conveyor belts based on visual saliency
    Hao, Xiao-li
    Liang, Huan
    MEASUREMENT, 2019, 146 : 125 - 132
  • [10] Electrocardiographic Signals and Swarm-Based Support Vector Machine for Hypoglycemia Detection
    Nuryani, Nuryani
    Ling, Steve S. H.
    Nguyen, H. T.
    ANNALS OF BIOMEDICAL ENGINEERING, 2012, 40 (04) : 934 - 945
12345