Heart Blocks Detection in ECG Signals Using Time Frequency Distribution Techniques

被引：0
|
作者：
Azarnia, Ghanbar [1 ]
Tinati, Mohammad Ali [1 ]
Afkhami, Rashid Ghorbani [1 ]
机构：
[1] Univ Tabriz, Fac Elect & Comp Engn, Tabriz, Iran
来源：
2016 24TH IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE) | 2016年
关键词：
electrocardiogram signal; heart blocks; time-frequency distribution; time-frequency resolution; Choi-Williams distribution;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The electrical conduction system of the heart has an elaborate structure. A small failure in this system can put the person's life in danger. In people with heart block disease the electrical signal that controls the heartbeat is partially or completely blocked from reaching the ventricles. Electrocardiograms are used in detection of heart blocks and thus in this paper we have used it for the assessment of heart block cases. In this paper we propose a noninvasive diagnostic method based on the Choi Williams distribution to differentiate between normal and heart block subjects. The proposed procedure in addition to detection of heart blocks provides additional information for better characterization of ECG parameters for heart block subjects. Simulation results show superior performance of our proposed algorithm with respect to the others.
引用
收藏
页码：1568 / 1573
页数：6
相关论文
共 50 条
  • [1] Detection of heart blocks in ECG signals by spectrum and time-frequency analysis
    Saad, Norhashimah Mohd
    Abdullah, Abdul Rahim
    Low, Yin Fen
    2006 4TH STUDENT CONFERENCE ON RESEARCH AND DEVELOPMENT, 2006, : 61 - +
  • [2] The analysis of ECG signals using time-frequency techniques
    Tagluk, ME
    English, MJ
    PROCEEDINGS OF THE 19TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOL 19, PTS 1-6: MAGNIFICENT MILESTONES AND EMERGING OPPORTUNITIES IN MEDICAL ENGINEERING, 1997, 19 : 1320 - 1323
  • [3] ECG arrhythmia classification using time frequency distribution techniques
    Sultan Qurraie S.
    Ghorbani Afkhami R.
    Biomedical Engineering Letters, 2017, 7 (4) : 325 - 332
  • [4] Abnormal ECG Signals Analysis Using Non-Parametric Time–Frequency Techniques
    A. Dliou
    R. Latif
    M. Laaboubi
    F. M. R. Maoulainine
    Arabian Journal for Science and Engineering, 2014, 39 : 913 - 921
  • [5] Abnormal ECG Signals Analysis Using Non-Parametric Time-Frequency Techniques
    Dliou, A.
    Latif, R.
    Laaboubi, M.
    Maoulainine, F. M. R.
    ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2014, 39 (02) : 913 - 921
  • [6] Arrhythmia detection and recognition in ECG signals using nonlinear techniques
    No, Yan Sun, 2000, Am Inst Phys, Woodbury, NY, United States (28)
  • [7] Cardiac arrhythmia detection using deep learning approach and time frequency representation of ECG signals
    Yared Daniel Daydulo
    Bheema Lingaiah Thamineni
    Ahmed Ali Dawud
    BMC Medical Informatics and Decision Making, 23
  • [8] Cardiac arrhythmia detection using deep learning approach and time frequency representation of ECG signals
    Daydulo, Yared Daniel
    Thamineni, Bheema Lingaiah
    Dawud, Ahmed Ali
    BMC MEDICAL INFORMATICS AND DECISION MAKING, 2023, 23 (01)
  • [9] An efficient detection of congestive heart failure using frequency localized filter banks for the diagnosis with ECG signals
    Bhurane, Ankit A.
    Sharma, Manish
    San-Tan, Ru
    Acharya, U. Rajendra
    COGNITIVE SYSTEMS RESEARCH, 2019, 55 : 82 - 94
  • [10] Diagnostic application of a Universal Kernel to Time Frequency Energy Distribution of ECG signals
    Tagluk, ME
    English, MJ
    Vincent, R
    COMPUTERS IN CARDIOLOGY 1997, VOL 24, 1997, 24 : 395 - 398
12345