Person Specific Characteristic Analysis Time domain Techniques for ECG Signals

被引:0
|
作者
Rudresh, M. D. [1 ]
Jayanna, H. S. [2 ]
Sheela, Anitha K. [3 ]
机构
[1] KIT, Dept Elect & Commun Engn, Tiptur 572202, Karnataka, India
[2] SIT, Dept Informat Sci Engn, Tumkur, Karnataka, India
[3] JNTUH, Dept Elect & Commun Engn, Hyderabad, Telangana, India
关键词
Electrocardiogram (ECG); PRD; Person Identification; PQRST;
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
This paper research Work investigates the feasibility of using the electrocardiogram (ECG) as a new biometric for human identification and verification. It is well known that the shapes of the ECG waveforms of different person are different but it is unclear whether such differences can be used identify different individuals. In this work we demonstrated successfully that it is possible to identify a specific person from group of persons. In order to Prove the ECG signals can be used for the biometric purposes The 5 seconds ECG data of 5 individuals are taken and shown how the both sessions ECG data of every individuals are having unique characteristics, while they are different characteristics for different subjects. This can be proved by drawing time domain, frequency domain plots for ECG signals of 5 individuals of different sessions this results shown that ECG is used for Biometric. After proving this we implemented a Time domain methods for identifications for ECG signals for Different ECG complexes. Results show that PQRST, QRS wave, P wave has much person specific information than T Wave.
引用
收藏
页码:441 / 446
页数:6
相关论文
共 50 条
  • [31] Preprocessing and analysis of the ECG signals
    Zhu Jianmin
    Zhang Xiaolan
    Wang Zhongyu
    Wang Xiaoling
    SEVENTH INTERNATIONAL SYMPOSIUM ON INSTRUMENTATION AND CONTROL TECHNOLOGY: SENSORS AND INSTRUMENTS, COMPUTER SIMULATION, AND ARTIFICIAL INTELLIGENCE, 2008, 7127
  • [32] ANALYSIS OF ECG SIGNALS.
    Chandra Mouly, M.C.
    Raja Rao, C.
    1600, (64):
  • [33] Comprehensive Time-Frequency Analysis of Noisy ECG Signals A Review
    Praveena, Hirald Dwaraka
    Sudha, Katta
    Geetha, P.
    Venkatanaresh, M.
    CARDIOMETRY, 2022, (24): : 271 - 275
  • [34] TIME DOMAIN ANALYSIS OF MYOELECTRIC SIGNALS IN HANSENOLOGY.
    Emmanuel, D.S.
    Chitore, D.S.
    Mukhopadhyay, P.
    Journal of the Institution of Engineers (India), Part IDP: Interdisciplinary Panels, 1986, 67 (Part 1): : 7 - 12
  • [35] Time Domain Analysis of NB-IoT Signals
    Barellini, Andrea
    Bracci, Barbara
    Licitra, Gaetano
    Silvi, Alberto Maria
    APPLIED SCIENCES-BASEL, 2023, 13 (04):
  • [36] A Framework for Automatic Time-Domain Characteristic Parameters Extraction of Human Pulse Signals
    Pei-Yong Zhang
    Hui-Yan Wang
    EURASIP Journal on Advances in Signal Processing, 2008
  • [37] A framework for automatic time-domain characteristic parameters extraction of human pulse signals
    Zhang, Pei-Yong
    Wang, Hui-Yan
    EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2008, 2008 (1)
  • [38] An Approach for Human Identification Based on Time and Frequency Domain Features Extracted from ECG Signals
    Fattah, Shaikh Anowarul
    Jameel, Abu Shafin Mohammad Mahdee
    Goswami, Rajib
    Saha, Sudip Kumar
    Syed, Nitu
    Akter, Shakil
    Shahnaz, Celia
    2011 IEEE REGION 10 CONFERENCE TENCON 2011, 2011, : 259 - 263
  • [39] Frequency domain analysis of ECG signals using auto-associative neural networks
    Sethi, Atul
    Arora, Siddharth
    Ballaney, Abhishek
    2006 INTERNATIONAL CONFERENCE ON BIOMEDICAL AND PHARMACEUTICAL ENGINEERING, VOLS 1 AND 2, 2006, : 528 - +
  • [40] The Characteristic Analysis of Reciprocal Beat ECG
    Pan, Yunping
    Jing, Yan
    Zhu, Tao
    Guo, Sen
    Li, Shifeng
    Lv, Congmin
    Yang, Lihong
    Yuan, Shai
    Guo, Qi
    Li, Zhongjian
    CIRCULATION, 2010, 122 (02) : E117 - E118