Improving an automatic arrhythmias recogniser based in ECG signals

被引：0
|
作者：
Corsino, Jorge [1 ]
Travieso, Carlos M. [1 ]
Alonso, Jesus B. [1 ]
Ferrer, Miguel A. [1 ]
机构：
[1] Univ Las Palmas Gran Canaria, Technol Ctrr Innovat Commun CeTIC, Las Palmas Gran Canaria 35017, Spain
来源：
BIOSIGNALS 2008: PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON BIO-INSPIRED SYSTEMS AND SIGNAL PROCESSING, VOL II | 2008年
关键词：
automatic recognition of arrhythmias; electrocardiography; neural network; principal component analysis; wavelet transform;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
In the present work, we have developed and improved a tool for the automatic arrhythmias detection, based on neural network with the "more-voted" algorithm. Arrhythmia Database MIT has been used in the work in order to detect eight different states, seven are pathologies and one is normal. The unions of different blocks and its optimization have found an improvement of success rates. In particular, we have used wavelet transform in order to characterize the patron wave of electrocardiogram (ECG), and principal components analysis in order to improve the discrimination of the coefficients. Finally, a neural network with more-voted method has been applied.
引用
收藏
页码：453 / 457
页数：5
相关论文
共 50 条
  • [31] COMPUTER AIDED DIAGNOSIS FOR CARDIOVASCULAR DISEASES BASED ON ECG SIGNALS: A SURVEY
    Faust, Oliver
    Ng, E. Y. K.
    JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY, 2016, 16 (01)
  • [32] An Effective Method for Distinguishing Sleep Apnea and Hypopnea Based on ECG Signals
    Wang, Yao
    Ji, Siyu
    Yang, Tianshun
    Wang, Xiaohong
    Wang, Huiquan
    Zhao, Xiaoyun
    Jinhai, Wang
    IEEE ACCESS, 2021, 9 : 67928 - 67941
  • [33] Reversible data hiding in ECG Signals Based on Histogram Shifting and Thresholding
    WenchaoWu
    Liu, Bin
    Zhang, Weiming
    Chen, Changwen
    2015 2nd International Symposium on Future Information and Communication Technologies for Ubiquitous HealthCare (Ubi-Health Tech), 2015,
  • [34] A review of automatic detection of epilepsy based on EEG signals
    Ren, Qirui
    Sun, Xiaofan
    Fu, Xiangqu
    Zhang, Shuaidi
    Yuan, Yiyang
    Wu, Hao
    Li, Xiaoran
    Wang, Xinghua
    Zhang, Feng
    JOURNAL OF SEMICONDUCTORS, 2023, 44 (12)
  • [35] ST-T complex automatic analysis of the electrocardiogram signals based on wavelet transform
    Li, XY
    Wang, T
    Zhou, P
    Feng, HQ
    PROCEEDINGS OF THE IEEE 29TH ANNUAL NORTHEAST BIOENGINEERING CONFERENCE, 2003, : 144 - 145
  • [36] ECG Identification of Arrhythmias by using an Associative Petri Net
    Shih, Dong-Her
    Chiang, Hsiu-Sen
    Shih, Ming-Hung
    2013 IEEE INTERNATIONAL CONFERENCE ON CLUSTER COMPUTING (CLUSTER), 2013,
  • [37] High-performance classification of STEMI and NSTEMI by automatic feature selection from ECG signals: a triple approach
    Latifoglu, Fatma
    Icer, Semra
    Guven, Aysegul
    Zhusupova, Aigul
    Avsarogullari, Omer Levent
    Kelesoglu, Saban
    Kalay, Nihat
    Orhanbulucu, Firat
    Ince, Merve
    Erturk, Aybike
    Ozdet, Berat
    SIGNAL IMAGE AND VIDEO PROCESSING, 2025, 19 (04)
  • [38] The Effect of Denoising on Classification of ECG Signals
    Alickovic, Emina
    Babic, Zdenka
    2015 XXV INTERNATIONAL CONFERENCE ON INFORMATION, COMMUNICATION AND AUTOMATION TECHNOLOGIES (ICAT), 2015,
  • [39] Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods
    Alajlan, Naif
    Bazi, Yakoub
    Melgani, Farid
    Malek, Salim
    Bencherif, Mohamed A.
    SIGNAL IMAGE AND VIDEO PROCESSING, 2014, 8 (05) : 931 - 942
  • [40] Wavelet analysis of ECG and MAP signals
    Tikkanen, PE
    Palmgren, JE
    Sellin, LC
    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 : 317 - 320
12345