ECG identification based on PCA-RPROP

Jinrun Yu, Yujuan Si, Xin Liu, Dunwei Wen, Tengfei Luo, Liuqi Lang

    Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

    6 Citations (Scopus)

    Abstract

    With the quick development of information technology, people pay more and more attention to information security and property safety, where identity is one of the most important aspects of information security. Compared with the traditional means of identification, biometrics recognition technology offers greater security and convenience. Among which, electrocardiogram (ECG) human identification has been attracted great attention in recent years. As a new type of biometric feature authentication technology, the feature selection and classification of ECG has become a focus of the research community. However, there exist some problems that can impair the efficiency and accuracy of ECG identification, including information redundancy and high dimensionality in feature extraction, and insufficient stability in classification. In order to solve the problems, in this paper, we propose a recognition method based on PCA-RPROP. In this method, firstly, only R points are located to get the original single-cycle waveforms. Then, PCA and whitening are used to process original data, where whitening is to make the input less redundant and PCA is to reduce its dimensionality. Finally, the resilient propagation (RPROP) algorithm is used to optimize the neural network and establish a complete recognition model. In order to evaluate the effectiveness of the algorithm, we compared the PCA feature with the wavelet decomposition and multi-point localization features in an ECG-ID database, and also compared RPROP with traditional BP algorithm, SVM and KNN. The experimental results show that this method can improve the performance compared with other classifiers, and simultaneously reduce the complexity of localization and the redundancy of features. It is superior to the other methods both speed and accuracy in recognition, especially when compared with the traditional BP. It can solve the problems of traditional BP with 2.4% higher recognition accuracy than LIBSVM, and 14 s faster than KNN in terms of time efficiency. Therefore, it is an efficient, simple and practical recognition algorithm.

    Original languageEnglish
    Title of host publicationDigital Human Modeling
    Subtitle of host publicationApplications in Health, Safety, Ergonomics, and Risk Management: Health and Safety - 8th International Conference, DHM 2017 Held as Part of HCI International 2017, Proceedings
    EditorsVincent G. Duffy
    Pages419-432
    Number of pages14
    DOIs
    Publication statusPublished - 2017
    Event8th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management, DHM 2017, held as part of 19th International Conference on Human-Computer Interaction, HCI 2017 - Vancouver, Canada
    Duration: 9 Jul. 201714 Jul. 2017

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10287 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference8th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management, DHM 2017, held as part of 19th International Conference on Human-Computer Interaction, HCI 2017
    Country/TerritoryCanada
    CityVancouver
    Period9/07/1714/07/17

    Keywords

    • ECG
    • Identity recognition
    • Neural network
    • PCA dimensionality reduction
    • RPROP
    • Whitening

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