Dictionary learning for VQ feature extraction in ECG beats classification

Tong Liu, Yujuan Si, Dunwei Wen, Mujun Zang, Liuqi Lang

    Research output: Contribution to journalJournal Articlepeer-review

    38 Citations (Scopus)

    Abstract

    Vector quantization(VQ) can perform efficient feature extraction from electrocardiogram (ECG) with the advantages of dimensionality reduction and accuracy increase. However, the existing dictionary learning algorithms for vector quantization are sensitive to dirty data, which compromises the classification accuracy. To tackle the problem, we propose a novel dictionary learning algorithm that employs k-medoids cluster optimized by k-means++ and builds dictionaries by searching and using representative samples, which can avoid the interference of dirty data, and thus boost the classification performance of ECG systems based on vector quantization features. We apply our algorithm to vector quantization feature extraction for ECG beats classification, and compare it with popular features such as sampling point feature, fast Fourier transform feature, discrete wavelet transform feature, and with our previous beats vector quantization feature. The results show that the proposed method yields the highest accuracy and is capable of reducing the computational complexity of ECG beats classification system. The proposed dictionary learning algorithm provides more efficient encoding for ECG beats, and can improve ECG classification systems based on encoded feature.

    Original languageEnglish
    Pages (from-to)129-137
    Number of pages9
    JournalExpert Systems with Applications
    Volume53
    DOIs
    Publication statusPublished - 1 Jul. 2016

    Keywords

    • Classification
    • ECG beats
    • Feature extraction
    • k-means++
    • k-medoids
    • Vector quantization

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