TY - JOUR
T1 - Dictionary learning for VQ feature extraction in ECG beats classification
AU - Liu, Tong
AU - Si, Yujuan
AU - Wen, Dunwei
AU - Zang, Mujun
AU - Lang, Liuqi
N1 - Publisher Copyright:
© 2016 Elsevier Ltd. All rights reserved.
PY - 2016/7/1
Y1 - 2016/7/1
N2 - 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.
AB - 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.
KW - Classification
KW - ECG beats
KW - Feature extraction
KW - Vector quantization
KW - k-means++
KW - k-medoids
UR - http://www.scopus.com/inward/record.url?scp=84958172351&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2016.01.031
DO - 10.1016/j.eswa.2016.01.031
M3 - Journal Article
AN - SCOPUS:84958172351
SN - 0957-4174
VL - 53
SP - 129
EP - 137
JO - Expert Systems with Applications
JF - Expert Systems with Applications
ER -