English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Feature extraction plays an important role in Electrocardiogram (ECG) Beatsclassification system. Compared to other popular methods, VQ method performswell in feature extraction from ECG with advantages of dimensionalityreduction. In VQ method, a set of dictionaries corresponding to segments of ECGbeats is trained, and VQ codes are used to represent each heartbeat. However,in practice, VQ codes optimized by k-means or k-means++ exist largequantization errors, which results in VQ codes for two heartbeats of the sametype being very different. So the essential differences between different typesof heartbeats cannot be representative well. On the other hand, VQ uses toomuch data during codebook construction, which limits the speed of dictionarylearning. In this paper, we propose a new method to improve the speed andaccuracy of VQ method. To reduce the computation of codebook construction, aset of sparse dictionaries corresponding to wave segments of ECG beats isconstructed. After initialized, sparse dictionaries are updated efficiently byFeature-sign and Lagrange dual algorithm. Based on those dictionaries, a set ofcodes can be computed to represent original ECG beats.Experimental results showthat features extracted from ECG by our method are more efficient andseparable. The accuracy of our method is higher than other methods with lesstime consumption of feature extraction

ECG Beats Fast Classification Base on Sparse Dictionaries