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

Monitoring electrocardiogram signals is of great significance for thediagnosis of arrhythmias. In recent years, deep learning and convolutionalneural networks have been widely used in the classification of cardiacarrhythmias. However, the existing neural network applied to ECG signaldetection usually requires a lot of computing resources, which is not friendlyFto resource-constrained equipment, and it is difficult to realize real-timemonitoring. In this paper, a binarized convolutional neural network suitablefor ECG monitoring is proposed, which is hardware-friendly and more suitablefor use in resource-constrained wearable devices. Targeting the MIT-BIHarrhythmia database, the classifier based on this network reached an accuracyof 95.67% in the five-class test. Compared with the proposed baselinefull-precision network with an accuracy of 96.45%, it is only 0.78% lower.Importantly, it achieves 12.65 times the computing speedup, 24.8 times thestorage compression ratio, and only requires a quarter of the memory overhead.

Arrhythmia Classifier using Binarized Convolutional Neural Network for Resource-Constrained Devices