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

Electrocardiogram (ECG) signal is a common and powerful tool to study heartfunction and diagnose several abnormal arrhythmias. While there have beenremarkable improvements in cardiac arrhythmia classification methods, theystill cannot offer acceptable performance in detecting different heartconditions, especially when dealing with imbalanced datasets. In this paper, wepropose a solution to address this limitation of current classificationapproaches by developing an automatic heartbeat classification method usingdeep convolutional neural networks and sequence to sequence models. Weevaluated the proposed method on the MIT-BIH arrhythmia database, consideringthe intra-patient and inter-patient paradigms, and the AAMI EC57 standard. Theevaluation results for both paradigms show that our method achieves the bestperformance in the literature (a positive predictive value of 96.46% andsensitivity of 100% for the category S, and a positive predictive value of98.68% and sensitivity of 97.40% for the category F for the intra-patientscheme; a positive predictive value of 92.57% and sensitivity of 88.94% for thecategory S, and a positive predictive value of 99.50% and sensitivity of 99.94%for the category V for the inter-patient scheme.). The source code is availableat https://github.com/SajadMo/ECG-Heartbeat-Classification-seq2seq-model.

Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach