Quality Assessment of Ambulatory Electrocardiogram Signals by Noise Detection using Optimal Binary Classification

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Precision (positive predict value) has noticed for QAAES, SLND [42] & RUSBOOST [43]
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Published: Oct 31, 2022
DOI: https://doi.org/10.17762/ijritcc.v10i10.5739
Keywords:
Noise Detection, Optimal Binary Classification, ECG, IoT

Main Article Content

V. Supraja
Research Scholar, Department of ECE, JNTU College of Engineering, Anantapur, India
P. Nageswara Rao
Professor, Vardhaman College of Engineering, Hyderabad, India
M. N. Giriprasad
Professor, Department of ECE, JNTU College of Engineering, Anantapur, India

Abstract

In order to improve the diagnostic capability in Ambulatory Electrocardiogram signal and to reduce the noise signal impacts, there is a need for more robust models in place. In terms of improvising to the existing solutions, this article explores a novel binary classifier that learns from the features optimized by fusion of diversity assessment measures, which performs Quality Assessment of Ambulatory Electrocardiogram Signals (QAAES) by Noise Detection. The performance of the proposed model QAAES has been scaled by comparing it with contemporary models. Concerning performance analysis, the 10-fold cross-validation has been carried on a benchmark dataset. The results obtained from experiments carried on proposed and other contemporary models for cross-validation metrics have been compared to signify the sensitivity, specificity, and noise detection accuracy.

Article Details

How to Cite
Supraja, V., Rao, P. N. ., & Giriprasad, M. N. . (2022). Quality Assessment of Ambulatory Electrocardiogram Signals by Noise Detection using Optimal Binary Classification. International Journal on Recent and Innovation Trends in Computing and Communication, 10(10), 91–108. https://doi.org/10.17762/ijritcc.v10i10.5739
Issue
Vol. 10 No. 10 (2022): October (2022) Issue
Section
Articles

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