Comparative Study of Neural Network-Based Approaches for QRS Segmentation

Comparative Study of Neural Network-Based Approaches for QRS Segmentation

George Kolokolnikov, Anna Borde, Victor Skuratov, Roman Gaponov, Anastasiya Rumyantseva
Copyright: © 2020 |Pages: 24
DOI: 10.4018/IJERTCS.2020100105
OnDemand:
(Individual Articles)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

The paper is devoted to the development of QRS segmentation system based on deep learning approach. The considered segmentation problem plays an important role in the automatic analysis of heart rhythms, which makes it possible to identify life-threatening pathologies. The main goal of the research is to choose the best segmentation pipeline in terms of accuracy and time-efficiency. Process of ECG-signal analysis is described, and the problem of QRS segmentation is discussed. State-of-the-art algorithms are analyzed in literature review section and the most prominent are chosen for further research. In the course of the research, four hypotheses about appropriate deep learning model are checked: LSTM-based model, 2-input 1-dimensional CNN model, “signal-to-picture” approach based on 2-dimensional CNN, and the simplest 1-dimensional CNN model. All the architectures are tested, and their advantages and disadvantages are discussed. The proposed ECG segmentation pipeline is developed for Holter monitor software.
Article Preview
Top

Background

One of the most important tasks in automatic ECG signal analysis is detection of specific points: P, QRS and T waves, i.e. ECG segmentation. Specific points include onset, peak and offset of wave (Figure 1).

Figure 1.

Specific points of ECG signal wave

IJERTCS.2020100105.f01

Complete Article List

Search this Journal:
Reset
Volume 15: 1 Issue (2024): Forthcoming, Available for Pre-Order
Volume 14: 1 Issue (2023)
Volume 13: 4 Issues (2022): 1 Released, 3 Forthcoming
Volume 12: 4 Issues (2021)
Volume 11: 4 Issues (2020)
Volume 10: 4 Issues (2019)
Volume 9: 2 Issues (2018)
Volume 8: 2 Issues (2017)
Volume 7: 2 Issues (2016)
Volume 6: 2 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing