Next Article in Journal
The Role of Façade Materials in Blast-Resistant Buildings: An Evaluation Based on Fuzzy Delphi and Fuzzy EDAS
Next Article in Special Issue
Bicriteria Vehicle Routing Problem with Preferences and Timing Constraints in Home Health Care Services
Previous Article in Journal
Iterative Numerical Scheme for Non-Isothermal Two-Phase Flow in Heterogeneous Porous Media
Previous Article in Special Issue
Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration
Open AccessArticle

Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier

1
Intelligent System Research Group, Universitas Sriwijaya, Palembang 30137, Indonesia
2
Faculty of Computer Science, Universitas Sriwijaya, Palembang 30137, Indonesia
3
Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, BE 1410, Brunei
4
Mechanical Engineering Department, Faculty of Engineering, Diponegoro University, Jl. Prof. Soedharto SH, Tembalang, Semarang 50275, Indonesia
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(6), 118; https://doi.org/10.3390/a12060118
Received: 10 May 2019 / Revised: 2 June 2019 / Accepted: 3 June 2019 / Published: 7 June 2019
(This article belongs to the Special Issue Evolutionary Algorithms in Health Technologies)
  |  
PDF [3485 KB, uploaded 7 June 2019]
  |  

Abstract

The interpretation of Myocardial Infarction (MI) via electrocardiogram (ECG) signal is a challenging task. ECG signals’ morphological view show significant variation in different patients under different physical conditions. Several learning algorithms have been studied to interpret MI. However, the drawback of machine learning is the use of heuristic features with shallow feature learning architectures. To overcome this problem, a deep learning approach is used for learning features automatically, without conventional handcrafted features. This paper presents sequence modeling based on deep learning with recurrent network for ECG-rhythm signal classification. The recurrent network architecture such as a Recurrent Neural Network (RNN) is proposed to automatically interpret MI via ECG signal. The performance of the proposed method is compared to the other recurrent network classifiers such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The objective is to obtain the best sequence model for ECG signal processing. This paper also aims to study a proper data partitioning ratio for the training and testing sets of imbalanced data. The large imbalanced data are obtained from MI and healthy control of PhysioNet: The PTB Diagnostic ECG Database 15-lead ECG signals. According to the comparison result, the LSTM architecture shows better performance than standard RNN and GRU architecture with identical hyper-parameters. The LSTM architecture also shows better classification compared to standard recurrent networks and GRU with sensitivity, specificity, precision, F1-score, BACC, and MCC is 98.49%, 97.97%, 95.67%, 96.32%, 97.56%, and 95.32%, respectively. Apparently, deep learning with the LSTM technique is a potential method for classifying sequential data that implements time steps in the ECG signal. View Full-Text
Keywords: deep learning; gated recurrent unit; long short-term memory; myocardial infarction; recurrent neural network; sequence modeling deep learning; gated recurrent unit; long short-term memory; myocardial infarction; recurrent neural network; sequence modeling
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Darmawahyuni, A.; Nurmaini, S.; Sukemi; Caesarendra, W.; Bhayyu, V.; Rachmatullah, M.N.; Firdaus. Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier. Algorithms 2019, 12, 118.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top