Next Article in Journal
Using the Characteristics of Pulse Waveform to Enhance the Accuracy of Blood Pressure Measurement by a Multi-Dimension Regression Model
Next Article in Special Issue
Efficient Real-Time R and QRS Detection Method Using a Pair of Derivative Filters and Max Filter for Portable ECG Device
Previous Article in Journal
Visualization of the Strain-Rate State of a Data Cloud: Analysis of the Temporal Change of an Urban Multivariate Description
Open AccessArticle

An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique

1
Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Jl. Raya Palembang-Prabumulih KM. 32, Indralaya 30662, Indonesia
2
Faculty of Medicine, Universitas Sriwijaya, Jl. Raya Palembang-Prabumulih KM. 32, Indralaya 30662, Indonesia
3
Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE 1410, Brunei Darussalam
4
Mechanical Engineering Department, Faculty of Engineering, Diponegoro University, Jl. Prof. Soedharto SH, Tembalang, Semarang 50275, Indonesia
5
Electrical Engineering Department, Politeknik Negeri Sriwijaya, Jalan Srijaya Negara, Palembang 30139, Indonesia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(14), 2921; https://doi.org/10.3390/app9142921
Received: 25 June 2019 / Revised: 17 July 2019 / Accepted: 18 July 2019 / Published: 22 July 2019
(This article belongs to the Special Issue ECG Signal and Its Applications)
An automated classification system based on a Deep Learning (DL) technique for Cardiac Disease (CD) monitoring and detection is proposed in this paper. The proposed DL architecture is divided into Deep Auto-Encoders (DAEs) as an unsupervised form of feature learning and Deep Neural Networks (DNNs) as a classifier. The objective of this study is to improve on the previous machine learning technique that consists of several data processing steps such as feature extraction and feature selection or feature reduction. It is also noticed that the previously used machine learning technique required human interference and expertise in determining robust features, yet was time-consuming in the labeling and data processing steps. In contrast, DL enables an embedded feature extraction and feature selection in DAEs pre-training and DNNs fine-tuning process directly from raw data. Hence, DAEs is able to extract high-level of features not only from the training data but also from unseen data. The proposed model uses 10 classes of imbalanced data from ECG signals. Since it is related to the cardiac region, abnormality is usually considered for an early diagnosis of CD. In order to validate the result, the proposed model is compared with the shallow models and DL approaches. Results found that the proposed method achieved a promising performance with 99.73% accuracy, 91.20% sensitivity, 93.60% precision, 99.80% specificity, and a 91.80% F1-Score. Moreover, both the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR) curve from the confusion matrix showed that the developed model is a good classifier. The developed model based on unsupervised feature extraction and deep neural network is ready to be used on a large population before its installation for clinical usage. View Full-Text
Keywords: cardiac disease; classification; deep learning; unsupervised feature learning cardiac disease; classification; deep learning; unsupervised feature learning
Show Figures

Figure 1

MDPI and ACS Style

Nurmaini, S.; Umi Partan, R.; Caesarendra, W.; Dewi, T.; Naufal Rahmatullah, M.; Darmawahyuni, A.; Bhayyu, V.; Firdaus, F. An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique. Appl. Sci. 2019, 9, 2921.

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.

Article Access Map by Country/Region

1
Back to TopTop