Open Access

ARTICLE

Noisy ECG Signal Data Transformation to Augment Classification Accuracy

Iqra Afzal¹, Fiaz Majeed¹, Muhammad Usman Ali², Shahzada Khurram³, Akber Abid Gardezi⁴, Shafiq Ahmad⁵, Saad Aladyan⁵, Almetwally M. Mostafa⁶, Muhammad Shafiq^7,*

1 Department of Information Technology, University of Gujrat, Gujrat, 50700, Pakistan
2 Department of Computer Science, University of Gujrat, Gujrat, 50700, Pakistan
3 Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
4 Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
5 Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
6 Department of Information Systems, College of Computer and Information Sciences, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia
7 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Korea

* Corresponding Author: Muhammad Shafiq. Email:

Computers, Materials & Continua 2022, 71(2), 2191-2207. https://doi.org/10.32604/cmc.2022.022711

Received 16 August 2021; Accepted 27 September 2021; Issue published 07 December 2021

Abstract

In this era of electronic health, healthcare data is very important because it contains information about human survival. In addition, the Internet of Things (IoT) revolution has redefined modern healthcare systems and management by providing continuous monitoring. In this case, the data related to the heart is more important and requires proper analysis. For the analysis of heart data, Electrocardiogram (ECG) is used. In this work, machine learning techniques, such as adaptive boosting (AdaBoost) is used for detecting normal sinus rhythm, atrial fibrillation (AF), and noise in ECG signals to improve the classification accuracy. The proposed model uses ECG signals as input and provides results in the form of the presence or absence of disease AF, and classifies other signals as normal, other, or noise. This article derives different features from the signal using Maximal Information Coefficient (MIC) and minimum Redundancy Maximum Relevance (mRMR) technique, and then classifies them based on their attributes. Since the ECG contains some kind of noise and irregular data streams so the purpose of this study is to remove artifacts from the ECG signal by deploying the method of Second-Order-Section (SOS) (filter) and correctly classify them. Several features were extracted to improve the detection of ECG data. Compared with existing methods, this work gives promising results and can help improve the classification accuracy of the ECG signals.

---

Keywords

---