Noisy ECG Signal Data Transformation to Augment Classification Accuracy

作     者：Iqra Afzal Fiaz Majeed Muhammad Usman Ali Shahzada Khurram Akber Abid Gardezi Shafiq Ahmad Saad Aladyan Almetwally M.Mostafa Muhammad Shafiq 

作者机构：Department of Information TechnologyUniversity of GujratGujrat50700Pakistan Department of Computer ScienceUniversity of GujratGujrat50700Pakistan Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan Department of Computer ScienceCOMSATS University IslamabadIslamabadPakistan Industrial Engineering DepartmentCollege of EngineeringKing Saud UniversityP.O.Box 800Riyadh 11421Saudi Arabia Department of Information SystemsCollege of Computer and Information SciencesKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Information and Communication EngineeringYeungnam UniversityGyeongsan38541Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第71卷第5期

页      面：2191-2207页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 10[医学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the Deanship of Scientific Research at King Saud University through research group No(RG-1441-425). 

主　　题：ECG atrial fibrillation adaptive boosting heart rate variability 

摘      要：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.