Review of Remote ECG signal Monitoring, Preprocessing and Arrhythmia Detection
DOI:
https://doi.org/10.32792/jeps.v10i2.75Keywords:
ECG, ANN, Raspberry Pi and QRS.Abstract
ECG signal is one of the most significant biomedical signals, which is used to reflect the activity of human heart, and it is considered as one of better understood signals, that can provide basic information to diagnosis of heart disease. Therefore, different study and intensive research are developed in recent years, also different effective techniques and methods are proposed for analysis and processing in order to discover essential and new diagnostic information. In this paper, we are introduced literature review of significant study, techniques and algorithms, which are applied to ECG signal, some of these method are used to pre-process the ECG signal in order to increase the accuracy of diagnosis of heart problem, while other techniques are used to classify the signal automatically. The study is explained the methods that are applied to extract the ECG signal with different monitoring system either directly or by using remote monitoring system depending on some technique, also the ECG signal algorithms and methods are described in some details. Because QRS complex part of the ECG signal is considered the most significant information of the signal, different algorithm to detect this peak are explained in our study, finally, the classification algorithm of the ECG signal is described, classification is used to determine the statue of the heart activity and according it, classify the problem.References
Warish D., Chirag P.and Carlos V., “IoMT based Efficient Vital Signs Monitoring System for Elderly Healthcare Using Neural Network”, International Journal of Research, Vol.7, No.1, 2019.
Jasti Sowmya Sree, Mohammed Ali Hussain,” An efficient body Line health monitoring system with alerts triggered through predictive data analytics”, International journal of innovative technology and exploring engineering, Vol.8, No.6, 2019.
Rashida Shujaee and M. Nasiruddin, “Optimization of A Smart IOT Gateway “, International journal on recent and innovation trends in computing and communication, Vol. 5,2017.
Andre Gl ´ oria, Francisco Cercasa and Nuno S.,” Design and implementation of an IoT gateway to create smart environments”, 8th international conference on ambient Systems, networks and technologies, 2017.Elsever.
Surekha N., Yamuna N., Akhil K. and Naveen K.,” Patiant monitoring system using IOT “, International Journal of Innovative Research in Advanced Engineering, Vol. 5, No.5, 2018.
Poonam Gupta, Satyasheel P. and Dharmanath R.,” Smart Ambulance System”, International journal of computer applications (0975 – 8887) national conference on advances in computing, communication and networking ,2016.
Harishchandra D. at el., “Fog computing in medical internet of Things: architecture, implementation, and applications”, arXiv:1706., 2017.
Anand Kubal and Chandrashekarappa K,” Design of E-Health Monitoring of Patient using Internet of Things”, International journal of latest technology ine, management and applied science, Vol. 4, No.7 ,2017.
Rohit Kumar et.al,” Patient’s Health Monitoring System using Internet of Things (Iot) “, international journal of engineering trends and technology, Vol.59, No.3 2018.
Chandini et al. “ECG telemetry system for IOT using Raspberry Pi”, international journal of engineering research and technology, 2018 Conference Proceedings Vol. 6, No. 13, 2018.
K. Seena Naik and E. Sudarshan,” smart health care monitoring system using raspberry pi on IOT platform, journal of engineering and applied sciences, Vol. 14, No. 4, 2019
Ayaskanta Mishra and Biswarup chakaboraty,” AD8232 based smart health care system using internet of things (IOT), international journal of engineering research and technology, vol.7, No., 2018.
Mrs.G. Mohana Prabha, Autamatic,” health monitoring system using Raspberry PI”, international journal of pure and applied mathematics, Vol. 118, No. 8, 2018.
Shamika Jog, Prajakta Ghodake, Darshana Jagta pand Deepgandha Shete,” Patient Health Monitoring and Controlling System using Internet of Things “, international journal of advanced research in computer and communication engineering, Vol. 6, No.5, May 2017.
Jae H. and Dong W.S, “Development of ECG monitoring system and implantable device with wireless charging”, Micromachines (Basel). 2019.
Deiaa Eida, Amr Y., and Ali E., “ECG signal transmissions performance over wearable wireless sensor networks “, International Conference on Communication, Management and Information Technology (ICCMIT 2015) Procedia Computer Science 65 (2015) 412 – 421.
Gauravi. A. Yadav and Shailaja.S., “Rasberry Pi based ECG data acquisition system “, international journal of advance research in engineering and technology.vol.6, No. 4, 2017.
Zhang, et al. “An ECG signal denoising approach based on wavelet energy and subband smoothing filter dengyong “, applied Scinence,2019.
Octa H.and Ali M. Al, “comparison of wavelet family performances in ECG signal denoising “, Telekomunikasi (JET), Vol. 17, No. 1, p.p. 1:6., 2017.
Smoothing C. et al. “ECG signal denoising and features extraction using unbiased FIR”, Biomedical Research International, Vol.2019, 2019.
Ibtissem Houameed, Lamir Said and Fawzi Srairi, “ECG signal denoising by fractional wavelet transform thresholding”, Research of Biomedical Engineering, Vol. 36, p.p. 349:360, 2020.
Pinjala N., Ch. B. and K. Satya P.,” An investigation on the performance analysis of ECG signal denoising using digital filters and wavelet Family”, international journal of recent technology and engineering, Vol.8, No.1, 2019.
Aswathy Velayudhan and Soniya P., “noise analysis and different denoising techniques of ECG Signal, a Survey “, journal of electronics and communication engineering. P.P. 40:44 ,2016
r. Rahul Kher, G H Patel, “Signal Processing Techniques for Removing Noise from ECG Signals”, Journal of Biomedical Engineering and Research, Vol 3: 101, 2019.
Ragini Sharma and Rashmi Kashyap, “design and implementation of denoising of ECG system for medical applications”, international journal of science, engineering and technology research, Vol.6, No. 8, t 2017.
Xiong H. et al., “A stacked contractive denoising auto encoder for ECG signal denoising” , Physiological measurement, Vol.37, No12, 2018.
M.Said Ashraf and A.M. Ashraf,” Proposed network structures and combined adaptive algorithms for electrocardiogram signal denoising”, International Journal of control and signal processing, Vol.34, No.3, 2020.
W. Jenkal et al. “An efficient method of ECG signals denoising based on an adaptive algorithm using mean filter and an fdaptive dual threshold filter”, International review in computer and software, vol.10, No.11, 2015.
Zhaoyang, Juniang Zhu, Tiang Yan and Lulu Yang, “A new modified wavelet based ECG denoising”, Journal of computer assisted surgery, Vol. 24, 2019.
X. Ning, and W. Selesnick, “ECG Enhancement and QRS detection based on sparse derivatives”, biomedical signal processing and control, p.p. 713:723, 2017.
ParkJ. Et al. “R peak detection method using wavelet transform and modified shannon energy envelope”, Journal of health care engineering, Vol. 2017.
Runnan, H.et al., “A novel method for the detection of R-peaks in ECG based on K-nearest neighbors and particle swarm optimization.”, Journal on advances in signal processing, 2017.
Barhattei, a et al. “Cardiac events detection using curvelet transform”, Indian Academy of Sciences, Vol.44, No. 2, 2018.
S. Billgan and Z. Akin, “New robust QRS detection algorithm in arrhythmia ECG signals”, Journal of engineering sciences and design, Vol. 6, No. 1, p.p. 64:73, 2018.
Antonio E. et al., “RS complex detection and measurement algorithms for multichannel ECGs in cardiac resynchronization therapy patients”, IEEE J Trans. Eng. Health Med. Vol.6, 2018;
Xuanyu Lu, Maolin P., and Yang Y., “QRS detection based on improved adaptive threshold”, Journal of healthcare engineering, Vol.2, 2018.
Aiyun C. et al., “A real time QRS detection algorithm based on ET and PD controlled threshold strategy”, Sensors, 2020.
Trio P. Utomo1, Nuryani N. and, Anto S., “Automatic QRS-complex peak detector based on moving average and thresholding”, journal of physics, conference series, 9th international conference .2019.
Daizong, Y. and Yue Z. “A real time QRS detector based on low pass differentiator and hilbert transform”, Web of Conferences, 2018.
Hung Y. C., Yan H. and Cheng Y. “A Novel wavelet based algorithm for detection of QRS complex”, applied science, 2019.
Bilal H. et al. “An accurate QRS complex and P wave detection in ECG signals using complete ensemble empirical mode decomposition with adaptive noise approach “, IEEE Access, Vol.7, 2019.
Subha. R., Anandakumar K. and Bharathi A., “Study on cardiovascular disease classification using machine learning approaches”, international journal of applied engineering research, Vol. 11, No. 6, p.p.4377:2016.4380
Karthikeyan T. and.A. Kanimozhi V. “Deep learning approach for prediction of heart disease using data mining classification algorithm deep belief network”, International journal of advanced research in science, engineering and technology Vol. 4, No.1, 2017.
Rami assari, Parham A. and Mohammed reza Taghava,” heart disease diagnosis using data mining techniques”, international journal of economics and management science Vol 6, No.3, 2017.
Kathleen H. Miaoa, Julia H. Miaoa, “Heart disease diagnosis using deep neural networks”, international journal of advanced computer science and applications, Vol. 9, No. 10, 2018.
Abhay K. et al., “heart attack prediction using deep learning”, international research journal of engineering and technology, Vol.05, No.4,2018.
Youness Khourdifi1 and Mohamed B. “Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization”, international journal of engineering and systems, 2018.
Ankit S.and Sachin M. B. “ECG signal classification using hidden markov model and artificial neural network”, international journal of engineering research & technology Vol. 3, No. 2, 2014.
Stalin S., Rajkumar P. and Subbuthai P., “Feature extraction and classification for ECG signal processing based on artificial neural network and machine learning Approach”, International Conference on Inter Disciplinary Research in Engineering and technology, 2015.
Zhaohan X., Martin K. and Jichao Z,” Robust ECG signal classification for detection of atrial fibrillation using a novel neural network”, computing in cardiology, Vol. 44, 2017.
C. Venkastune, et al. “ECG signal preprocessing and SVM classifier based abnormality detection in remote healthcare applications”, IEEE.,2018.
Mohammad K. Shayan F. and Majid S., “ECG heartbeat classification, a deep transferable representation”, arXiv, 2018.
Tae Joon Jun et al., “ECG arrhythmia classification using a 2-D convolutional neural network”, arXiv, Vol., 2018.
Shraddha S., Saroj K. Pandey, Urja P. and Rekh R.,” Classification of ECG arrhythmia using recurrent neural networks”, international conference in computational intelligent and data science, Elsevier, Procedia computer science ,2018
Gaurav K. l and Ranbir P., “Artificial neural network for ECG classification “, recent research in science and Technology, Vol. 6, No. 1, 2014.
Firas Sabar Miften and Tawfiq A. Abbas, “Fractal recognition using Wavelet transform “, Journal of Babylon University, Vol.20, No.6,2012.
Firas Sabar Miften Tawfiq A. Abbas, “Fractal patterns Classification in GIS System”, Journal of College of Education for Pure Sciences, University of Thi_Qa, Vol.1, No,4,2011.
Rand Ameen Azeez, Firas Sabar Miften and Mustafa J Hayawi,” “, Journal of Global Scientific Research, Vol.9, No.4, pp. 461-465,2020
Downloads
Published
Issue
Section
License
The Authors understand that, the copyright of the articles shall be assigned to Journal of education for Pure Science (JEPS), University of Thi-Qar as publisher of the journal.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms and any other similar reproductions, as well as translations. The reproduction of any part of this journal, its storage in databases and its transmission by any form or media, such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc. , will be allowed only with a written permission from Journal of education for Pure Science (JEPS), University of Thi-Qar.
Journal of education for Pure Science (JEPS), University of Thi-Qar, the Editors and the Advisory International Editorial Board make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in the Journal of education for Pure Science (JEPS), University of Thi-Qar are sole and exclusive responsibility of their respective authors and advertisers.