Login Register Cart 0
Generic placeholder image

Current Signal Transduction Therapy

Editor-in-Chief

ISSN (Print): 1574-3624
ISSN (Online): 2212-389X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
General Research Article

Early Detection of Life-threatening Cardiac Arrhythmias Using Deep Learning Techniques

Author(s): Sumathi S.* and Agalya V.

Volume 16, Issue 1, 2021

Published on: 22 October, 2019

Page: [51 - 62] Pages: 12

DOI: 10.2174/1574362414666191022145259

Abstract

Introduction: A progressive and flourishing technological advancement occurs across the communities working on a domain that needs clinical training and Technology Transfer. There is an essentiality for the evolution of advanced concepts in the Classification of healthcare, particularly in relation to arrhythmia detection towards clinical operations. Being the forerunner among the emerging areas in science and technology, this field demands an extensive practical and verification research. These innovative technological progress has significantly contributed to highquality, on-time, acceptable and affordable healthcare.

Materials & Methods: This paper approaches a novel method of Detecting and classifying the cardiac arrhythmias using deep learning model for classification of electrocardiogram (ECG) signals. This method is based on using Cubic Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias.

Results & Discussion: In these parameters are used as input to these classifier, five most important types of ECG signals they are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Pre- Ventricular Contraction (PVC), Ventricular Fibrillation (VF), and Ventricular Flutter (VFLU). By using the deep learning algorithm to recognition and classification capabilities across a broad area of biomedical engineering. The performance of the deep learning model was evaluated in terms of training performance and classification accuracies. The classification accuracy of 99.24% is achieved. Good accuracy of ECG patterns is achievable only over a large number of files.

Conclusion: These difficulties have necessitated us to develop a new detection scheme, which gives a high level of accuracy, low false positive and low false-negative statistics.

Keywords: ECG, feature extraction, segmentation, prediction, five cardiac arrhythmias, diagnosis, deep learning.

Graphical Abstract

[1]
Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng 1985; 32(3): 230-6.
[http://dx.doi.org/10.1109/TBME.1985.325532] [PMID: 3997178]
[2]
Hamilton PS, Tompkins WJ. Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. IEEE Trans Biomed Eng 1986; 33(12): 1157-65.
[http://dx.doi.org/10.1109/TBME.1986.325695] [PMID: 3817849]
[3]
Cuiwei. Li, C. Zheng, and C. Tai. Detection of ECG characteristic points using wavelet transform. IEEE Trans Biomed Eng 1995; 42(1): 8-21.
[4]
Zhao Q, Zhang L. ECG feature extraction and classification using wavelet transform and support vector machines. IEEE Trans Biomed Eng 2005; 23: 1089-92.
[5]
Lee L, Park KL, Song MH, Lee KJ. Arrhythmia classification with reduced features by linear discrimination anlysis, IEEE EMBS 27th Annual International Conference 1142-4 2005.
[6]
Feature Extraction From ECG for Classification By Artificial Neural Network University of Pretoria. In IEEE Proceeding 2002; 67-70.
[7]
Mokeddem D, Khellaf A. July “Design of a Fuzzy Controller for pH Using Genetic Algorithm,”. International Review of Computer Science and Software 2007; 2(4): 318-24.
[8]
Afonso VX, Tompkins WJ, Nguyen TQ, Luo S. ECG beat detection using filter banks. IEEE Trans Biomed Eng 1999; 46(2): 192-202.
[http://dx.doi.org/10.1109/10.740882] [PMID: 9932341]
[9]
Mahmoodabadi SZ, Ahmadian A, Abolhasani MD, Eslami M. 2005; ECG Feature Extraction Based on Multiresolution Wavelet Transform,” IEEE EMBS 27th Annual International Conference 1142-4.
[10]
Anuradha B, Suresh Kumar K, Veera Reddy V C. Cardiac Arrhythmia classification Fuzzy Clasifiers Journal of Theoretical and applied Information Technology 2008; 4: 353-8.
[11]
Benali R. Neuro-fuzzy classifier classifier for cardiac arrhythmias recognition Journal of Theoretical and applied Information Technology 2009; 4: 577-82.
[12]
Kannathal N, Sadasivan K. Puthusserypady. Cardiac State Diagnosis using Adaptive Neuro-Fuzzy Technique Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China 20053864 .
[13]
Tamil EM, Kamarudin NH. Heartbeat Elecrocardiogram signal feature extraction using discrete wavelet transform Proceedings of CSPA 1112-7. 2009.
[14]
Daubechies I. The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inf Theory 1990; •••: 961-1005.
[http://dx.doi.org/10.1109/18.57199]
[15]
Sumathi S. Dr. M.Y. Sanavullah, “Comparative Study of QRS Complex Detection in ECG Based on Discrete Wavelet Transform. International Journal of Recent Trends in Engineering 2009; 2(5): 273-7.
[16]
Branimir B. Modified ANFIS Architecture – Improving Efficiency of ANFIS Technique 7th Seminar on Neural Network Applications in Electrical Engineering, university of blgrade, Serbia and Montenegro 2004; 215-20.
[17]
Carnevale L, Celesti A, Fazio M, Bramanti P, Villari M. Heart disorder detection with menard algorithm on apache spark European Conference on Service-Oriented and Cloud Computing. 2017; 229-37.
[http://dx.doi.org/10.1007/978-3-319-67262-5_17]
[18]
Melillo P, Castaldo R, Sannino G, Orrico A, De Pietro G, Pecchia L. Wearable technology and ECG processing for fall risk assessment, prevention and detection 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBC 2015; 7740-3.
[http://dx.doi.org/10.1109/EMBC.2015.7320186]
[19]
Sannino G, De Pietro G. An evolved ehealth monitoring system for a nuclear medicine department. Developments in E-systems Engineering (DeSE) 2011; 3-6.
[http://dx.doi.org/10.1109/DeSE.2011.67]
[20]
Fang R, Pouyanfar S, Yang Y, Chen S-C, Iyengar S. Computational health informatics in the big data age: A survey. ACM Comput Surv 2016; 49(1)
[http://dx.doi.org/10.1145/2932707]
[21]
Celesti F, Celesti A, Carnevale L, et al. Big data analytics in genomics: The point on Deep Learning solutions IEEE Symposium on Computers and Communications, ISCC IEEE 2017; 306-9.
[22]
O.E. Isafiade, O.E. Isafiade, A.B. Bagula. data mining trends and applications in criminal science and investigations 1st ed. 2016; 1-386.
[23]
Garcia G, Moreira G, Menotti D, Luz E. Inter-patient ECG heartbeat classification with temporal VCG optimized by PSO. Sci Rep 2017; 7(1): 10543.
[http://dx.doi.org/10.1038/s41598-017-09837-3] [PMID: 28874683 ]
[24]
http://www.physionet.org

Rights & Permissions Print Cite
Article Metrics
20
1
© 2024 Bentham Science Publishers | Privacy Policy