Generic placeholder image

Current Signal Transduction Therapy

Author(s): Prerak Mathur, Tanu Sharma and Karan Veer*

DOI: 10.2174/1574362417666220518120229

DownloadDownload PDF Flyer Cite As
ECG for Cardiovascular Diseases Using Soft Computing Algorithms

Article ID: e180522204973 Pages: 8

  • * (Excluding Mailing and Handling)

Abstract

Electrocardiogram (ECG) is widely used in the healthcare domain because of its usage as a diagnostics tool for several cardiovascular diseases. It becomes essential to study and analyse the ECG data with the help of classification techniques. In this review paper, a brief overview of ECG signal information is presented. Various approaches for diagnosing cardiovascular diseases have been discussed, along with the need for accurate ECG signal analysis. These approaches are mainly based on the principles of machine learning and deep learning. The advantages and limitations of these techniques in the detection of cardiovascular diseases are presented within the scope of future work. This study can be helpful for researchers in bridging the gap between current approaches and future techniques for the detection of arrhythmia conditions.

Keywords: Electrocardiogram, noise, filtering, QRS complex, machine learning, deep Learning.

Graphical Abstract

[1]
Sahoo S, Dash M, Behera S, Sabut S. Machine learning approach to detect cardiac arrhythmias in ECG signals: A survey. IRBM 2020; 41(4): 185-94.
[http://dx.doi.org/10.1016/j.irbm.2019.12.001]
[2]
Pal S, Mitra M. Increasing the accuracy of ECG-based biometric analysis by data modelling. Measurement 2012; 45(7): 1927-32.
[http://dx.doi.org/10.1016/j.measurement.2012.03.005]
[3]
Hong S, Zhou Y, Shang J, Xiao C, Sun J. Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review. Comput Biol Med 2020; 122: 103801.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103801] [PMID: 32658725]
[4]
Maglaveras N, Stamkopoulos T, Diamantaras K, Pappas C, Strintzis M. ECG pattern recognition and classification using non-linear trans-formations and neural networks: A review. Int J Med Inform 1998; 52(1-3): 191-208.
[http://dx.doi.org/10.1016/S1386-5056(98)00138-5] [PMID: 9848416]
[5]
Afadar Y, Akram A, Alkeebali A, Majzoub S. Heart Arrhythmia Detection & Monitoring Using Machine Learning & ECG Wearable De-vice. Proceedings of the Seventh International Conference on Information Technology Trends (ITT). Abu Dhabi, United Arab Emirates.. Nov 25-26, 2020;
[http://dx.doi.org/10.1109/ITT51279.2020.9320881]
[6]
Elhaj FA, Salim N, Ahmed T, Harris AR, Swee TT. Hybrid classification of Bayesian and extreme learning machine for heartbeat classifi-cation of arrhythmia detection. Proceedings of the 6th ICT International Student Project Conference (ICT-ISPC). Johar, Malaysia. May 23-24, 2017;
[http://dx.doi.org/10.1109/ICT-ISPC.2017.8075320]
[7]
Debnath T, Biswas T, Ashik MH, Dash S. Auto-encoder based nonlinear dimensionality reduction of ECG data and classification of cardi-ac arrhythmia groups using deep neural network. Proceedings of the 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT). Dhaka, Bangladesh. September 13-15, 2018;
[http://dx.doi.org/10.1109/CEEICT.2018.8628044]
[8]
Chen KC, Chien PC. A fast ECG diagnosis using a frequency-based compressive neural network. Proceedings of the IEEE 6th Global Conference on Consumer Electronics (GCCE). Nagoya, Japan.. Oct 24-27, 2017.;
[http://dx.doi.org/10.1109/GCCE.2017.8229377]
[9]
Satija U, Ramkumar B, Manikandan MS. A new automated signal quality-aware ECG beat classification method for unsupervised ECG diagnosis environments. IEEE Sens J 2018; 19(1): 277-86.
[http://dx.doi.org/10.1109/JSEN.2018.2877055]
[10]
Patel RN, Barot MP. Development of methodology for precise diagnosis of ECG by artificial intelligent. Proceedings of the 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). Coimbatore, India. February 20-22, 2019;
[http://dx.doi.org/10.1109/ICECCT.2019.8869413]
[11]
Veer K. Spectral and mathematical evaluation of electromyography signals for clinical use. Int J Biomath 2016; 9(6): 1650094.
[http://dx.doi.org/10.1142/S1793524516500947]
[12]
Veer K. A flexible approach for segregating physiological signals. Measurement 2016; 87: 21-6.
[http://dx.doi.org/10.1016/j.measurement.2016.03.017]
[13]
Bansal M. Cardiovascular disease and COVID-19. Diabetes Metab Syndr 2020; 14(3): 247-50.
[http://dx.doi.org/10.1016/j.dsx.2020.03.013] [PMID: 32247212]
[14]
Krithika R, Rohini K. Comparative interpretation of machine learning algorithms in predicting the cardiovascular death rate for Covid-19 data. 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) 2021. 394-400.
[http://dx.doi.org/10.1109/ICCIKE51210.2021.9410777]
[15]
Mahenthiran AK, Mahenthiran AK, Mahenthiran J. Cardiovascular system and COVID-19: Manifestations and therapeutics. Rev Cardiovasc Med 2020; 21(3): 399-409.
[http://dx.doi.org/10.31083/j.rcm.2020.03.124] [PMID: 33070544]
[16]
Nishiga M, Wang DW, Han Y, Lewis DB, Wu JC. COVID-19 and cardiovascular disease: From basic mechanisms to clinical perspectives. Nat Rev Cardiol 2020; 17(9): 543-58.
[http://dx.doi.org/10.1038/s41569-020-0413-9] [PMID: 32690910]
[17]
Ozdemir MA, Ozdemir GD, Guren O. Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learn-ing. BMC Med Inform Decis Mak 2021; 21(1): 170.
[http://dx.doi.org/10.1186/s12911-021-01521-x] [PMID: 34034715]
[18]
Chowdhury MH, Sultana M, Ghosh R, Ahamed JU, Mahmood MA. AI-assisted portable ECG for fast and patient-specific diagnosis. Proceedings of the International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2). Rajshahi, Bangladesh. Feb 8-9, 2018;
[19]
Bhattacharjee S, Das Z, Das AK, Roy S, Neogi B. An approach towards error-less ECG signal equation based on computational simulation aspect with modeling of cardiovascular disorder diagnosis. Proceedings of the 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC). Calcutta, India. 31 Jan.-2 Feb, 2014;
[http://dx.doi.org/10.1109/CIEC.2014.6959074]
[20]
Chen KC, Chien PC. A fast ECG diagnosis by using spectral artificial neural network (SANN) approach. Proceedings of the International Symposium on VLSI Design, Automation and Test (VLSIDAT) Hsinchu, Taiwan. April 16-19, 2018;
[21]
Yan W, Yuanjiao X, Siha Q. Algorithm study on ECG diagnosis based on wavelet packet decomposition combined with DPFNN. Proceedings of the 2nd International Conference on Information Science and Engineering Hangzhou. December 4-6, 2010;
[22]
Sameni R, Shamsollahi MB, Jutten C, Clifford GD. A nonlinear Bayesian filtering framework for ECG denoising. IEEE Trans Biomed Eng 2007; 54(12): 2172-85.
[http://dx.doi.org/10.1109/TBME.2007.897817] [PMID: 18075033]
[23]
Köhler BU, Hennig C, Orglmeister R. The principles of software QRS detection. IEEE Eng Med Biol Mag 2002; 21(1): 42-57.
[http://dx.doi.org/10.1109/51.993193] [PMID: 11935987]
[24]
Thakor NV, Zhu YS. Applications of adaptive filtering to ECG analysis: Noise cancellation and arrhythmia detection. IEEE Trans Biomed Eng 1991; 38(8): 785-94.
[http://dx.doi.org/10.1109/10.83591] [PMID: 1937512]
[25]
Dokur Z, Olmez T. ECG beat classification by a novel hybrid neural network. Comput Methods Programs Biomed 2001; 66(2-3): 167-81.
[http://dx.doi.org/10.1016/S0169-2607(00)00133-4] [PMID: 11551391]
[26]
Yu C, Liu Z, McKenna T, Reisner AT, Reifman J. A method for automatic identification of reliable heart rates calculated from ECG and PPG waveforms. J Am Med Inform Assoc 2006; 13(3): 309-20.
[http://dx.doi.org/10.1197/jamia.M1925] [PMID: 16501184]
[27]
Polat K, Şahan S, Guneş S. A new method to medical diagnosis: Artificial immune recognition system (AIRS) with fuzzy weighted prepro-cessing and application to ECG arrhythmia. Expert Syst Appl 2006; 31(2): 264-9.
[http://dx.doi.org/10.1016/j.eswa.2005.09.019]
[28]
Mehta SS, Lingayat NS. SVM-based algorithm for recognition of QRS complexes in Electrocardiogram. IRBM 2008; 29(5): 310-7.
[http://dx.doi.org/10.1016/j.rbmret.2008.03.006]
[29]
Ceylan R, Ozbay Y, Karlik B. A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network. Expert Syst Appl 2009; 36(3): 6721-6.
[http://dx.doi.org/10.1016/j.eswa.2008.08.028]
[30]
Yeh YC, Wang WJ, Chiou CW. Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals. Measurement 2009; 42(5): 778-89.
[http://dx.doi.org/10.1016/j.measurement.2009.01.004]
[31]
Dutta S, Chatterjee A, Munshi S. Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification. Med Eng Phys 2010; 32(10): 1161-9.
[http://dx.doi.org/10.1016/j.medengphy.2010.08.007] [PMID: 20833096]
[32]
Zeraatkar E, Kermani S, Mehridehnavi A, Aminzadeh A, Zeraatkar E, Sanei H. Arrhythmia detection based on morphological and time-frequency features of T-wave in electrocardiogram. J Med Signals Sens 2011; 1(2): 99-106.
[http://dx.doi.org/10.4103/2228-7477.95293] [PMID: 22606664]
[33]
Muthuchudar A, Baboo SS. A study of the processes involved in ECG signal analysis. Intern J Sci Res Publications 2013; 3(3): 1-5.
[34]
Das MK, Ari S. ECG beats classification using mixture of features. Int Sch Res Notices 2014; 2014: 178436.
[http://dx.doi.org/10.1155/2014/178436] [PMID: 27350985]
[35]
Khalaf AF, Owis MI, Yassine IA. A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines. Expert Syst Appl 2015; 42(21): 8361-8.
[http://dx.doi.org/10.1016/j.eswa.2015.06.046]
[36]
Afkhami RG, Azarnia G, Tinati MA. Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals. Pattern Recognit Lett 2016; 70: 45-51.
[http://dx.doi.org/10.1016/j.patrec.2015.11.018]
[37]
Celin S, Vasanth K. ECG signal classification using various machine learning techniques. J Med Syst 2018; 42(12): 241.
[http://dx.doi.org/10.1007/s10916-018-1083-6] [PMID: 30334106]
[38]
Syama S, Sweta GS, Kavyasree PI, Reddy KJ. Classification of ECG signal using machine learning techniques. In 2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC). 2019 Aug 21; IEEE 122-8;
[http://dx.doi.org/10.1109/ICPEDC47771.2019.9036613]
[39]
Devi RL, Kalaivani V. Machine learning and IoT-based cardiac arrhythmia diagnosis using statistical and dynamic features of ECG. J Supercomput 2019; 7: 1-2.
[40]
Prashar N, Sood M, Jain S. Novel cardiac arrhythmia processing using machine learning techniques. Int J Image Graph 2020; 20(03): 2050023.
[http://dx.doi.org/10.1142/S0219467820500230]
[41]
Izci E, Ozdemir MA, Degirmenci M, Akan A. Cardiac arrhythmia detection from 2D ECG images by using deep learning technique. Medi-cal Technologies Congress (TIPTEKNO) 2019; 1-4.
[http://dx.doi.org/10.1109/TIPTEKNO.2019.8895011]
[42]
Ozdemir AM, Guren O, Cura OK, Akan A, Onan A. Abnormal ECG beat detection based on convolutional neural networks. Medical Technologies Congress (TIPTEKNO) 2020; 1-4.
[http://dx.doi.org/10.1109/TIPTEKNO50054.2020.9299260]
[43]
Degirmenci M, Ozdemir MA, Izci E, Akan A. Arrhythmic heartbeat classification using 2d convolutional neural networks. IRBM 2021.
[http://dx.doi.org/10.1016/j.irbm.2021.04.002]
[44]
Ubeyli ED. Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals. Expert Syst Appl 2010; 37(2): 1192-9.
[http://dx.doi.org/10.1016/j.eswa.2009.06.022]
[45]
Zubair M, Kim J, Yoon C. An automated ECG beat classification system using convolutional neural networks. Proceedings of the 6th international conference on IT convergence and security (ICITCS). Prague, Czech Republic. September 26-26, 2016;
[http://dx.doi.org/10.1109/ICITCS.2016.7740310]
[46]
Isin A, Ozdalili S. Cardiac arrhythmia detection using deep learning. Procedia Comput Sci 2017; 120: 268-75.
[http://dx.doi.org/10.1016/j.procs.2017.11.238]
[47]
Pyakillya B, Kazachenko N, Mikhailovsky N. Deep learning for ECG classification. J Phys Conf Ser 2017; 913(1): 012004.
[http://dx.doi.org/10.1088/1742-6596/913/1/012004]
[48]
Acharya UR, Oh SL, Hagiwara Y, et al. A deep convolutional neural network model to classify heartbeats. Comput Biol Med 2017; 89: 389-96.
[http://dx.doi.org/10.1016/j.compbiomed.2017.08.022] [PMID: 28869899]
[49]
Swapna G, Soman KP, Vinayakumar R. Automated detection of cardiac arrhythmia using deep learning techniques. Procedia Comput Sci 2018; 132: 1192-201.
[http://dx.doi.org/10.1016/j.procs.2018.05.034]
[50]
Yıldırım Ö, Pławiak P, Tan RS, Acharya UR. Arrhythmia detection using deep convolutional neural network with long duration ECG sig-nals. Comput Biol Med 2018; 102: 411-20.
[http://dx.doi.org/10.1016/j.compbiomed.2018.09.009] [PMID: 30245122]
[51]
Oh SL, Ng EYK, Tan RS, Acharya UR. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput Biol Med 2018; 102: 278-87.
[http://dx.doi.org/10.1016/j.compbiomed.2018.06.002] [PMID: 29903630]
[52]
Liu W, Huang Q, Chang S, Wang H, He J. Multiple-feature-branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram. Biomed Signal Process Control 2018; 45: 22-32.
[http://dx.doi.org/10.1016/j.bspc.2018.05.013]
[53]
Mousavi S, Afghah F. Inter-and Intra-patient ECG heartbeat classification for arrhythmia detection: A sequence to sequence deep learning approach. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2019 May 12; 1308-12;
[http://dx.doi.org/10.1109/ICASSP.2019.8683140]
[54]
Baloglu UB, Talo M, Yildirim O, San Tan R, Acharya UR. Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recognit Lett 2019; 122: 23-30.
[http://dx.doi.org/10.1016/j.patrec.2019.02.016]
[55]
Atal DK, Singh M. Arrhythmia classification with ECG signals based on the optimization-enabled deep convolutional neural network. Comput Methods Programs Biomed 2020; 196: 105607.
[http://dx.doi.org/10.1016/j.cmpb.2020.105607] [PMID: 32593973]
[56]
Saadatnejad S, Oveisi M, Hashemi M. LSTM-based ECG classification for continuous monitoring on personal wearable devices. IEEE J Biomed Health Inform 2020; 24(2): 515-23.
[http://dx.doi.org/10.1109/JBHI.2019.2911367] [PMID: 30990452]
[57]
Essa E, Xie X. Multi-model deep learning ensemble for ecg heartbeat arrhythmia classification. Proceedings of the 28th European Signal Processing Conference (EUSIPCO). Amsterdam, Netherlands. January 18-21, 2021;
[http://dx.doi.org/10.23919/Eusipco47968.2020.9287520]
[58]
Eltrass AS, Tayel MB, Ammar AI. A new automated CNN deep learning approach for identification of ECG congestive heart failure and arrhythmia using constant-Q non-stationary Gabor transform. Biomed Signal Process Control 2021; 65: 102326.
[http://dx.doi.org/10.1016/j.bspc.2020.102326]
[59]
Veer K, Sharma T. Electromyographic classification of effort in muscle strength assessment. Biomedical Engineering/Biomedizinische Technik 2018; 63: 131-7.
[http://dx.doi.org/10.1515/bmt-2016-0038]
[60]
Yadav D. Yadav Shilpee, Veer K. Trends and applications of brain computer interfaces. Curr Signal Transduct Ther 2020; 16: 211-23.
[http://dx.doi.org/10.2174/1574362415999201124224606]
[61]
Sharma P, Pahuja SK, Veer K. Recent approaches on classification and feature extraction of EEG signal: A review. Robotica 2022; 40: 77-101.
[http://dx.doi.org/10.1017/S0263574721000382]