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Invited Review

A review of arrhythmia detection based on electrocardiogram with artificial intelligence

ORCID Icon, , , , , & ORCID Icon show all
Pages 549-560 | Received 22 Jun 2022, Accepted 18 Aug 2022, Published online: 25 Aug 2022

References

  • Benjamin EJ, Muntner P, Alonso A, et al. Heart disease and stroke statistics—2019 update: a report from the American Heart Association. Circulation. 2019;139(10):e56–e528.
  • Martis RJ, Acharya UR, Adeli H. Current methods in electrocardiogram characterization. Comput Biol Med. 2014;48:133–149.
  • Sahoo S, Dash M, Behera S, et al. Machine learning approach to detect cardiac arrhythmias in ECG signals: a survey. Irbm. 2020;41(4):185–194.
  • Elgendi M, Meo M, Abbott D. A proof-of-concept study: simple and effective detection of P and T waves in arrhythmic ECG signals. Bioengineering. 2016;3(4):26.
  • Faust O, Hagiwara Y, Hong TJ, et al. Deep learning for healthcare applications based on physiological signals: a review. Comput Methods Programs Biomed. 2018;161:1–13.
  • Naik S, Debnath S, Justin V. A review of arrhythmia classification with artificial intelligence techniques: deep vs machine learning. In: 2021 2nd International Conference for Emerging Technology (INCET), Belagavi, India, (IEEE, 2021) 1–14. •• Review article using artificial intelligence for arrhythmia classification.
  • Liu X, Wang H, Li Z, et al. Deep learning in ECG diagnosis: a review. Knowledge-Based Syst. 2021;227:107187.
  • Grosan C, Abraham A Rule-based expert systems. In: Intelligent systems. Berlin, Heidelberg: Springer; 2011. p. 149–185.
  • Jin Y, Li Z, Liu Y, et al. Multi-class 12-lead ECG Automatic Diagnosis based on a novel Subdomain Adaptive Deep Network. Science China Technological Sciences. 2022;(): doi:https://doi.org/10.1007/s11431-022-2080-6.
  • Jin Y, Li Z, Qin C, et al. A novel interpretable method based on attentional deep neural network for actual ECG quality assessment. Biomed Signal Process Cont. 2023;79:104064.
  • Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inf Theory. 1967;13(1):21–27.
  • Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–297.
  • Safavian SR, Landgrebe D. A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern. 1991;21(3):660–674.
  • Jain AK, Mao J, Mohiuddin KM. Artificial neural networks: a tutorial. Computer. 1996;29(3):31–44.
  • Park J, Lee K, Kang K. Arrhythmia detection from heartbeat using k-nearest neighbor classifier. In: 2013 IEEE International Conference on Bioinformatics and Biomedicine, Shanghai, China, (IEEE, 2013) 15–22.
  • Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng. 1985;3:230–236.
  • Sharma M, Tan R-S, Acharya UR. Automated heartbeat classification and detection of arrhythmia using optimal orthogonal wavelet filters. Inf Med Unlocked. 2019;16:100221.
  • Alonso-Atienza F, Morgado E, Fernandez-Martinez L, et al. Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans Biomed Eng. 2013;61(3):832–840.
  • Mustaqeem A, Anwar SM, Majid M. Multiclass classification of cardiac arrhythmia using improved feature selection and SVM invariants. Comput Math Methods Med. 2018;2018.
  • Asuncion A, Newman D. UCI machine learning repository. CA, USA: Irvine; 2007.
  • Elsayyad A, Nassef AM, Baareh AK. Cardiac arrhythmia classification using boosted decision trees. Int Rev Comput Softw. 2015;10:280–289.
  • Sahoo S, Subudhi A, Dash M, et al. Automatic classification of cardiac arrhythmias based on hybrid features and decision tree algorithm. Int J Autom Comput. 2020;17(4):551–561.
  • Martis RJ, Acharya UR, Prasad H, et al. Application of higher order statistics for atrial arrhythmia classification. Biomed Signal Process Control. 2013;8(6):888–900.
  • Li H, Yuan D, Ma X, et al. Genetic algorithm for the optimization of features and neural networks in ECG signals classification. Sci Rep. 2017;7(1):1–12.
  • Murat F, Sadak F, Yildirim O, et al., Review of deep learning-based atrial fibrillation detection studies. Int J Environ Res Public Health. 2021;18(21): 11302.
  • Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25.
  • Acharya UR, Oh SL, Hagiwara Y, et al. A deep convolutional neural network model to classify heartbeats. Comput Biol Med. 2017;89:389–396.
  • AAMI A, EC57 A. (R) 2008-Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms. American National Standards Institute, Arlington, VA, (2008).
  • He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas, NV, USA. 2016. 770–778.
  • Jing E, Zhang H, Li Z, et al. ECG heartbeat classification based on an improved ResNet-18 model. Comput Math Methods Med. 2021;2021.
  • Singh S, Pandey SK, Pawar U, et al. Classification of ECG arrhythmia using recurrent neural networks. Procedia Comput Sci. 2018;132:1290–1297.
  • Noor ST, Asad ST, Khan MM, et al. Predicting the risk of depression based on ECG using RNN. Comput Intell Neurosci. 2021;2021.
  • Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–1780.
  • Yildirim O, Baloglu UB, Tan R-S, et al. A new approach for arrhythmia classification using deep coded features and LSTM networks. Comput Methods Programs Biomed. 2019;176:121–133.
  • Schuster M, Paliwal KK. Bidirectional recurrent neural networks. IEEE Trans Signal Process. 1997;45(11):2673–2681.
  • Rahul J, Sharma LD. Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and Bi-LSTM model. Biocybernetics Biomed Eng. 2022;42(1):312–324.
  • Andersen RS, Peimankar A, Puthusserypady S. A deep learning approach for real-time detection of atrial fibrillation. Expert Syst Appl. 2019;115:465–473.
  • Salem M, Taheri S, Yuan JS. ECG arrhythmia classification using transfer learning from 2-dimensional deep CNN features. In: 2018 IEEE biomedical circuits and systems conference (BioCAS), Cleveland, OH, USA. (IEEE, 2018) 1–4.
  • Shi H, Wang H, Qin C, et al. An incremental learning system for atrial fibrillation detection based on transfer learning and active learning. Comput Methods Programs Biomed. 2020;187:105219.
  • Moody G. A new method for detecting atrial fibrillation using RR intervals. Comput Cardiol. 1983: 227–230
  • Serhani MA, Ismail H, El-Kassabi HT, et al. Adaptive deep reinforcement learning model for predicting arrhythmia from ECG signal. Available at SSRN 4069600).
  • Xiong P, Wang H, Liu M, et al. ECG signal enhancement based on improved denoising auto-encoder. Eng Appl Artif Intell. 2016;52:194–202.
  • Kabir MA, Shahnaz C. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed Signal Process Control. 2012;7(5):481–489.
  • Arsene CT, Hankins R, Yin H. Deep learning models for denoising ECG signals. In: 2019 27th European Signal Processing Conference (EUSIPCO). A Coruna, Spain, (IEEE, 2019) 1–5.
  • Chiang H-T, Hsieh -Y-Y, S-W F, et al. Noise reduction in ECG signals using fully convolutional denoising autoencoders. Ieee Access. 2019;7:60806–60813.
  • Wang G, Yang L, Liu M, et al. ECG signal denoising based on deep factor analysis. Biomed Signal Process Control. 2020;57:101824.
  • Singh P, Pradhan G. A new ECG denoising framework using generative adversarial network. IEEE/ACM Trans Comput Biol Bioinform. 2020;18(2):759–764.
  • Dasan E, Panneerselvam I. A novel dimensionality reduction approach for ECG signal via convolutional denoising autoencoder with LSTM. Biomed Signal Process Control. 2021;63:102225.
  • Qiu L, Cai W, Zhang M, et al. Two-stage ECG signal denoising based on deep convolutional network. Physiol Meas. 2021;42(11):115002.
  • Zhou X, Zhu X, Nakamura K, et al. Electrocardiogram quality assessment with a generalized deep learning model assisted by conditional generative adversarial networks. Life. 2021;11(10):1013.
  • Antczak K. Deep recurrent neural networks for ECG signal denoising. arXiv preprint arXiv:1807.11551. 2018;
  • Goldberger AL, Amaral LA, Glass L, et al. PhysioBank, physiotoolkit, and physioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):e215–e220.
  • Wang J, Li R, Li R, et al. Adversarial de-noising of electrocardiogram. Neurocomputing. 2019;349:212–224.
  • Rasti-Meymandi A, Ghaffari A. A deep learning-based framework For ECG signal denoising based on stacked cardiac cycle tensor. Biomed Signal Process Control. 2022;71:103275.
  • Zhou X, Zhu X, Nakamura K, et al. ECG quality assessment using 1D-convolutional neural network. In: 2018 14th IEEE International Conference on Signal Processing (ICSP). Beijing, China. (IEEE, 2018) 780–784.
  • Albaba A, Simões-Capela N, Wang Y, et al. Assessing the signal quality of electrocardiograms from varied acquisition sources: a generic machine learning pipeline for model generation. Comput Biol Med. 2021;130:104164.
  • Liu G, Han X, Tian L, et al. ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features. Comput Methods Programs Biomed. 2021;208:106269.
  • Silva I, Moody GB, Celi L. Improving the quality of ECGs collected using mobile phones: the physionet/computing in cardiology challenge 2011. In: 2011 Computing in Cardiology, Hangzhou, China, (IEEE, 2011) 273–276.
  • Zhu Z, Liu W, Yao Y, et al. AdaBoost based ECG signal quality evaluation. In: 2019 Computing in Cardiology (CinC), Singapore. (IEEE, 2019) 1–4.
  • Zhao Z, Liu C, Li Y, et al. Noise rejection for wearable ECGs using modified frequency slice wavelet transform and convolutional neural networks. IEEE Access. 2019;7:34060–34067.
  • Li Q, Rajagopalan C, Clifford GD. A machine learning approach to multi-level ECG signal quality classification. Comput Methods Programs Biomed. 2014;117(3):435–447.
  • Peimankar A, Puthusserypady S. DENS-ECG: a deep learning approach for ECG signal delineation. Expert Syst Appl. 2021;165:113911.
  • Abrishami H, Campbell M, Chia H, et al. P-QRS-T localization in ECG using deep learning (2018).
  • Liang X, Li L, Liu Y, et al. ECG_SegNet: an ECG delineation model based on the encoder-decoder structure. Comput Biol Med. 2022;145:105445.
  • Moskalenko V, Zolotykh N, Osipov G. Deep learning for ECG segmentation. In: international conference on Neuroinformatics. Dolgoprudny, Russia, Springer, 2019 246–254.
  • Vila JA, Gang Y, Presedo JMR, et al. A new approach for TU complex characterization. IEEE Trans Biomed Eng. 2000;47(6):764–772.
  • Gupta R, Mitra M, Mondal K, et al. A derivative-based approach for QT-segment feature extraction in digitized ECG record. In: 2011 Second International Conference on Emerging Applications of Information Technology, Kolkata, India. (IEEE, 2011) 63–66.
  • Martínez A, Alcaraz R, Rieta JJ. Application of the phasor transform for automatic delineation of single-lead ECG fiducial points. Physiol Meas. 2010;31(11):1467.
  • Dumont J, Hernandez A, Carrault G. Parameter optimization of a wavelet-based electrocardiogram delineator with an evolutionary algorithm. In: Computers in Cardiology, 2005, Lyon, France, (IEEE, 2005) 707–710.
  • Almeida R, MartÍnez JP, Rocha AP, et al. Multilead ECG delineation using spatially projected leads from wavelet transform loops. IEEE Trans Biomed Eng. 2009;56(8):1996–2005.
  • Madeiro JP, Cortez PC, Marques JA, et al. An innovative approach of QRS segmentation based on first-derivative, Hilbert and wavelet transforms. Med Eng Phys. 2012;34(9):1236–1246.
  • Mukhopadhyay S, Mitra M, Mitra S. Time plane ECG feature extraction using Hilbert transform, variable threshold and slope reversal approach. In: 2011 International Conference on Communication and Industrial Application, Kolkata, India. (IEEE, 2011) 1–4.
  • Maršánová L, Němcová A, Smíšek R, et al. Automatic detection of P wave in ECG during ventricular extrasystoles. In: World congress on medical physics and biomedical engineering 2018. New York: Springer; 2019. 381–385.
  • Liu J, Liu Y, Jin Y, et al. P-wave detection using a parallel convolutional neural network in electrocardiogram. In: 2021 4th International Conference on Information Communication and Signal Processing (ICICSP). Shanghai, China. (IEEE, 2021) 157–161.
  • Šarlija M, Jurišić F, Popović S. A convolutional neural network based approach to QRS detection. In: Proceedings of the 10th international symposium on image and signal processing and analysis, Ljubljana, Slovenia. (IEEE, 2017) 121–125.
  • Xiang Y, Lin Z, Meng J. Automatic QRS complex detection using two-level convolutional neural network. Biomed Eng Online. 2018;17(1):1–17.
  • Wang X, Zou Q. QRS detection in ECG signal based on residual network. In: 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN), Chongqing, China. (IEEE, 2019) 73–77.
  • Belkadi MA, Daamouche A, Melgani F. A deep neural network approach to QRS detection using autoencoders. Expert Syst Appl. 2021;184:115528.
  • Peimankar A, Puthusserypady S. An ensemble of deep recurrent neural networks for p-wave detection in electrocardiogram. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brighton, UK. (IEEE, 2019). 1284–1288.
  • Costandy RN, Gasser SM, El-Mahallawy MS, et al. P-wave detection using a fully convolutional neural network in electrocardiogram images. Appl Sci. 2020;10(3):976.
  • Sodmann P, Vollmer M, Nath N, et al. A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms. Physiol Meas. 2018;39(10):104005.
  • Abrishami H, Han C, Zhou X, et al. Supervised ECG interval segmentation using lstm neural network. In: Proceedings of the International Conference on Bioinformatics & Computational Biology (BIOCOMP). Las Vegas, NV. The Steering Committee of The World Congress in Computer Science, Computer, 2018. 71–77.
  • Londhe AN, Atulkar M. Semantic segmentation of ECG waves using hybrid channel-mix convolutional and bidirectional LSTM. Biomed Signal Process Control. 2021;63:102162.
  • Nurmaini S, Darmawahyuni A, Rachmatullah MN, et al. Beat-to-beat electrocardiogram waveform classification based on a stacked convolutional and bidirectional long short-term memory. IEEE Access. 2021;9:92600–92613.
  • Duraj K, Piaseczna N, Kostka P, et al. Semantic segmentation of 12-Lead ECG using 1D residual U-Net with squeeze-excitation blocks. Appl Sci. 2022;12(7):3332.
  • Rahul J, Sora M, Sharma LD. A novel and lightweight P, QRS, and T peaks detector using adaptive thresholding and template waveform. Comput Biol Med. 2021;132:104307.
  • Jimenez-Perez G, Alcaine A, Camara O. Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks. Sci Rep. 2021;11(1):1–11.
  • Feeny AK, Chung MK, Madabhushi A, et al. Artificial intelligence and machine learning in arrhythmias and cardiac electrophysiology. Circ Arrhythm Electrophysiol. 2020;13(8):e007952.
  • Raj S, Ray KC. Automated recognition of cardiac arrhythmias using sparse decomposition over composite dictionary. Comput Methods Programs Biomed. 2018;165:175–186.
  • Yang W, Si Y, Wang D, et al. Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine. Comput Biol Med. 2018;101:22–32.
  • Bin G, Shao M, Bin G, et al. Detection of atrial fibrillation using decision tree ensemble. In: 2017 Computing in Cardiology (CinC). Rennes, France. (IEEE, 2017) 1–4.
  • Li P, Wang Y, He J, et al. High-performance personalized heartbeat classification model for long-term ECG signal. IEEE Trans Biomed Eng. 2016;64(1):78–86.
  • Yıldırım Ö, Pławiak P, Tan R-S, et al. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med. 2018;102:411–420.
  • Ping Y, Chen C, Wu L, et al.Automatic detection of atrial fibrillation based on CNN-LSTM and shortcut connection. MDPI Healthcare, Rennes, France. 2020:139.
  • Shi H, Qin C, Xiao D, et al. Automated heartbeat classification based on deep neural network with multiple input layers. Knowledge-Based Syst. 2020;188:105036.
  • Yao Q, Wang R, Fan X, et al. Multi-class arrhythmia detection from 12-lead varied-length ECG using attention-based time-incremental convolutional neural network. Inf Fusion. 2020;53:174–182.
  • Guan J, Wang W, Feng P, et al. Low-dimensional denoising embedding transformer for ECG classification. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Toronto, ON, Canada. (IEEE, 2021) 1285–1289.
  • Che C, Zhang P, Zhu M, et al. Constrained transformer network for ECG signal processing and arrhythmia classification. BMC Med Inform Decis Mak. 2021;21(1):1–13.
  • Liu Y, Qin C, Liu J, et al. An efficient neural network-based method for patient-specific information involved arrhythmia detection. Knowledge-Based Syst. 2022:109021.
  • Madan P, Singh V, Singh DP, et al. A hybrid deep learning approach for ECG-based arrhythmia classification. Bioengineering. 2022;9(4):152.
  • Rangappa VG, Prasad S, Agarwal A. Classification of cardiac arrhythmia stages using hybrid features extraction with k-nearest neighbour classifier of ECG signals. learning. 2018;11:21–32.
  • Rajani Kumari L, Padma Sai Y Classification of arrhythmia beats using optimized K-nearest neighbor classifier. In: Intelligent systems. Singapore: Springer; 2021. p. 349–359.
  • Shi H, Wang H, Zhang F, et al. Inter-patient heartbeat classification based on region feature extraction and ensemble classifier. Biomed Signal Process Control. 2019;51:97–105.
  • Kiranyaz S, Ince T, Gabbouj M. Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed Eng. 2015;63(3):664–675.
  • Acharya UR, Fujita H, Lih OS, et al. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf Sci. 2017;405:81–90.
  • Rajpurkar P, Hannun AY, Haghpanahi M, et al., Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv preprint arXiv:1707.01836. 2017;
  • Liu Y, Jin Y, Liu J, et al. Precise and efficient heartbeat classification using a novel lightweight-modified method. Biomed Signal Process Control. 2021;68:102771.
  • Faust O, Shenfield A, Kareem M, et al. Automated detection of atrial fibrillation using long short-term memory network with RR interval signals. Comput Biol Med. 2018;102:327–335.
  • Gao J, Zhang H, Lu P, et al. An effective LSTM recurrent network to detect arrhythmia on imbalanced ECG dataset. J Healthc Eng. 2019;2019.
  • Jin Y, Qin C, Huang Y, et al. Multi-domain modeling of atrial fibrillation detection with twin attentional convolutional long short-term memory neural networks. Knowledge-Based Syst. 2020;193:105460.
  • Zheng Z, Chen Z, Hu F, et al. An automatic diagnosis of arrhythmias using a combination of CNN and LSTM technology. Electronics. 2020;9(1):121.
  • Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al., An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019;394(10201): 861–867.
  • Ribeiro AH, Ribeiro MH, Paixão GM, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020;11(1):1–9.
  • Biton S, Gendelman S, Ribeiro AH, et al. Atrial fibrillation risk prediction from the 12-lead electrocardiogram using digital biomarkers and deep representation learning. Eur Heart J Digit Health. 2021;2(4):576–585.
  • Meng L, Tan W, Ma J, et al. Enhancing dynamic ECG heartbeat classification with lightweight transformer model. Artif Intell Med. 2022;124:102236.
  • Wang Y, Yao Q, Kwok JT, et al. Generalizing from a few examples: a survey on few-shot learning. ACM Comput Surveys. 2020;53(3):1–34.
  • Xu C, Shen J, Du X. A method of few-shot network intrusion detection based on meta-learning framework. IEEE Trans Inf Forensics Secur. 2020;15:3540–3552.

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