Abstract
Electrocardiogram (ECG) analysis is a conventional way of finding heart abnormality. It is a clinical procedure in which the electrical activity of the heart is measured during every cardiac cycle and checked for healthiness of the heart. It is approximated in this industrialized world that millions of people expire every 12 months because of various coronary heart diseases and short of prompt detection of uncharacteristic heart rhythms. To detect these abnormalities promptly, the ECG measures should provide the cardiac signals without any mixtures or other disturbances. Though accurate classification of ECG is a challenging task as it varies with time and also with persons of different ages, it is the need of the hour. In this proposed research work, an improved independent component analysis (ICA) algorithm is used to extract pure ECG components from the ECG mixtures before the signals are applied to machine learning classifiers for accurate detection and classification of ECG signals. These machine learning models are applied after the signals are preprocessed to reduce the dimensionality and the training time. This work also uses deep learning convolution neural network (CNN) model with different optimizers for ECG classification and analysis. Classification performance of these algorithms is improved when classification is done after extracting the features using ICA technique.
Similar content being viewed by others
References
Rundo, F.: An advanced bio-inspired photo-plethysmography (PPG) and ECG pattern recognition system for medical assessment. Sensors 18(2), 1–22 (2018)
Huikuri, H.V., Castellanos, A., Myerburg, R.J.: Sudden death due to cardiac arrhythmias. N. Engl. J. Med. 345(20), 1473–1482 (2001)
Barros, A.K., Mansour, A., Ohnishi, N.: Removing artefacts from electrocardiographic signals using independent components analysis. Neurocomputing 22(1), 173–186 (1998)
Kaufmann, M., Niknejad, A., Petrovic, D.: Introduction to computational intelligence techniques and areas of their applications in medicine. Med. Appl. Artif. Intell. 52, 51–70 (2013)
WHO report on cardiovascular diseases, World Health Organization, May 2017
Chang, C.H.: Cancellation of high-frequency noise in ECG signals using adaptive filter without external reference. In: 3rd International Conference on Biomedical Engineering and Informatics, pp. 787–790 (2010)
Casas, M.M., Avitia, R.L., Gonzalez-Navarro, F.F., Cardenas-Haro, J.A., Reyna, M.A.: Bayesian classification models for premature ventricular contraction detection on ECG traces. J. Healthcare Eng. 2018, 1–7 (2018)
Xiong, Z., Stiles, M.K., Zhao, J.: Robust ECG signal classification for detection of atrial fibrillation (AF) using a novel neural network. Comput. Cardiol. 44, 1–4 (2017)
Vishwa, A., Lal, M.K., Dixit, S., Varadwaj, P.: Classification of arrhythmic ECG data using machine learning techniques. Int. J. Interact. Multi 1(4), 67–70 (2011)
Malay, M., Samantab, R.K.: Cardiac arrhythmia classification using neural networks with selected features. Proc. Technol. 10, 76–84 (2013)
Das, M., Ari, S.: ECG beats classification using mixture of features. Int. Sch. Res. Notices 3, 1–12 (2014)
Gupta, A., Betsy, Thomas., Pradeep, K., Saket, K.: Neural Network based indicative ECG classification. In: Proceedings of 5th International Conference on Confluence the Next Generation Information Technology Summit, Noida, India, pp. 277–279
Ingole, M.D., Alaspure, S.V., Ingole, D.T.: Electrocardiogram signal feature extraction and classification using various signal analysis techniques. Int. J. Eng. Sci. Technol. 3(1), 39–44 (2014)
Jambukia, S.H., Dabhi, V.K., Prajapati, H.B.: Classification of ECG signals using machine learning techniques. In: Proceedings of International Conference on Advances in Computer Engineering and Applications, Ghaziabad, India, vol. 26, No. 1, pp. 32–53 (2015)
Rajpurkar, P., Hannun, A., Haghpanahi, M., Bourn, C., Ng, A.Y.: Cardiologist level arrhythmia detection with convolutional neural networks. Nat. Med. 25(1), 65–69 (2017)
Pourbabaee, B., Roshtkhari, M.J., Khorasani, K.: Deep convolutional neural networks and learning ECG features for screening paroxysmal atrial fibrillation patients. IEEE Trans. Syst. Man Cybern. Syst. 48(12), 2095–2104 (2018)
Pyakillya, B., Kazachenko, N., Mikhailovsky, N.: Deep learning for ECG classification. J. Phys. Conf. Ser. 913, 1 (2017)
Kshirsagar, P.R., Akojwar, S.G., Dhanoriya, R.: Classification of ECG-signals using artificial neural networks. In: Proceedings of International Conference on Intelligent Technologies and Engineering Systems, Lecture Notes in Electrical Engineering, vol. 345. Springer, Cham (2014)
Xiong, Z., Nash, M.P., Cheng, E., Fedorov, V.V., Stiles, M.K., Zhao, J.: ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network. Physiol. Meas. 39(9), 094006 (2018)
Venkatesan, C., Karthigaikumar, P., Paul, A., Satheeskumaran, S., Kumar, R.: ECG signal preprocessing and SVM classifier-based abnormality detection in remote healthcare applications. IEEE Access 6, 9767–9773 (2018)
Ochiai, K., Takahashi, S., Fukazawa, Y.: Arrhythmia detection from 2-lead ECG using convolutional denoising autoencoders. In: Proceedings of International Conference on Knowledge Discovery and Data Mining, London, United Kingdom, pp. 1–7 (2018)
Karthik, R., Tyagi, D., Raut, A., Saxena, S., Kumar, R.: Implementation of neural network and feature extraction to classify ECG signals. In: Part of the Lecture Notes in Electrical Engineering Book Series, Microelectronics, Electromagnetics and Telecommunications, vol. 521, pp. 317–326 (2018)
Rangappa, V.G., Prasad, S.V.A.V., Agarwal, A.: Classification of cardiac arrhythmia stages using hybrid features extraction with K-nearest neighbor classifier of ECG signals. Int. J. Intell. Eng. Syst. 11(6), 21–32 (2018)
Namatēvs, I.: Deep convolutional neural networks: structure, feature extraction and training. Inf. Technol. Manag. Sci. 20, 40–47 (2017)
Jayasanthi, M., Muniraj, N.J.R.: ECG extraction by improved independent component analysis. Asian J. Inf. Technol. 15(15), 2638–2644 (2016)
Wisbeck, J.O., Barros, A.K., Yy, B., Ojeda, R.G.: Application of ICA in the separation of breathing artefacts in ECG signals. In: Proceedings of the International Conference on Neural Information Processing, pp. 211–214 (1998)
Barros, A.K., Mansour, A., Ohnishi, N.: Removing artefacts from electrocardiographic signals using independent components analysis. Neurocomputing 22(1–3), 173–186 (1998)
Romero, I.: PCA-based noise reduction in ambulatory ECGs. In: Proceedings of 2010 Computing in Cardiology, Belfast, pp. 677–680 (2010)
Sarfraz, M., Li, F.: Independent component analysis for motion artefacts removal from electrocardiogram. Open Artif. Intell. J. 1(4), 49–55 (2013)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Jayasanthi, M., Rajendran, G. & Vidhyakar, R.B. Independent component analysis with learning algorithm for electrocardiogram feature extraction and classification. SIViP 15, 391–399 (2021). https://doi.org/10.1007/s11760-020-01813-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-020-01813-1