A Hybrid Approach of a Deep Learning Technique for Real–Time ECG Beat Detection
By:
References
- Abdel-Hamid, O., Mohamed, A.-r., Jiang, H., Deng, L., Penn, G. and Yu, D. (2014). Convolutional neural networks for speech recognition, IEEE/ACMTransactions on Audio, Speech, and Language Processing 22(10): 1533–1545.
- Acharya, U.R., Fujita, H., Lih, O.S., Adam, M., Tan, J.H. and Chua, C.K. (2017a). Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network, Knowledge-Based Systems 132(1): 62–71.
- Acharya, U.R., Fujita, H., Oh, S.L., Hagiwara, Y., Tan, J.H. and Adam, M. (2017b). Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals, Information Sciences 415(1): 190–198.
- Acharya, U.R., Fujita, H., Oh, S.L., Raghavendra, U., Tan, J.H., Adam, M., Gertych, A. and Hagiwara, Y. (2018). Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network, Future Generation Computer Systems 79(1): 952–959.
- Acharya, U.R., Krishnan, S.M., Spaan, J.A. and Suri, J.S. (2007). Advances in Cardiac Signal Processing, Springer, Berlin.
- Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M., Gertych, A. and San Tan, R. (2017c). A deep convolutional neural network model to classify heartbeats, Computers in Biology and Medicine 89(1): 389–396.
- Allam, J.P., Samantray, S., Behara, C., Kurkute, K.K. and Sinha, V.K. (2022). Customized deep learning algorithm for drowsiness detection using single-channel EEG signal, in V. Bajaj and G.R. Sinha (Eds), Artificial Intelligence- Based Brain-Computer Interface, Elsevier, Amsterdam, pp. 189–201.
- Asgharzadeh-Bonab, A., Amirani, M.C. and Mehri, A. (2020). Spectral entropy and deep convolutional neural network for ECG beat classification, Biocybernetics and Biomedical Engineering 40(2): 691–700.
- Bidias à Mougoufan, J.B., Eyebe Fouda, J.S.A., Tchuente, M. and Koepf, W. (2021). Three-class ECG beat classification by ordinal entropies, Biomedical Signal Processing and Control 67(1): 102506.
- Bodyanskiy, Y.V. and Tyshchenko, O.K. (2019). A hybrid cascade neuro-fuzzy network with pools of extended neo-fuzzy neurons and its deep learning, International Journal of Applied Mathematics and Computer Science 29(3): 477–488, DOI: 10.2478/amcs-2019-0035.
- CVDs (2021). Cardiovascular diseases, World Health Organization, Geneva, www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
- do Vale Madeiro, J.P., Marques, J.A.L., Han, T. and Pedrosa, R.C. (2020). Evaluation of mathematical models for QRS feature extraction and QRS morphology classification in ECG signals, Measurement 156(1): 107580.
- Elhaj, F.A., Salim, N., Harris, A.R., Swee, T.T. and Ahmed, T. (2016). Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals, Computer Methods and Programs in Biomedicine 127(1): 52–63.
- Faust, O., Hagiwara, Y., Hong, T.J., Lih, O.S. and Acharya, U.R. (2018). Deep learning for healthcare applications based on physiological signals: A review, Computer Methods and Programs in Biomedicine 161(1): 1–13.
- Hammad, M., Maher, A., Wang, K., Jiang, F. and Amrani, M. (2018). Detection of abnormal heart conditions based on characteristics of ECG signals, Measurement 125(1): 634–644.
- Hasan, N.I. and Bhattacharjee, A. (2019). Deep learning approach to cardiovascular disease classification employing modified ECG signal from empirical mode decomposition, Biomedical Signal Processing and Control 52(1): 128–140.
- Jiang, J., Zhang, H., Pi, D. and Dai, C. (2019). A novel multi-module neural network system for imbalanced heartbeats classification, Expert Systems with Applications X 1(1): 100003.
- Khalaf, A.F., Owis, M.I. and Yassine, I.A. (2015). A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines, Expert Systems with Applications 42(21): 8361–8368.
- Khorrami, H. and Moavenian, M. (2010). A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification, Expert Systems with Applications 37(8): 5751–5757.
- Kiranyaz, S., Ince, T. and Gabbouj, M. (2015). Real-time patient-specific ECG classification by 1-D convolutional neural networks, IEEE Transactions on Biomedical Engineering 63(3): 664–675.
- Kishore, N. and Singh, S. (2015). Cardiac analysis and classification of ECG signal using GA and NN, International Journal of Computer Applications 127(12): 23–27.
- Kowal, M., Skobel, M., Gramacki, A. and Korbicz, J. (2021). Breast cancer nuclei segmentation and classification based on a deep learning approach, International Journal of Applied Mathematics and Computer Science 31(1): 85–106, DOI: 10.34768/amcs-2021-0007.
- Kumar, K.S., Yazdanpanah, B. and Raju, D. (2014). Performance comparison of windowing techniques for ECG signal enhancement, International Journal of Engineering Research 3(12): 753–756.
- Li, H., Yuan, D., Wang, Y., Cui, D. and Cao, L. (2016). Arrhythmia classification based on multi-domain feature extraction for an ECG recognition system, Sensors 16(10): 1744.
- Li, T. and Zhou, M. (2016). ECG classification using wavelet packet entropy and random forests, Entropy 18(8): 285.
- Liu, Z., Meng, X., Cui, J., Huang, Z. and Wu, J. (2018). Automatic identification of abnormalities in 12-lead ECGs using expert features and convolutional neural networks, 2018 International Conference on Sensor Networks and Signal Processing (SNSP), Xi’an, China, pp. 163–167.
- Mandal, S., Mondal, P. and Roy, A.H. (2021). Detection of ventricular arrhythmia by using heart rate variability signal and ECG beat image, Biomedical Signal Processing and Control 68(1): 102692.
- Martis, R.J., Acharya, U.R., Lim, C.M. and Suri, J.S. (2013a). Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework, Knowledge-Based Systems 45(1): 76–82.
- Martis, R.J., Acharya, U.R. and Min, L.C. (2013b). ECG beat classification using PCA, LDA, ICA and discrete wavelet transform, Biomedical Signal Processing and Control 8(5): 437–448.
- Mathews, S.M., Kambhamettu, C. and Barner, K.E. (2018). A novel application of deep learning for single-lead ECG classification, Computers in Biology and Medicine 99(1): 53–62.
- Moody, G.B. and Mark, R.G. (2001). The impact of the MIT–BIH arrhythmia database, IEEE Engineering in Medicine and Biology Magazine 20(3): 45–50.
- Obeidat, Y. and Alqudah, A.M. (2021). A hybrid lightweight 1D CNN-LSTMarchitecture for automated ECG beat-wise classification, Traitement du Signal 38(5): 1281–1291.
- Oh, S.L., Ng, E.Y., San Tan, R. and Acharya, U.R. (2018). Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats, Computers in Biology and Medicine 102(1): 278–287.
- Pan, J. and Tompkins, W.J. (1985). A real-time QRS detection algorithm, IEEE Transactions on Biomedical Engineering BME-32(3): 230–236.
- Patro, K.K., Jaya Prakash, A., Jayamanmadha Rao, M. and Rajesh Kumar, P. (2020). An efficient optimized feature selection with machine learning approach for ECG biometric recognition, IETE Journal of Research 68(4): 1–12.
- Prakash, A.J., Samantray, S., Bala, C.L. and Narayana, Y. (2021). An automated diagnosis system for cardiac arrhythmia classification, in V. Bajaj and G.R. Sinha (Eds), Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare, CRC Press, Boca Raton, pp. 301–314.
- Raj, S. and Ray, K.C. (2018a). Automated recognition of cardiac arrhythmias using sparse decomposition over composite dictionary, Computer Methods and Programs in Biomedicine 165(1): 175–186.
- Raj, S. and Ray, K.C. (2018b). Sparse representation of ECG signals for automated recognition of cardiac arrhythmias, Expert Systems with Applications 105(1): 49–64.
- Rajagopal, R. and Ranganathan, V. (2017). Evaluation of effect of unsupervised dimensionality reduction techniques on automated arrhythmia classification, Biomedical Signal Processing and Control 34(1): 1–8.
- Rodríguez, R., Mexicano, A., Bila, J., Cervantes, S. and Ponce, R. (2015). Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis, Journal of Applied Research and Technology 13(2): 261–269.
- Sahay, S., Wadhwani, A., Wadhwani, S. and Bhadauria, S.S. (2019). Detection and classification of ECG signal through machine learning, International Journal of Innovative Technology and Exploring Engineering 8(10): 3221–3227.
- Sahoo, J.P., Prakash, A.J., Pławiak, P. and Samantray, S. (2022). Real-time hand gesture recognition using fine-tuned convolutional neural network, Sensors 22(3): 706.
- Sahoo, S., Dash, M., Behera, S. and Sabut, S. (2020). Machine learning approach to detect cardiac arrhythmias in ECG signals: A survey, Innovation and Research in BioMedical Engineering 41(4): 185–194.
- Sahoo, S., Mohanty, M., Behera, S. and Sabut, S.K. (2017). ECG beat classification using empirical mode decomposition and mixture of features, Journal of Medical Engineering & Technology 41(8): 652–661.
- Shadmand, S. and Mashoufi, B. (2016). A new personalized ecg signal classification algorithm using block-based neural network and particle swarm optimization, Biomedical Signal Processing and Control 25(1): 12–23.
- Shimpi, P., Shah, S., Shroff, M. and Godbole, A. (2017). A machine learning approach for the classification of cardiac arrhythmia, 2017 International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, pp. 603–607.
- Sinha, V.K.K., Patro, K.K.K., Pławiak, P. and Prakash, A.J.J. (2021). Smartphone-based human sitting behaviors recognition using inertial sensor, Sensors 21(19): 6652.
- Tan, J.H., Hagiwara, Y., Pang, W., Lim, I., Oh, S.L., Adam, M., San Tan, R., Chen, M. and Acharya, U.R. (2018). Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals, Computers in Biology and Medicine 94(1): 19–26.
- Uchaipichat, N., Thanawattano, C. and Buakhamsri, A. (2016). The development of ST-episode detection in Holter monitoring for myocardial ischemia, Procedia Computer Science 86(1): 188–191.
- Venkata Phanikrishna, B., Pławiak, P. and Jaya Prakash, A. (2021). A brief review on EEG signal pre-processing techniques for real-time brain-computer interface applications, TechRxiv: 16691605.
- Venkatesan, C., Karthigaikumar, P., Paul, A., Satheeskumaran, S. and Kumar, R. (2018). ECG signal preprocessing and SVM classifier-based abnormality detection in remote healthcare applications, IEEE Access 6(1): 9767–9773.
- Wu, Q., Sun, Y., Yan, H. and Wu, X. (2020). ECG signal classification with binarized convolutional neural network, Computers in Biology and Medicine 121(1): 103800.
- Xie, P., Wang, G., Zhang, C., Chen, M., Yang, H., Lv, T., Sang, Z. and Zhang, P. (2018). Bidirectional recurrent neural network and convolutional neural network (BIRCNN) for ECG beat classification, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, USA, pp. 2555–2558.
- Yang, W., Si, Y., Wang, D. and Guo, B. (2018). Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine, Computers in Biology and Medicine 101(1): 22–32.
Language: English
Page range: 455 - 465
Submitted on: Jan 27, 2022
Accepted on: Jun 10, 2022
Published on: Oct 8, 2022
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Keywords:
Related subjects:
© 2022 Kiran Kumar Patro, Allam Jaya Prakash, Saunak Samantray, Joanna Pławiak, Ryszard Tadeusiewicz, Paweł Pławiak, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.