Comparison of shallow and deep learning methods of ECG signals clas-sification for arrhythmia detection

A. Shakya, B. Gurung, M. S. Thapa, M. Rai, B. Joshi, Music classification based on genre and mood, International Conference On Computational Intelligence, Communications, and Business Analytics (2017) 168-183, https://doi.org/10.1007/978-981-10-6430-2_14.
DOI: https://doi.org/10.1007/978-981-10-6430-2_14   Google Scholar

R. F. R. Junior, F. A. D. Almeida, G. F. Gomes, Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks, Neural Computing and Applications 32(18) (2020) 15171-15189, https://doi.org/10.1007/s00521-020-04868-w.
DOI: https://doi.org/10.1007/s00521-020-04868-w   Google Scholar

M. K. Delimayanti, B. Purnama, N.G. Nguyen, M.R. Faisal, K. R. Mahmudah, F. Indriani, M. Kubo, K. Satou, Classification of brainwaves for sleep stages by high-dimensional FFT features from EEG signals, Applied Sciences. 10(5) (2020) 1797-1809, https://doi.org/10.3390/app10051797.
DOI: https://doi.org/10.3390/app10051797   Google Scholar

Z. Ebrahimi, M. Loni, M. Daneshtalab, A. Gharehbaghi, A review on deep learning methods for ECG arrhythmia classification, Expert Systems with Applications X 7 (2020), https://doi.org/10.1016/j.eswax.2020.100033.
DOI: https://doi.org/10.1016/j.eswax.2020.100033   Google Scholar

M. Kropf, D. Hayn, G. Schreier, ECG classification based on time and frequency domain features using random forests. In 2017 Computing in Cardiology (CinC) (2017) 1-4, https://doi.org/10.22489/CinC.2017.168-168.
DOI: https://doi.org/10.22489/CinC.2017.168-168   Google Scholar

O. M. A. Ali; S. W. Kareem, A. S.Mohammed, Evaluation of electrocardiogram signals classification using CNN, SVM, and LSTM algorithm: A review, In 2022 8th International Engineering Conference on Sustainable Technology and Development (IEC) (2022) 185-191, https://doi.org/10.1109/IEC54822.2022.9807511.
DOI: https://doi.org/10.1109/IEC54822.2022.9807511   Google Scholar

C. Venkatesan, P. Karthigaikumar, R. J. M. T. Varatharajan, A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection, Multimedia Tools and Applications 77(8) (2018) 10365-10374, https://doi.org/10.1007/s11042-018-5762-6.
DOI: https://doi.org/10.1007/s11042-018-5762-6   Google Scholar

B. Krithiga, P. Sabari, I. Jayasri, I. Anjali, Early detection of coronary heart disease by using naive bayes algorithm, In Journal of Physics: Conference Series Vol. 1717 No. 1 IOP Publishing (2021) 012-040, http://doi.org/10.1088/1742-6596/1717/1/012040.
DOI: https://doi.org/10.1088/1742-6596/1717/1/012040   Google Scholar

R. A. Cahya, C. Dewi, B. Rahayudi, Arrhythmia classification from electrocardiogram results using support vector machine with feature selection using genetic algorithms, Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (2018) 1170 – 1178.
  Google Scholar

T. Wang, C. Lu, Y. Sun, M. Yang, C. Liu, C. Ou, Automatic ECG classification using continuous wavelet transform and convolutional neural network, Entropy 23(1) (2021) 119-132, https://doi.org/10.3390/e23010119.
DOI: https://doi.org/10.3390/e23010119   Google Scholar

S. I. Dimitriadis, D. Liparas, How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer's disease, Neural Regeneration Research 13(6) (2018) 962-970, https://doi.org/10.4103%2F1673-5374.233433.
DOI: https://doi.org/10.4103/1673-5374.233433   Google Scholar

M. Dovbnych, M. P. Wójcik, A comparison of conventional and deep learning methods of image classification, Journal of Computer Sciences Institute (2021) 303-308, https://doi.org/10.35784/jcsi.2727.
DOI: https://doi.org/10.35784/jcsi.2727   Google Scholar

I. Tabian, H. Fu, Z. S. Khodaei, A convolutional neural network for impact detection and characterization of complex composite structures, Sensors, 19(22) (2019) 4933-4958, https://doi.org/10.3390/s19224933.
DOI: https://doi.org/10.3390/s19224933   Google Scholar

D. Li, J. Zhang, Q. Zhang, X. Wei, Classification of ECG signals based on 1D convolution neural network, 19th International Conference on e-Health Networking Applications and Services (Healthcom) (2017) 1-6, https://doi.org/10.1109/HealthCom.2017.8210784.
DOI: https://doi.org/10.1109/HealthCom.2017.8210784   Google Scholar

H. M. Lynn, S. B. Pan, P. Kim, A deep bidirectional GRU network model for biometric electrocardiogram classification based on recurrent neural networks, IEEE Access 7 (2019) 145395-145405, https://doi.org/10.1109/ACCESS.2019.2939947.
DOI: https://doi.org/10.1109/ACCESS.2019.2939947   Google Scholar