A new approach for heart disease detection using Motif transform-based CWT’s time-frequency images with DenseNet deep transfer learning methods
-
Hazret Tekin
and
Yılmaz Kaya
Abstract
Objectives: Electrocardiogram (ECG) signals are extensively utilized in the identification and assessment of diverse cardiac conditions, including congestive heart failure (CHF) and cardiac arrhythmias (ARR), which present potential hazards to human health. With the aim of facilitating disease diagnosis and assessment, advanced computer-aided systems are being developed to analyze ECG signals. Methods: This study proposes a state-of-the-art ECG data pattern recognition algorithm based on Continuous Wavelet Transform (CWT) as a novel signal preprocessing model. The Motif Transformation (MT) method was devised to diminish the drawbacks and limitations inherent in the CWT, such as the issue of boundary effects, limited localization in time and frequency, and overfitting conditions. This transformation technique facilitates the formation of diverse patterns (motifs) within the signals. The patterns (motifs) are constructed by comparing the amplitudes of each individual sample value in the ECG signals in terms of their largeness and smallness. In the subsequent stage, the obtained one-dimensional signals from the MT transformation were subjected to CWT to obtain scalogram images. In the last stage, the obtained scalogram images were subjected to classification using DenseNET deep transfer learning techniques. Results and Conclusions: The combined approach of MT + CWT + DenseNET yielded an impressive success rate of 99.31 %.
-
Ethical approval: Not applicable.
-
Informed consent: Not applicable.
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: Authors state no conflict of interest.
-
Research funding: None.
-
Data availability: MIT-BIH and BIDMC databases datasets can be publicly downloaded from https://physionet.org/ under the Open Data Commons Attribution License v1.0.
References
1. Elhaj, FA, Salim, N, Harris, AR, Swee, TT, Ahmed, T. Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput Methods Progr Biomed 2016;127:52–63. https://doi.org/10.1016/j.cmpb.2015.12.024.Search in Google Scholar PubMed
2. Hu, X, Yuan, S, Xu, F, Leng, Y, Yuan, K, Yuan, Q. Scalp EEG classification using deep Bi-LSTM network for seizure detection. Comput Biol Med 2020;124:103919. https://doi.org/10.1016/j.compbiomed.2020.103919.Search in Google Scholar PubMed
3. Huang, S-H, Chuang, B-L, Lin, Y-H, Hung, C-S, Ma, H-P. A congestive heart failure detection system via multi-input deep learning networks. In: 2019 IEEE Global Communications Conference (GLOBECOM); 2019:1–6 pp.10.1109/GLOBECOM38437.2019.9013460Search in Google Scholar
4. Ponikowski, P, Voors, AA, Anker, SD, Bueno, H, Cleland, JGF, Coats, AJS, et al.. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Kardiol Pol 2016;74:1037–147. https://doi.org/10.5603/kp.2016.0141.Search in Google Scholar
5. Fürniss, HE, Stiller, B. Arrhythmic risk during pregnancy in patients with congenital heart disease. Herzschrittmachertherap Elektrophysiol 2021;32:174–9. https://doi.org/10.1007/s00399-021-00754-7.Search in Google Scholar PubMed
6. Alday, EAP, Gu, A, Shah, AJ, Robichaux, C, Wong, A-KI, Liu, C, et al.. Classification of 12-lead ecgs: the physionet/computing in cardiology challenge 2020. Physiol Meas 2020;41:124003. https://doi.org/10.1088/1361-6579/abc960.Search in Google Scholar PubMed PubMed Central
7. Ribeiro, AH, Ribeiro, MH, Paixão, GMM, Oliveira, DM, Gomes, PR, Canazart, JA, et al.. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun 2020;11:1760. https://doi.org/10.1038/s41467-020-15432-4.Search in Google Scholar PubMed PubMed Central
8. Kaya, Y, Kuncan, F, Tekin, R. A new approach for congestive heart failure and arrhythmia classification using angle transformation with LSTM. Arabian J Sci Eng 2022;47:10497–513. https://doi.org/10.1007/s13369-022-06617-8.Search in Google Scholar
9. Król-Józaga, B. Atrial fibrillation detection using convolutional neural networks on 2-dimensional representation of ECG signal. Biomed Signal Process Control 2022;74:103470. https://doi.org/10.1016/j.bspc.2021.103470.Search in Google Scholar
10. Lee, WK, Ratnam, MM, Ahmad, ZA. Detection of chipping in ceramic cutting inserts from workpiece profile during turning using fast Fourier transform (FFT) and continuous wavelet transform (CWT). Precis Eng 2017;47:406–23. https://doi.org/10.1016/j.precisioneng.2016.09.014.Search in Google Scholar
11. Kumari, CU, Murthy, ASD, Prasanna, BL, Reddy, MPP, Panigrahy, AK. An automated detection of heart arrhythmias using machine learning technique: SVM. Mater Today Proc 2021;45:1393–8. https://doi.org/10.1016/j.matpr.2020.07.088.Search in Google Scholar
12. Sahoo, S, Dash, M, Behera, S, Sabut, S. Machine learning approach to detect cardiac arrhythmias in ECG signals: a survey. Irbm 2020;41:185–94. https://doi.org/10.1016/j.irbm.2019.12.001.Search in Google Scholar
13. Menger, V, Scheepers, F, Spruit, M. Comparing deep learning and classical machine learning approaches for predicting inpatient violence incidents from clinical text. Appl Sci 2018;8:981. https://doi.org/10.3390/app8060981.Search in Google Scholar
14. Rawat, S, Srinivasan, A, Ravi, V, Ghosh, U. Intrusion detection systems using classical machine learning techniques vs integrated unsupervised feature learning and deep neural network. Internet Technol Lett 2022;5:e232. https://doi.org/10.1002/itl2.232.Search in Google Scholar
15. Çalışkan, A. A new ensemble approach for congestive heart failure and arrhythmia classification using shifted one-dimensional local binary patterns with long short-term memory. Comput J 2022;65:2535–46. https://doi.org/10.1093/comjnl/bxac087.Search in Google Scholar
16. Salem, M, Taheri, S, Yuan, J. ECG arrhythmia classification using transfer learning from 2-dimensional deep CNN features. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS); 2018:1–4 pp.10.1109/BIOCAS.2018.8584808Search in Google Scholar
17. Saputra, AD, Hindarto, D, Santoso, H. Disease classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201. Sinkron: Jurnal Dan Penelitian Teknik Informatika 2023;8:48–55. https://doi.org/10.33395/sinkron.v8i1.11906.Search in Google Scholar
18. Guo, W, Xu, Z, Zhang, H. Interstitial lung disease classification using improved DenseNet. Multimed Tool Appl 2019;78:30615–26. https://doi.org/10.1007/s11042-018-6535-y.Search in Google Scholar
19. Kim, CH, Aggarwal, R. Wavelet transforms in power systems. Part 1: general introduction to the wavelet transforms. Power Eng J 2000;14:81–7. https://doi.org/10.1049/pe:20000210.10.1049/pe:20000210Search in Google Scholar
20. Gdeisat, MA, Abid, A, Burton, DR, Lalor, MJ, Lilley, F, Moore, C, et al.. Spatial and temporal carrier fringe pattern demodulation using the one-dimensional continuous wavelet transform: recent progress, challenges, and suggested developments. Opt Laser Eng 2009;47:1348–61. https://doi.org/10.1016/j.optlaseng.2009.07.009.Search in Google Scholar
21. Wu, J-D, Liu, C-H. An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network. Expert Syst Appl 2009;36:4278–86. https://doi.org/10.1016/j.eswa.2008.03.008.Search in Google Scholar
22. Rahul, J, Sharma, LD. Artificial intelligence-based approach for atrial fibrillation detection using normalised and short-duration time-frequency ECG. Biomed Signal Process Control 2022;71:103270. https://doi.org/10.1016/j.bspc.2021.103270.Search in Google Scholar
23. Mohonta, SC, Motin, MA, Kumar, DK. Electrocardiogram based arrhythmia classification using wavelet transform with deep learning model. Sensing and Bio-Sensing Research 2022;37:100502. https://doi.org/10.1016/j.sbsr.2022.100502.Search in Google Scholar
24. Panganiban, EB, Paglinawan, AC, Chung, WY, Paa, GLS. ECG diagnostic support system (EDSS): a deep learning neural network based classification system for detecting ECG abnormal rhythms from a low-powered wearable biosensors. Sensing and Bio-Sensing Research 2021;31:100398. https://doi.org/10.1016/j.sbsr.2021.100398.Search in Google Scholar
25. Madan, P, Singh, V, Singh, DP, Diwakar, M, Pant, B, Kishor, A. A hybrid deep learning approach for ECG-based arrhythmia classification. Bioengineering 2022;9:152. https://doi.org/10.3390/bioengineering9040152.Search in Google Scholar PubMed PubMed Central
26. Huang, J, Chen, B, Yao, B, He, W. ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network. IEEE Access 2019;7:92871–80. https://doi.org/10.1109/access.2019.2928017.Search in Google Scholar
27. Ozaltin, O, Yeniay, O. A novel proposed CNN-SVM architecture for ECG scalograms classification. Soft Comput 2023;27:4639–58. https://doi.org/10.1007/s00500-022-07729-x.Search in Google Scholar PubMed PubMed Central
28. 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. https://doi.org/10.1016/j.bspc.2020.102326.Search in Google Scholar
29. Goldberger, AL, Amaral, LAN, Glass, L, Hausdorff, JM, Ivanov, PC, Mark, RG, et al.. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101:e215–20. https://doi.org/10.1161/01.cir.101.23.e215.Search in Google Scholar PubMed
30. Moody, GB, Mark, RG. The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag 2001;20:45–50. https://doi.org/10.1109/51.932724.Search in Google Scholar PubMed
31. Kaya, Y, Ertuğrul, ÖF. Estimation of neurological status from non-electroencephalography bio-signals by motif patterns. Appl Soft Comput 2019;83:105609. https://doi.org/10.1016/j.asoc.2019.105609.Search in Google Scholar
32. Kuncan, F, Kaya, Y, Yiner, Z, Kaya, M. A new approach for physical human activity recognition from sensor signals based on motif patterns and long-short term memory. Biomed Signal Process Control 2022;78:103963. https://doi.org/10.1016/j.bspc.2022.103963.Search in Google Scholar
33. Mateo, C, Talavera, JA. Bridging the gap between the short-time Fourier transform (STFT), wavelets, the constant-Q transform and multi-resolution STFT. Signal, Image and Video Processing 2020;14:1535–43. https://doi.org/10.1007/s11760-020-01701-8.Search in Google Scholar
34. Byeon, Y-H, Pan, S-B, Kwak, K-C. Ensemble deep learning models for ECG-based biometrics. In: 2020 Cybernetics & Informatics (K&I); 2020:1–5 pp.10.1109/KI48306.2020.9039871Search in Google Scholar
35. Narin, A. Detection of focal and non-focal epileptic seizure using continuous wavelet transform-based scalogram images and pre-trained deep neural networks. Innov Res Biomed Eng 2022;43:22–31. https://doi.org/10.1016/j.irbm.2020.11.002.Search in Google Scholar
36. Zhang, J, Lu, C, Li, X, Kim, H-J, Wang, J. A full convolutional network based on DenseNet for remote sensing scene classification. Math Biosci Eng 2019;16:3345–67. https://doi.org/10.3934/mbe.2019167.Search in Google Scholar PubMed
37. Chen, B, Zhao, T, Liu, J, Lin, L. Multipath feature recalibration DenseNet for image classification. Int J Mach Learn Cyber 2021;12:651–60. https://doi.org/10.1007/s13042-020-01194-4.Search in Google Scholar
38. Huang, G, Liu, Z, Van Der Maaten, L, Weinberger, KQ. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017:4700–8 pp.10.1109/CVPR.2017.243Search in Google Scholar
39. Chauhan, T, Palivela, H, Tiwari, S. Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging. Int J Inf Manage Data Insights 2021;1:100020. https://doi.org/10.1016/j.jjimei.2021.100020.Search in Google Scholar
40. Bian, W, Wang, J, Zhuang, B, Yang, J, Wang, S, Xiao, J. Audio-based music classification with DenseNet and data augmentation. In: PRICAI 2019: Trends in Artificial Intelligence: 16th Pacific Rim International Conference on Artificial Intelligence, vol 16. Cuvu, Yanuca Island, Fiji; 2019:56–65 pp. August 26–30, 2019, Proceedings, Part III.Search in Google Scholar
41. Isin, A, Ozdalili, S. Cardiac arrhythmia detection using deep learning. Proc Comput Sci 2017;120:268–75. https://doi.org/10.1016/j.procs.2017.11.238.Search in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Review
- Actuators and transmission mechanisms in rehabilitation lower limb exoskeletons: a review
- Research Articles
- Ergonomic RFID tag placement on surgical instruments – a preliminary user study
- Medical textile implants: hybrid fibrous constructions towards improved performances
- Effect of calcium phosphate/bovine serum albumin coated Al2O3–Ti biocomposites on osteoblast response
- Self-supervised context-aware correlation filter for robust landmark tracking in liver ultrasound sequences
- Assessment of brain tumor detection techniques and recommendation of neural network
- A new approach for heart disease detection using Motif transform-based CWT’s time-frequency images with DenseNet deep transfer learning methods
- Structural EEG signal analysis for sleep apnea classification
Articles in the same Issue
- Frontmatter
- Review
- Actuators and transmission mechanisms in rehabilitation lower limb exoskeletons: a review
- Research Articles
- Ergonomic RFID tag placement on surgical instruments – a preliminary user study
- Medical textile implants: hybrid fibrous constructions towards improved performances
- Effect of calcium phosphate/bovine serum albumin coated Al2O3–Ti biocomposites on osteoblast response
- Self-supervised context-aware correlation filter for robust landmark tracking in liver ultrasound sequences
- Assessment of brain tumor detection techniques and recommendation of neural network
- A new approach for heart disease detection using Motif transform-based CWT’s time-frequency images with DenseNet deep transfer learning methods
- Structural EEG signal analysis for sleep apnea classification
