Wavelet Transform and SVM Based Heart Disease Monitoring for Flexible Wearable Devices
DOI:
https://doi.org/10.4108/eetpht.10.5163Keywords:
wavelet transform, SVM, flexible wearable devices, heart diseaseAbstract
INTRODUCTION: Heart disease has been a major health challenge globally, therefore the development of reliable and real-time heart disease monitoring methods is crucial for the prevention and management of heart health. The aim of this study is to explore a flexible wearable device approach based on wavelet transform and support vector machine (SVM) to improve the accuracy and portability of heart disease monitoring.
OBJECTIVES: The main objective of this study is to develop a wearable device that combines wavelet transform and SVM techniques to achieve accurate monitoring of physiological signals of heart diseases.
METHODS: An integrated method for heart disease monitoring was constructed using flexible sensor technology combined with a wavelet transform and support vector machine. The Marr wavelet transform was applied to the ECG signals, and the feature vectors were constructed by feature parameter extraction. Then, the radial basis kernel SVM was utilized to identify the three ECG signals. The performance of the algorithm was optimized by adjusting the SVM parameters to improve the accurate monitoring of heart diseases.
RESULTS: The experimental results show that the proposed wavelet transform and SVM-based approach for flexible wearable devices achieves satisfactory results in heart disease monitoring. In particular, the algorithm successfully extracted feature vectors and accurately classified different ECG signals by skillfully combining the wavelet transform and SVM techniques for the processing of premature beat signals.
CONCLUSION: The potential application value of the wavelet transform and SVM-based flexible wearable device approach in heart disease monitoring is emphasized. By efficiently processing ECG signals, the method provides an innovative and comfortable solution for real-time monitoring of cardiac diseases.
Downloads
References
Liu, J.; Liu, M.; Bai, Y.; Zhang, J.; Liu, H.; Zhu, W. Recent Progress in Flexible Wearable Sensors for Vital Sign Monitoring. Sensors 2020, 20, 4009. DOI: https://doi.org/10.3390/s20144009
Shahandashti, P.F.; Pourkheyrollah, H.; Jahanshahi, A.; Ghafoorifard, H. Highly Conformable Stretchable Dry Electrodes Based on Inexpensive Flex Substrate for Long-Term Biopotential (EMG/ECG) Monitoring. Sens. Actuators A Phys. 2019, 295, 678–686. DOI: https://doi.org/10.1016/j.sna.2019.06.041
Xiong P, Xu M, Lei YJ. Recognition of premature ventricular contractions (PVCs) based on wavelet transform and BP neural network[J]. Chinese Journal of Medical Physics,2010,27(2):1762-1764.
Bong, K.-H. Comparison of SVM classification kernel function and parameter selection[J]. Computer Engineering and Applications,2011,47(3):123–125.
Zhan, Z.; Lin, R.; Tran, V.-T.; An, J.; Wei, Y.; Du, H.; Tran, T.; Lu, W. Paper/Carbon Nanotube-Based Wearable Pressure Sensor for Physiological Signal Acquisition and Soft Robotic Skin. acs Appl. Mater. Interfaces 2017, 9, 37921–37928. DOI: https://doi.org/10.1021/acsami.7b10820
Lim, C.L.; Byrne, C.; Lee, J.K. Human Thermoregulation and Measurement of Body Temperature in Exercise and Clinical Settings. Ann. acad. med. Snap. 2008, 37, 347–353. DOI: https://doi.org/10.47102/annals-acadmedsg.V37N4p347
Chen, X.; Ren, Z.; Guo, H.; Cheng, X.; Zhang, H. Self-Powered Flexible and Transparent Smart Patch for Temperature Sensing. Appl. Phys. Lett. 2020, 116, 043902. DOI: https://doi.org/10.1063/1.5134526
Haji, S.A.; Movahed, A. Right Ventricular Infarction-Diagnosis and Treatment. Clin. Cardiol. 2000, 23, 473-482. DOI: https://doi.org/10.1002/clc.4960230721
Huang, Y.; Song, Y.; Gou, L.; Zou, Y. A Novel Wearable Flexible Dry Electrode Based on Cowhide for ECG Measurement. Biosensors 2021, 11, 101. DOI: https://doi.org/10.3390/bios11040101
Jiang Xi, Zh Guoxing, Qin Jucun. A modulated signal recognition method based on wavelet neural network[J]. Journal of Guilin University of Electronic Science and Technology, 2012, 32 (2):122-124.
Qiao Zengwei, Sun Weixiang. A multi-class classifier based on support vector machine decision tree[J]. Computer Application and Software,2009,26(11):227-230.
Allison, R.D.; Holmes, E.L.; Nyboer, J. Volumetric Dynamics of Respiration as Measured by Electrical Impedance Plethysmography. j. Appl. Physiol. 1964, 19, 166–173. DOI: https://doi.org/10.1152/jappl.1964.19.1.166
Qi, J.; Lai, X.; Wang, J.; Tang, H.; Ren, H.; Yang, Y.; Jin, Q.; Zhang, L.; Yu, R.; Ma, G.; et al. Multi-Shelled Hollow Micro-/Nanostructures. Chem. Soc. Rev. . 2015, 44, 6749–6773. DOI: https://doi.org/10.1039/C5CS00344J
Wang, L.; Lou, Z.; Jiang, K.; Shen, G. Bio-Multifunctional Smart Wearable Sensors for Medical Devices. adv. intell. syst. 2019, 1, 1900040. DOI: https://doi.org/10.1002/aisy.201900040
Min, S.D.; Yun, Y.; Shin, H. Simplified Structural Textile Respiration Sensor Based on Capacitive Pressure Sensing Method. IEEE Sens. J. 2014, 14, 3245–3251. DOI: https://doi.org/10.1109/JSEN.2014.2327991
Akhmed-Zaki, D. Zh., Mukhambetzhanov, T. S., Nurmakhanova, Zh. M., & Abdiakhmetova, Z. M. (2021). Using wavelet transform and machine learning to predict heart fibrillation disease on ECG. IEEE. 57, 11-25 DOI: https://doi.org/10.1109/SIST50301.2021.9465990
Ambhore, S. Early detection of cardiovascular diseases using deep convolutional neural network & Fourier wavelet transform. Materials Today: Proceedings, 2021,p. 127, 121-155. DOI: https://doi.org/10.1016/j.matpr.2020.11.563
Nahak, S., Pathak, A., & Saha, G. (a). Fragment-level classification of ECG arrhythmia using wavelet scattering transform. Expert Systems With Applications, 2023,224, 120019. https://doi.org/10.1016/j.eswa.2023.120019 DOI: https://doi.org/10.1016/j.eswa.2023.120019
Venkatesh, S., Moffat, D., Kirke, A., Shakeri, G., Brewster, S., Fachner, J., Odell-Miller, H., Street, A., Farina, N., & Banerjee, S. Artificially synthesizing data for audio classification and segmentation to improve speech and music detection in a radio broadcast. 2021,313, 145–177. https://doi.org/10.1109/ICASSP39728.2021.9413597 DOI: https://doi.org/10.1109/ICASSP39728.2021.9413597
Saad, S., Kareem, D. H., & Jassim, M. H. EEG motor-imagery BCI system based on maximum overlap discrete wavelet transform (MODWT) and cubic SVM. 2021,27, 121-155
Sheng, Yonggang Zhang, K. Applications in bearing fault diagnosis of an improved Kurtogram algorithm based on flexible frequency slice wavelet transform filter bank. Measurement, 2021,174(1),33, 45–89. DOI: https://doi.org/10.1016/j.measurement.2021.108975
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Binbin Han, Fuliang Zhang, Lin Zhao
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
