Open Access Open Access  Restricted Access Subscription or Fee Access

Wavelet-Based Relevance Vector Machines for Identification of Diseased Patterns in Plethysmographic Observations in Wrist Pulse


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irecap.v13i3.23233

Abstract


The morphology of the radial pulse enshrouds several patterns which correlate to normal and diseased conditions in the human body. This study explores the use of the Impedance Cardiovasography technique to bare the existence of eight such morphological contours from plethysmography observations on the radial pulse. Wavelet-based parallel Relevance Vector Machine (mRVM) architecture with Gaussian kernel achieves the highest accuracy of 87.27% as compared with Cauchy and spline kernels and also with principal components of the morphological patterns in the higher order space using an assortment of similar kernels. The results of the genotype are ably supported by several statistical parameters, including the Matthews Correlation Coefficient (MCC), Generalized Correlation Coefficient (GCC) and Kappa Coefficient, which provide the basis for the better performance of wavelet mRVM in comparison to the PCA technique. The higher sparsity achieved due to the wavelet features due to the reduction in the hyperparameters by 30% seals the fate of wavelets as the ideal classifier for radial pulse data. The morphological patterns are observed in normal subjects and those with heart, liver and lung diseases.
Copyright © 2023 Praise Worthy Prize - All rights reserved.

Keywords


Relevance Vector Machine; Impedance Cardiovasography; Morphological Patterns

Full Text:

PDF


References


Corciova, C.; Matei, D.; A. Design an Impedance Plethysmography System for Measuring Limb Blood Flow, IFMBE Proceedings,37: 157-160, 2012.
https://doi.org/10.1007/978-3-642-23508-5_42

Kubicek, W. G., Kottke, E. J., Ramos, F.J., Patterson, R.P., Witsoe, D.A., Labree J.W., Remole, W. The Minnesota impedance cardiograph theory and applications. Biomedical Engg. Pp.410-416.Vol9,1974.

Parmar, C. V., Prajapati, D. L., Chavda, V. V., Gokhale, P. A. Mehta, H. B. A Study of Cardiac Parameters using Impedance Plethysmography (IPG) in Healthy Volunteers. J. Phys. Pharm. Adv., Vol 11, Issue 2, pp 365-379, 2012.

Sri Vidhya, G., Nagarajan, R., Performance Analysis of Network Traffic Intrusion Detection System Using Machine Learning Technique, (2022) International Journal on Communications Antenna and Propagation (IRECAP), 12 (2), pp. 111-119.
https://doi.org/10.15866/irecap.v12i2.21724

Mora, E., Ordóñez Bueno, M., Gómez, C., Structural Vulnerability Assessment Procedure for Large Areas Using Machine Learning and Fuzzy Logic, (2021) International Review of Civil Engineering (IRECE), 12 (6), pp. 358-370.
https://doi.org/10.15866/irece.v12i6.19265

Al-Tarawneh, M., Muheilan, M., Al Tarawneh, Z., Hand Movement-Based Diabetes Detection Using Machine Learning Techniques, (2021) International Journal on Engineering Applications (IREA), 9 (4), pp. 234-242.
https://doi.org/10.15866/irea.v9i4.20616

Masoud, M., Jaradat, Y., Alsakarnah, R., A Non-Content Multilayers Hybrid Machine Learning Web Phishing Detection Model, (2022) International Review on Modelling and Simulations (IREMOS), 15 (2), pp. 108-115.
https://doi.org/10.15866/iremos.v15i2.21975

Bani Yassein, M., Shatnawi, M., Alomari, O., Users Awareness Prediction of Cyber Security Aspects in Twitter Using Machine Learning Algorithms, (2021) International Journal on Communications Antenna and Propagation (IRECAP), 11 (6), pp. 383-392.
https://doi.org/10.15866/irecap.v11i6.20725

Abidi, M., Fizazi, H., Boudali, N., Clustering of Remote Sensing Data Based on Spherical Evolution Algorithm, (2021) International Review of Aerospace Engineering (IREASE), 14 (2), pp. 72-79.
https://doi.org/10.15866/irease.v14i2.19209

Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE computational intelligence magazine, 1(4), 28-39.
https://doi.org/10.1109/MCI.2006.329691

Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.

Corciova, C.; Turnea, M.; Matei, D.; Andritoi, D. Evaluation of cardiac parameters using electrical impedance Plethysmography, Rev Med Chir Soc Med Nat Iasi., Vol 3, pp 327-332.

Guttke, S.; Laukner, M.; Weber, P. Cross sensitivity of different electrode types for impedance plethysmography under motion conditions, J. Phys.: Conf. Ser. 434 012028, 2012.
https://doi.org/10.1088/1742-6596/434/1/012028

Peripheral Pulse Analyzer, Internet: Available from:
http: www.larsentoubro.com.pdf

Tipping, M.: Sparse Bayesian Learning and the Relevance Vector Machine, Journal of Machine Learning Research. Vol 1, pp 211-244,2001.

Tipping, M. Bayesian Inference: An Introduction to Principles and Practice in Machine Learning. LCNS. S, pp 41-62,2004.
https://doi.org/10.1007/978-3-540-28650-9_3

Tipping, M., Faul, A., Fast marginal likelihood maximization for sparse Bayesian models. Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics. Vol 1, pp no 1-13, 2003.

Psorakis, I., Damoulas, T., Girolami, M.: Multiclass Relevance Vector Machines: Sparsity and Accuracy. IEEE Transactions on Neural Networks. Vol 10, pp 1588 -1598,2010.
https://doi.org/10.1109/TNN.2010.2064787

Gunn, S.: ISIS Technical Report: Support Vector Machines for Classification and Regression. Vol 11, pp 66, 1997.

Ye, G., Yin, H., Chen, T., Chen, H., Cui, L., & Zhang, X. FENet: a frequency extraction network for obstructive sleep apnea detection. IEEE Journal of Biomedical and Health Informatics, Vol 25, Issue 8, pp 2848-2856, 2021.
https://doi.org/10.1109/JBHI.2021.3050113

Naqvi, S. Z. H., Aziz, S., Khan, M. U., Abbas, M., Haider, A., & Hashmi, H. A. Electrocardiography based System for Characterization of Diabetes. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). IEEE, pp 1-6, 2020.
https://doi.org/10.1109/ICECCE49384.2020.9179452

Ukil, A., & Bandyopadhyay, S. Automated cardiac health screening using smartphone and wearable sensors through anomaly analytics. In Mobile Solutions and Their Usefulness in Everyday Life. Springer, Cham. pp. 145-172, 2019.
https://doi.org/10.1007/978-3-319-93491-4_8

Gravina, R., Alinia, P., Ghasemzadeh, H., & Fortino, G. Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges. Information Fusion, 35, pp 68-80, 2017.
https://doi.org/10.1016/j.inffus.2016.09.005

S. H. Karamchandani, U. B. Desai, S. N. Merchant, G. D. Jindal, Parallel Support Vector Architectures for Taxonomy of Radial Pulse Morphology, Signal, Image and Video Processing, 2012, pp. 1-16, ISSN 1863-1703.


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize