An Upgraded Entropy and Fractal Investigation of HRV Signal for Identification of Heart Dynamics-A Multiscale Methodology
Keywords:
WGN, ANS, HRV, ImDistEnAbstract
It's possible that the increased popularity of biomedical engineering is due in part to a number of variables, such as how easy it is to gather the data, how little bandwidth it needs for power-efficient telemetry, and how much interest there is currently in the field. Because of the potential influence that HRV research could have on the health of the autonomic nervous system, both conventional medicine and complementary and alternative medicine have given it a lot of attention (ANS). In order to solve the issue of instability as well as a high level of sensitivity to pre-determined parameters and the duration of the data, a one-of-a-kind multiscale enhanced distribution entropy (ImDistEn) has been developed. In order to offer a more accurate evaluation of the vectors' distribution in phase space, L1-norm distance is employed in conjunction with the ordinal and orientation similarity of embedded vectors. [Case in point:] [Case in point:] [Case in point:] [Cas The proposed ImDistEn parameter has the ability to differentiate between a wide range of synthetic signals, including white Gaussian noise (WGN), chaotic signals (based on both the Logistic map and the two-dimensional Henon map), MIX processes, fractal time series (with varying Hurst exponents), and pink noise at a number of different scales. After being tested on three different HRV datasets, it was discovered that the performance of the algorithm on real-world signals was consistent.
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Boon, K. H., Khalil-Hani, M., and Malarvili, M. B. Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III. Computer Methods and Programs in Biomedicine, 153:171–184, 2018.
Chandrashekar, G. and Sahin, F. A survey on feature selection methods. Computers and Electrical Engineering, 40:16–28, 2014.
Jiang, F., Zhu, Z., and Li, W. An improved VMD with empirical mode decomposition and its application in incipient fault detection of rolling bearing. IEEE Access, 6:44483–44493, 2018.
Jiang, S., Bian, C., Ning, X., and Ma, Q. D. Y. Visibility graph analysis on heartbeat dynamics of meditation training. Applied Physics Letters, 102 (25):1–3, 2013.
Koichubekov, B., Sorokina, M., Laryushina, Y., Luydmila, T., and Korshukov, I. Nonlinear analyses of heart rate variability in hypertension. Annales de Cardiologie et D'Ang_eiologie, 67(3):174–179, 2018.
Kumar, A., Tomar, H., Mehla, V. K., Komaragiri, R., and Kumar, M. Stationary wavelet transform based ECG signal denoising method. ISA Transactions, 114:251–262, 2021.
Lan, K.-C., Raknim, P., Kao, W.-F., and Huang, J.-H. Toward hypertension prediction based on PPG-derived HRV signals: a feasibility study. Journal of Medical Systems, 42(6):1–7, 2018.
Mohebbian, M. R., Alam, M. W., A.Wahid, K., and Dinh, A. Single channel high noise level ECG deconvolution using optimized blind adaptive filtering and fixed-point convolution kernel compensation. Biomedical Signal Processing and Control, 57:1–11, 2020.
Narin, A., Isler, Y., Ozer, M., and Perc, M. Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability. Physica A, 509: 56–65, 2018.
Vijayarangan, S., Vignesh, R., Murugesan, B., Preejith, P. S., Joseph, J., and Sivaprakasam, M. RPnet: A deep learning approach for robust R peak detection in noisy ECG. In 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pages 345–348, 2020.
Villa, A., Padhy, S., Willems, R., Huffel, S. V., and Varon, C. Variational mode decomposition features for heartbeat classification. In Computing in Cardiology Conference (CinC), pages 1–4, Maastricht, Netherlands, 2018.
Chen, Y.-S.; Lu, W.-A.; Pagaduan, J.C.; Kuo, C.-D. A Novel Smartphone App for the Measurement of Ultra-Short-Term and Short-Term Heart Rate Variability: Validity and Reliability Study. JMIR mHealth uHealth 2020, 8, e18761.
Wehler, D.; Jelinek, H.F.; Gronau, A.; Wessel, N.; Kraemer, J.F.; Krones, R.; Penzel, T. Reliability of Heart-Rate-Variability Features Derived from Ultra-Short ECG Recordings and Their Validity in the Assessment of Cardiac Autonomic Neuropathy. Biomed. Signal. Process. Control 2021, 68, 102651.
Moya-Ramon, M.; Mateo-March, M.; Peña-González, I.; Zabala, M.; Javaloyes, A. Validity and Reliability of Different Smartphones Applications to Measure HRV during Short and Ultra-Short Measurements in Elite Athletes. Comput. Methods Programs Biomed. 2022, 217, 106696.
H. F. Jelinek, D. J. Cornforth, M. P. Tarvainen, and K. Khalaf, “Investigation of Linear and Nonlinear Properties of a Heartbeat Time Series Using Multiscale Rényi Entropy,” Entropy 2019, Vol. 21, Page 727, vol. 21, no. 8, p. 727, Jul. 2019.
C. H. Lee, H. W. Shin, and D. G. Shin, “Impact of Oxidative Stress on Long-Term Heart Rate Variability: Linear Versus Non-Linear Heart Rate Dynamics,” Hear. Lung Circ., vol. 29, no. 8, pp. 1164–1173, 2020.
Yao, W., Zhang, Y., and Wang, J. Quantitative analysis in nonlinear dynamic complexity detection of meditative heart beats. Physica A, 512:1060–1068, 2018.
Castaldo, R., Melillo, P., Izzo, R., Luca, N. D., and Pecchia, L. Fall prediction in hypertensive patients via short-term HRV analysis. IEEE Jounal of Biomedical and Health Informatics, 21(2):399–406, 2017.
Choi, A. and Shin, H. Quantitative analysis of the effect of an ectopic beat on the heart rate variability in the resting condition. Frontiers in Physiology, 9:1–10, 2018.
Chen, Z., Li, Y., Liang, H., and Yu, J. Improved permutation entropy for measuring complexity of time series under noisy condition. Complexity, 2019:1–12, 2019.
Li, Y., Pan, W., Li, K., Jiang, Q., and Liu, G. Sliding trend fuzzy approximate entropy as a novel descriptor of heart rate variability in obstructive sleep apnea. IEEE Journal of Biomedical and Health Informatics, 23(1):175–183, 2019.
Liu, C. and Gao, R. Multiscale entropy analysis of the differential RR interval time series signal and its application in detecting congestive heart failure. Entropy, 19:1–19, 2017.
Ni, H., Cho, S., Mankoff, J., Yang, J., and Dey, A. K. Automated recognition of hypertension through overnight continuous HRV monitoring. Journal of Ambient Intelligence and Humanized Computing, 9:2011–2023, 2018.
Karthick, S. ., Shankar, P. V. ., Jayakumar, T. ., Suba, G. M. ., Quadir, M. ., & Paul Roy, A. T. . (2023). A Novel Approach for Integrated Shortest Path Finding Algorithm (ISPSA) Using Mesh Topologies and Networks-on-Chip (NOC). International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 87–95. https://doi.org/10.17762/ijritcc.v11i2s.6032
Basaligheh, P. (2021). A Novel Multi-Class Technique for Suicide Detection in Twitter Dataset. Machine Learning Applications in Engineering Education and Management, 1(2), 13–20. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/14
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