Abstract
In recent years, the Slope Entropy (SlopEn) algorithm has been recognized as a critical tool for Time Series Analysis and Time Series Classification, particularly within biomedical signal processing. This paper presents a thorough literature review on the developments, applications, and advancements of the Slope Entropy algorithm, drawing extensively from the seminal work of Dr. David Cuesta Frau in 2019. By meticulously examining the existing literature, we discuss the algorithm's potential for unveiling intricate dynamics inherent in time series data and its capability for enhancing signal quality recognition. We also explore the algorithm’s adaptability across various domains beyond biomedical applications, including finance and environmental monitoring. Furthermore, we identify potential areas of improvement, such as computational efficiency and real-time processing capabilities, which could pave the way for novel applications and methodologies. This review culminates in providing a clear roadmap for researchers aiming to employ the Slope Entropy algorithm in novel settings, contributing to its continuous evolution and broader acceptance in the scientific community.
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References
Bandt, C., Pompe, B.: Permutation entropy: a natural complexity measure for time series. Phys. Rev. Lett. 88(17), 174102 (2002)
Cuesta-Frau, D.: Slope entropy: a new time series complexity estimator based on both symbolic patterns and amplitude information. Entropy 21(12), 1167 (2019). https://doi.org/10.3390/e21121167
Li, Y., Tang, B., Huang, B., Xue, X.: A dual-optimization fault diagnosis method for rolling bearings based on hierarchical slope entropy and SVM synergized with shark optimization algorithm. Sensors 23(12), 5630 (2023). https://doi.org/10.3390/s23125630
Shi, E.: Single feature extraction method of bearing fault signals based on slope entropy. Shock. Vib. 2022, 1–9 (2022). https://doi.org/10.1155/2022/6808641
Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948). https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol.-Heart Circul. Physiol. 278(6), H2039–H2049 (2000). https://doi.org/10.1152/ajpheart.2000.278.6.H2039
Li, Y., Gao, P., Tang, B., Yi, Y., Zhang, J.: Double feature extraction method of ship-radiated noise signal based on slope entropy and permutation entropy. Entropy 24(1), 22 (2021). https://doi.org/10.3390/e24010022
Cuesta-Frau, D., Varela-Entrecanales, M., Molina-Picó, A., Vargas, B.: Patterns with equal values in permutation entropy: do they really matter for biosignal classification? Complexity 2018, 1–15 (2018). https://doi.org/10.1155/2018/1324696
Cuesta-Frau, D., Dakappa, P.H., Mahabala, C., Gupta, A.R.: Fever time series analysis using slope entropy. Application to early unobtrusive differential diagnosis. Entropy 22(9), 1034 (2020). https://doi.org/10.3390/e22091034
Sousa, H., Ribeiro, M., Henriques, T.S.: Entropy analysis of total respiratory time series for sepsis detection. In: 2022 E-Health and Bioengineering Conference (EHB), pp. 01–06. IEEE, November 2022. https://doi.org/10.1109/EHB55594.2022.9991277
Kouka, M., Cuesta-Frau, D.: Slope entropy characterisation: the role of the δ parameter. Entropy 24(10), 1456 (2022). https://doi.org/10.3390/e24101456
Cuesta-Frau, D., Schneider, J., Bakštein, E., Vostatek, P., Spaniel, F., Novák, D.: Classification of actigraphy records from bipolar disorder patients using slope entropy: a feasibility study. Entropy 22(11), 1243 (2020). https://doi.org/10.3390/e22111243
Vargas, B., et al.: Discriminating bacterial infection from other causes of fever using body temperature entropy analysis. Entropy 24(4), 510 (2022). https://doi.org/10.3390/e24040510
Kouka, M., Cuesta-Frau, D.: Slope entropy characterisation: adding another interval parameter to the original method. In: ITISE 2023, Basel Switzerland: MDPI, July 2023, p. 67 (2023). https://doi.org/10.3390/engproc2023039067
Cuesta-Frau, D., Kouka, M., Silvestre-Blanes, J., Sempere-Payá, V.: Slope entropy normalisation by means of analytical and heuristic reference values. Entropy 25(1), 66 (2022). https://doi.org/10.3390/e25010066
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This work has been supported by Universitat Politècnica de València, research project PAID-06–22.
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Seguí Moreno, J., Molina Picó, A. (2024). A Comprehensive Literature Review on Slope Entropy Algorithm: Bridging Past Insights with Future Directions. In: Arai, K. (eds) Intelligent Computing. SAI 2024. Lecture Notes in Networks and Systems, vol 1018. Springer, Cham. https://doi.org/10.1007/978-3-031-62269-4_10
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DOI: https://doi.org/10.1007/978-3-031-62269-4_10
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