A Comprehensive Literature Review on Slope Entropy Algorithm: Bridging Past Insights with Future Directions

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.