## 1 Introduction
By addressing these challenges, researchers aim to enhance the reliability and efficacy of change detection methods in biomedical signals, ultimately improving healthcare outcomes and advancing our understanding of human physiology and disease. In comparison to classical statistical techniques, time-frequency analysis and machine learning algorithms offer several advantages [43; 44; 45; 46]:  
* **Enhanced sensitivity:** These methods can detect subtle changes that may be missed by classical techniques. * **Robustness to noise:** They are less sensitive to noise and artifacts, which is common in biomedical signals. * **Ability to handle complex signals:** They can effectively handle non-linear and non-stationary signals. * **Scalability:** They can handle large datasets and high-dimensional data. However, these methods also have their limitations:
* **Computational complexity:** Time-Frequency analysis and machine learning algorithms can be computationally expensive, especially for large datasets. * **Parameter tuning:** These methods often require careful tuning of parameters to achieve optimal performance. * **Interpretability:** The results of these methods can be difficult to interpret, especially for complex models. Here is the schematic diagram visualizing the application of classical statistical techniques, Continuous Wavelet Transform (CWT), and Support Vector Machine (SVM) for change detection in biomedical signals (see Figure 1). In this comprehensive study, we explore the methodologies, applications, and challenges of change detection in biomedical signals, underscoring its critical importance in advancing our understanding of human physiology, improving clinical decision-making, and fostering innovations in healthcare technology. In Section 2 we describe three methods for detecting changes in nonlinear biomedical signals: classical statistical techniques, time-frequency analysis using Continuous Wavelet Transform (CWT), and machine learning with Support Vector Machines (SVMs). In Section 3 we employ a multifaceted approach to change detection, incorporating classical statistical techniques to identify significant variations and patterns within the data. Time-Frequency analysis is utilized to dissect the complex temporal and spectral characteristics of biomedical signals, enabling a more detailed understanding of dynamic processes. Additionally, we leverage machine learning algorithms, such as Support Vector Machines (SVMs), to enhance the accuracy and robustness of change detection. These algorithms are particularly adept at handling high-dimensional data and identifying subtle, non-linear patterns that might elude traditional methods. In Section 4 we discuss the results obtained in Section 3. In Section 5, we continue our discussion by providing application, challenges, and future directions for each method. In Section 6, we provide a comprehensive discussion of the strengths and limitations of each approach. By applying these diverse and complementary techniques, this study lays the foundation for further exploration and discovery in this dynamic and interdisciplinary field, paving the way for more precise and reliable biomedical signal analysis.