## 1 Introduction
An inappropriate wavelet function can lead to suboptimal performance, and the complexity of the signal can challenge the CWT's ability to accurately detect changes. Therefore, careful tuning of parameters is necessary to balance sensitivity and specificity. The time-frequency analysis with CWT offers a more dynamic approach by decomposing signals into time-varying frequency components. Its success depends on selecting the appropriate wavelet function and threshold. The choice of wavelet function is critical, as different wavelets capture different signal features. Inappropriate wavelet selection can lead to poor change detection. Similarly, setting the right threshold is essential; an improper threshold can miss subtle changes or amplify noise, causing false positives. While CWT can capture complex signal dynamics, its effectiveness can be compromised by the inherent complexity and variability of the signal. Another promising approach is the use of deep learning models for signal change detection. Deep neural networks, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown remarkable success in analyzing complex patterns and extracting hierarchical features from biomedical signals [28, 29, 30, 31]. By leveraging large-scale datasets and hierarchical representations, deep learning models can automatically learn discriminative features and detect subtle changes in signals with high accuracy. Moreover, techniques such as transfer learning and adversarial training enhance the generalization and robustness of deep learning models, making them valuable tools for signal change detection in biomedical applications. Machine learning algorithms, such as Support Vector Machines (SVMs), represent a powerful tool for change detection in biomedical signals. These methods leverage sophisticated feature extraction and pattern recognition capabilities to detect changes. The effectiveness of SVMs, however, depends on the representation of features and the availability of informative training data. In scenarios with complex signal dynamics and subtle changes, traditional SVM approaches may struggle without adequate feature engineering. Recent advancements suggest that integrating deep learning techniques, such as convolutional neural networks (CNNs)and hybrid models combining CNNs with transformers, can significantly enhance the performance of SVMs in detecting changes in biomedical signals [32; 33; 34]. Furthermore, Bayesian inference methods offer a principled framework for modeling uncertainty and incorporating prior knowledge into signal change detection algorithms. Bayesian approaches, such as Bayesian filtering, probabilistic graphical models, and Bayesian networks, provide probabilistic representations of signal dynamics and enable the integration of domain knowledge and prior beliefs [35]. By explicitly modeling uncertainty and incorporating prior information, Bayesian inference methods enhance the reliability and interpretability of signal change detection results, facilitating more informed decision-making in clinical practice and biomedical research. Despite significant progress, challenges persist in the field of change detection in biomedical signals. These challenges include the development of robust algorithms capable of handling noisy and high-dimensional data, addressing inter-subject variability, and ensuring the interpretability and generalizability of results across diverse populations [36; 37; 38]. Additionally, the integration of multimodal data and the incorporation of domain knowledge present exciting avenues for future research and innovation in the field [39]. Efforts to overcome these challenges involve leveraging advanced machine learning techniques, such as deep learning and reinforcement learning, as well as exploring novel signal processing approaches, such as compressive sensing and phase-space reconstruction [40; 41; 42].