## 5 Application, Challenges, and Future Directions  
In biomedical signal processing, various methods are employed for detecting changes in signals, each with distinct advantages and applications. Classical statistical techniques, such as mean and standard deviation calculations, are commonly used due to their simplicity and robustness, making them suitable for scenarios where interpretability is crucial. Time-Frequency analysis methods, including the Continuous Wavelet Transform (CWT) and short-time Fourier transform (STFT), are valuable for analyzing dynamic physiological processes, offering insights into both time and frequency domains. These methods are particularly useful in applications such as EEG analysis, where understanding the temporal dynamics of brain activity is essential. Machine learning algorithms, such as Support Vector Machines (SVMs) and deep neural networks (DNN), provide high accuracy and automation, making them ideal for large-scale data analysis and real-time monitoring in clinical settings [47; 48; 49].  
Despite their effectiveness, each change detection method in biomedical signals faces specific challenges. Classical statistical techniques often struggle with noisy and high-dimensional data, limiting their applicability in complex biomedical signal environments. Time-frequency analysis methods, while powerful, require careful parameter tuning and can be computationally intensive, which may pose challenges for real-time applications. Machine learning algorithms, despite their high accuracy, often lack interpretability and require large amounts of labeled data for training, which can be a significant barrier in biomedical contexts where data labeling is costly and time-consuming. Bayesian inference methods, which incorporate prior knowledge and uncertainty modeling, are computationally demanding and sensitive to the choice of prior distributions, making their implementation complex. Furthermore, integrating multimodal data, such as combining EEG with functional magnetic resonance imaging (fMRI), offers complementary information but introduces challenges related to data alignment, synchronization, and sophisticated data fusion techniques.  
Advancements in signal processing and machine learning continue to drive innovation in change detection methods. Techniques such as compressive sensing offer the ability to efficiently sample and reconstruct signals from limited data, although they require careful selection of sensing matrices and sparsity constraints. Phase-space reconstruction methods provide insights into the underlying dynamics of physiological systems but may be sensitive to noise and require parameter optimization. Future research should focus on developing more robust algorithms capable of handling noisy and high-dimensional data, improving the interpretability of machine learning models, and enhancing data fusion techniques for multimodal signals. By leveraging the complementary strengths of various methods and addressing their limitations, researchers can advance our understanding of physiological processes and improve healthcare outcomes. The integration of advanced signal processing techniques, machine learning algorithms, and Bayesian inference methodshas the potential to revolutionize signal change detection, unlocking new insights into human health and disease.  
This research contributes to the advancement of biomedical signal processing by providing a comprehensive evaluation of different change detection methods and highlighting their strengths and limitations. The findings have implications for improving clinical decision-making and advancing healthcare technology.