## 1 Introduction  
In the area of biomedical signal processing, detecting changes in signals is paramount for understanding physiological processes, diagnosing medical conditions, and developing effective treatments [1, 2, 3, 4]. Biomedical signals, encompassing a wide range of modalities, such as electrocardiography (ECG), electromyography (EMG), electroencephalography (EEG), and more, encode valuable information about the functioning of biological systems [5, 6, 7] However, these signals are often complex, dynamic, and prone to various sources of noise and artifacts. Detecting changes in biomedical signals is thus essential for uncovering meaningful patterns, identifying anomalies, and extracting clinically relevant information [8, 9, 10]. The field of change detection in biomedical signals encompasses a diverse array of methodologies, ranging from classical statistical techniques to modern machine learning algorithms [11, 12]. These methodologies aim to distinguish between normal physiological variations and abnormal deviations, providing insights into health status, disease progression, and treatment efficacy. By leveraging advanced signal processing and pattern recognition techniques, researchers and clinicians can effectively monitor, analyze, and interpret biomedical signals in real-time, facilitating early detection, diagnosis, and intervention [12, 13]. The application of change detection in biomedical signals spans across numerous domains of healthcare and biomedical research [11, 12]. In cardiology [13, 14, 15], for instance, detecting changes in ECG signals enables the early detection of cardiac arrhythmias, ischemic events, and heart failure, guiding patient management and therapy selection [13]. Similarly, in neurology, change detection in EEG signals [16, 17] aids in diagnosing epilepsy, monitoring brain activity during cognitive tasks, and studying neurological disorders, such as Alzheimer's disease and Parkinson's disease [7, 18, 19, 20]. Classical statistical techniques for change detection, such as those based on the mean and standard deviation of the signal, are widely used due to their simplicity and ease of implementation. These methods, however, have significant limitations, particularly when applied to non-Gaussian and complex signals often found in biomedical data. Statistical techniques are sensitive to outliers and assume a Gaussian distribution, which can lead to inaccurate detection thresholds and failure to identify subtle changes. Outliers can skew the mean and standard deviation, making small changes undetectable. Non-Gaussian distributions, common in real-world biomedical signals, result in inaccurate statistical representations. Additionally, setting an appropriate threshold is difficult: a high threshold may miss subtle changes, while a low threshold can increase false positives. The reliance on fixed thresholds further complicates their application, as inappropriate threshold selection can either overlook important changes or increase the false positive rate [21]. This method also ignores temporal correlations, making it less effective for detecting gradual trends. Recently, the field of signal change detection has witnessed significant advancements, driven by the integration of advanced signal processing techniques and machine learning algorithms [22, 23, 24]. One such technique is time-frequency analysis, which allows for the decomposition of signals into their time-varying frequency components. Methods such as the Continuous Wavelet Transform (CWT) and the short-time Fourier transform (STFT) enable the localization of signal changes in both time and frequency domains, offering enhanced sensitivity to transient events and non-stationary signals [25, 26]. By capturing changes in signal characteristics over time, time-frequency analysis provides valuable insights into dynamic physiological processes and pathological events. However, the effectiveness of CWT is highly dependent on the choice of wavelet function and the threshold used for change detection [27].