## 7 Conclusions  
The analysis of the simulated EEG-like time series through three distinct methods--the evaluation of mean and standard deviation, time-frequency analysis using a spectrogram, and change detection employing a Support Vector Machine (SVM)--demonstrates a comprehensive approach to understanding the underlying dynamics of EEG signals. The mean and standard deviation provide insights into the overall signal characteristics and variability, while the time-frequency analysis using CWT effectively reveals the frequency content and its temporal evolution, highlighting dominant alpha and beta rhythms. The SVM model achieves promising performance in detecting changes (spikes) within the signal, indicating its potential utility in identifying significant events. However, limitations exist: the simulated data does not capture all the complexities and noise typically present in real EEG recordings, the choice of parameters for the SVM can significantly influence the results, and the reliance on sliding window features may overlook nuanced information that occurs outside the analyzed windows. Thus, while the methodologies are useful for basic analysis and change detection, they require careful parameter tuning and validation against real-world data to ensure robustness and applicability in clinical settings. In conclusion, this study highlights the critical role of change detection in biomedical signal processing for understanding physiological processes and diagnosing medical conditions. Through an investigation of different methods using synthetic signals mimicking real-world scenarios, including classical statistical techniques, Support Vector Machine (SVM) classification, and Continuous Wavelet Transform (CWT) time-frequency analysis, insights were gained into their efficacy. While all methods struggled to detect changes in synthetic nonlinear signals, classical statistical techniques and SVM classification proved effective in detecting changes in signals with simulated anomalies. However, CWT-based time-frequency analysis encountered difficulties in detecting changes in both synthetic nonlinear signals and signals with simulated anomalies. These findings emphasize the  
Figure 12: Analysis of synthetic sine wave signal and change detection. (**Top**) Original synthetic sine wave signal with a base frequency of 1 Hz (in blue), along with calculated mean (in green) and standard deviation (in red) lines. The abrupt changes in amplitude are highlighted in the signal plot between the vertical dashed lines. (**Middle**) Time–Frequency representation of the synthetic sine wave signal obtained via short-time Fourier transform (STFT). The spectrogram illustrates the frequency content over time, showing how the signal’s energy distribution shifts during the introduced changes. (**Bottom**) Change detection using Support Vector Machine (SVM) classification. The colored regions indicate the predicted states of the signal (baseline vs. changed amplitude), demonstrating the SVM’s ability to identify segments with different characteristics. The SVM was trained using features (mean and standard deviation) extracted from the signal. necessity of carefully selecting appropriate methods for change detection in biomedical signal processing, considering the unique characteristics of the signals and the specific requirements of the detection task. Future research endeavors may focus on exploring advanced machine learning algorithms and signal processing techniques to further enhance the accuracy of change detection in biomedical applications. Moving forward, several avenues for future research and development in change detection in biomedical signals can be pursued. Firstly, exploring hybrid approaches that combine the strengths of different methods, such as integrating classical statistical techniques with machine learning algorithms or incorporating domain knowledge into time-frequency analysis, could improve detection accuracy and robustness.