Morlet Wavelet Ridge Extraction

This article presents the implementation of a Morlet wavelet ridge extraction program, which efficiently extracts wavelet ridges from signals. The program is developed based on Morlet wavelet theory and performs ridge extraction through signal analysis and processing. The implementation typically involves computing the continuous wavelet transform using Morlet wavelets, followed by ridge detection algorithms that identify local maxima in the wavelet transform modulus. The key advantage of this method lies in its ability to accurately extract wavelet ridges from signals, enabling better understanding of signal characteristics. The ridge extraction process may include steps like scale-space analysis, threshold-based peak detection, and ridge line connection algorithms to ensure continuous ridge tracking. This approach has found widespread applications in signal processing fields, particularly in biomedical domains such as electroencephalogram (EEG) signal analysis, where it helps identify frequency-modulated components and transient features in non-stationary signals. The method's robustness makes it suitable for analyzing time-frequency characteristics in various oscillatory signals.