Scalogram Image

Scalogram images, generated through wavelet transforms of time-series data, are increasingly used as input for machine learning models to analyze complex signals. Current research focuses on applying scalograms to diverse fields, including acoustic recognition and medical diagnosis (e.g., arrhythmia detection from ECGs), often employing convolutional neural networks (CNNs) or hybrid architectures like CNN-LSTMs for classification tasks. This approach offers advantages in automated analysis of large datasets, improving efficiency and potentially accuracy compared to manual analysis, with applications ranging from predictive maintenance to healthcare. The effectiveness of scalogram-based methods is being actively evaluated and compared to traditional spectrograms.