Why the study?
Optimal approaches for biomedical signal classification combining time-frequency analysis and machine learning required comparative evaluation to guide method selection for ECG and EEG signals.
This study provides a comparative evaluation of time-frequency analysis and machine learning models to guide the selection of optimal methods for biomedical signal classification.
May guide model selection for ECG/EEG classification; leaves open prospective clinical validation.
This research aims at comparing the performance of TF analysis in classifying biomedical signals, with special reference to ECG and EEG signals using PhysioNet database as the major source of data. Fourier Transform and Wavelet Transform, are used to extract the feature from signals for the time-frequency analysis. These features are then used for classification using the algorithms like Random Forest and Neural Networks. All computations are performed with SciPy, NumPy, and Scikit-learn libraries. The study assesses the methods considering the classification performance, computational cost, and interpretability toward determining which approaches offer the best solution for the biomedical signal classification. It needs to be mentioned that the presented results are aimed at helping researchers and practitioners in choosing proper methods for biomedical applications of ECG and EEG signals, focused on the PhysioNet database.
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Reddy et al. (2025) studied this question.
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