A Comparison Between Wavelet Scattering Transform and Transfer Learning for Elevated Blood Pressure Detection

Hypertension is a health issue whose late diagnosis could lead to renal, cerebral, and cardiac events. In this work, it is proposed to use the wavelet scattering transform (WST) as a feature extraction technique applying classical machine learning techniques using photoplethysmography (PPG) signals as input to detect elevated blood pressure and compare its performance with transfer learning applied through fine-tuned convolutional neural networks. The results show that the features obtained by applying the WST and training a logistic regression and support vector machine produced similar results in terms of accuracy compared to fine-tuned convolutional neural networks, with the advantage that the WST could be used to generate a white-box model, which is better suited for a potential medical diagnosis application.