Novel Photoplethysmographic Signal Analysis via Wavelet Scattering Transform

Photoplethysmography (PPG) is a non-invasive optical technique, applied in clinical settings to measure arterial oxygen saturation. Using modern technology, PPG signals can be measured by wearable devices. This paper presents a novel procedure to study the dynamics of biomedical signals. The procedure uses features of a wavelet scattering transform to classify signal segments as either chaotic or non-chaotic. To this end, the paper also defines a chaos measure. Classification is made using a model trained on a dataset consisting of signals generated by systems with known characteristics. Using an example PPG signal, this paper demonstrates the usefulness of the wavelet scattering transform for the analysis of biomedical signals, and shows the importance of correctly preparing the training set.