Feature Extraction with Wavelets for Plethysmography Signal Classification

CUJANO AYALA, ESTEFANY GABRIELA; ECHEVERRÍA, NOELIA INÉS; SCANDURRA, ADRIANA GABRIELA; PASSONI, LUCÍA ISABEL; MESCHINO, GUSTAVO JAVIER; TUSMAN, GERARDO

San Juan

Congreso; XXIII Congreso Argentino de Bioingeniería; 2022

Sociedad Argentina de Bioingeniería

The objective of this work is to recognize blood pressure conditions from the analysis of photoplethysmography signals of patients under anesthesia. A multiresolution analysis was applied to the signals using the discrete wavelet transform to obtain the detail and approximation coefficients that provide information on each cardiac cycle. Characteristics in amplitude-time related to the morphology of the signal were also extracted. A set of signals was selected for the different blood pressure conditions, extracting their characteristics. Thus, a dataset was created with the characteristics as inputs and the blood pressure conditions as targets (hypotension, normotension, hypertension). Using this data, fully connected multilayer neural networks were trained, evaluating the performance of different architectures. The network with the best results was selected by means of statistical quality measures. The architecture selected presented an accuracy of 80.5% for data from new patients.