Abstract: A recurrent neural network model is trained to ignore noise components and accurately reconstruct quasi-periodic SpO2 signal waveforms. In accordance with the invention, the neural network is trained on a carefully structured data set so as to be able to (1) be able to use deep learning techniques for model training, and (2) utilize traditional time-series forecasting neural network techniques to produce a clean reconstructed signal from potentially noisy inputs. A novel technique is used to construct a training data set that turns a forward-looking RNN forecasting model into a “sideways-looking” model which acts as a sophisticated noise filter.