Pulse oximetry is improved through classification of plethysmographic signals by processing the plethysmographic signals using a neural network that receives input coefficients from multiple signal domains including, for example, spectral, bispectral, cepstral and Wavelet filtered signal domains. In one embodiment, a plethysmographic signal obtained from a patient is transformed ( 240 ) from a first domain to a plurality of different signal domains ( 242, 243, 244, 245 ) to obtain a corresponding plurality of transformed plethysmographic signals. A plurality of sets of coefficients derived from the transformed plethysmographic signals are selected and directed to an input layer ( 251 ) of a neural network ( 250 ). The plethysmographic signal is classified by an output layer ( 253 ) of the neural network ( 250 ) that is connected to the input layer ( 251 ) by one or more hidden layers ( 252 ).
Multi-domain motion estimation and plethysmographic recognition using fuzzy neural-nets