Abstract

A waveform analysis assembly (10) includes a sensor (12) for detecting physiological electrical and mechanical signals produced by the body. An extraction neural network (22, 22') will learn a repetitive waveform of the electrical signal, store the waveform in memory (18), extract the waveform from the electrical signal, store the location times of occurrences of the waveform, and subtract the waveform from the electrical signal. Each significantly different waveform in the electrical signal is learned and extracted. A single or multilayer layer neural network (22, 22') accomplishes the learning and extraction with either multiple passes over the electrical signal or accomplishes the learning and extraction of all waveforms in a single pass over the electrical signal. A reducer (20) receives the stored waveforms and times and reduces them into features characterizing the waveforms. A classifier neural network (36) analyzes the features by classifying them through nonliner mapping techniques within the network representing diseased states and produces results of diseased states based on learned features of the normal and patient groups.