Computer Pattern Recognition Techniques: Some Results with Real Electrocardiographic Data

Automatic interpretation of electrocardiograms is a particular example of the application of digital computers to medical diagnosis; this paper describes our experience with a new approach involving pattern recognition techniques. The program employs a multiple adaptive matched filter system with a variety of normalization, weighting, comparison, decision, modification, and adapting operations. The flexibility of the method has permitted study of effects of experimental variations of these operations on the pattern classification process to simulate human interpretation of electrocardiograms more closely. These programs have been successfully applied to actual electrocardiograms from cardiac patients. These researches in application of computer pattern recognition techniques to the automatic interpretation of electrocardiograms have been undertaken because they join together three fields of great interest. First, an example of artificial intelligence or a self-organized system is represented by the adaptive filter memory, together with the related decision operations. Second, we consider our program to be a model of complex sensory discrimination and use our intuition of human psychology as a guide when selecting one of several possible program mechanisms to overcome temporary obstacles. Third, the automation of medical diagnosis is a rapidly developing and promising field contributing to medical progress. This paper pays particular attention to the third of these objectives. The present state of computer analysis of electrocardiograms is mainly one of orthogonalization of the spatial vector, point recognition to separate the various component waves, parameterization, in one case via Fourier techniques, and then statistical matrix analysis.