Signal recognition based on multidimensional optimization of distance function in medical applications

Abstract

The paper presents an idea of the method of creating the signal classifier which is based on the optimization of the metric (distance) function. The authors suggest that the proper choice of metric function parameters allows to adapt the whole classification operation to solve certain problems of the time-varied signal recognition, especially in medical applications. The main advantage of the described approach is a possibility to interpret the obtained solutions. This may enable to progress the doctor’s skills, as well as improve the automatic classification method. The paper presents a brief example of the method usage in a practical application. It deals with the classification of the signals obtained from MEMS (3-axis accelerometer) sensors during the Lachman knee test. The authors point to main conditions which determine an increase in the efficiency of the described approach. Particularly, they are involved in developing efficient optimization methods of discontinuous criterion functions and algorithms for detection the cohesive group of points that define the relevant signal regions.