Online-Classification of Capnographic Curves Using Artificial Neural Networks

Computer assisted capnometry is a tool for advanced patient monitoring with potential towards a decision support system. CO2 values as represented in a capnogram reflect information about ventilation and gas exchange. Even more important, the shape of capnograms may reflect certain unexpected clinical relevant situations caused by ventilation or pathophysiological pulmonary reactions like bronchospasm. For the present study capnograms of different types of ventilation have been acquired and templates for the normal capnograms (as judged by experienced anaesthesiologists) were generated. A threshold method was used to extract single cdapnograms out of the CO2-monitor's continuous data. Labeled capnograms of pathological events are gathered. For generating templates a simple correlation algorithm was used to classify capnograms [1]. The algorithm was further improved by rules inspired by visual inspection of the templates. In a second step an artificial neural network (ANN) was trained to assign capnograms to such pre-defined templates. The network consisted of 25 input and 10 hidden neurons. As training algorithm resilient propagation was used [2]. The performance of classification differed with regard to the type of ventilation and the kind of template. In most combinations more than 2/3 of classifications were correct, i.e., the ANN was able to recognize the correct class of the capnogram in question. For practical use the correlation algorithm and the artificial neural network were implemented on a PocketPC. The software can easily classify capnograms using both the ANN as well as the correlation algorithm. The aim of a decision support system to diagnose certain ventilation related diseases or problems in the area of anaesthesia or pneumology seems within reach by now.