Self-learning neural networks in electrocardiography

Abstract

Activated partial thromboplastin time (APTT) and prothrombin time (PT) assays are widely used to screen for coagulation disorders and to monitor administration of therapeutic drugs. The analysis of data from coagulation assays has traditionally concentrated on determination of clot times (for APTT and PT) and magnitude of signal change during coagulation (e.g. for PT-based fibrinogen quantitation). The purpose of this study was to determine if the diagnostic power of these assays could be increased by using neural networks to interpret multiple parameters from these assays. Error back-propagation neural networks were trained using multiple variables derived from APTT and PT optical data for 200 normal and abnormal patient specimens. These networks were used to: (1) classify samples as either deficient or non-deficient with respect to individual blood components; and (2) estimate the approximate concentration of specific coagulation factors. Results indicated that these networks could be successfully trained to identify specific factor deficiencies at less than 30% normal levels with good specificity and variable sensitivity, but that they estimated actual concentrations poorly in most cases. These results support possible applications for neural networks identifying specific coagulation abnormalities from non-specific APTT and PT assays using expanded data parameter sets.