Serial VCG/ECG Analysis Using Neural Networks

Abstract

Serial ECG analysis is an important diagnostic tool in which two or more successive ECG recordings from the same patient are compared in order to find changes due to, e.g. myocardial infarction. The present study investigates a new approach to serial analysis which is based on artificial neural networks. Interrecording changes are sometimes falsely detected due to electrode misplacement or positional changes of the heart. In order to compensate for such problems, a new technique for VCG loop alignment was employed. A study population of 1000 patients with two recordings was used and manually scrutinized by three experienced ECG interpreters. Pathological changes indicating newly developed infarcts were found in 256 patients. Different combinations of VCG/ECG measurements served as input data to the neural network. The best performance of the neural network was obtained when ECG and VCG measurements were combined and the resulting sensitivity was 69% at a specificity of 90%. The use of only ECG or VCG measurements reduced the sensitivity to 63% and 60%, respectively. The results indicated that serial analysis based on neural networks did not improve significantly when VCG loop alignment was included.