Pain estimation after coronary angiography based on vital signs by using artificial neural networks

Abstract

Background: Coronary angiography is gold standard method to diagnose coronary arteries diseases. The aim of this study was to estimate pain after coronary angiography based on vital signs for determining best position by using artificial neural networks ANN. Methodology: This study used a database containing 86 subjects that refer to angiography center. For each subject Vital signs were measured that included blood pressure, percent of blood oxygen saturation, heart rate, respiratory rate and temperature. The Numeric Rating scale (NRS) was used to determine pain intensity. The vital signs were the inputs and the pain value was the corresponding output. These data were applied to train the ANN in the learning process. The model was implemented in MATLAB software. The results of pain estimation were compared with the results of NRS method and the error rate was calculated. Results: The absolute error and error percentage between NRS method and the present method were 5.41 ± 2.63 mmHg, 4.09 ± 1.59%. The results indicated that the pain measurement by NRS method and pain value predicted with trained ANN differ by only less than 11%. It is obvious that the neural network prediction fit properly to the NRS results. Conclusion: The results of proposed method were closely in agreement with the results of the NRS. so this method can be suggested for reliving the pain and determining the best patient's position after the angiography procedure.