Publication

Abstract : Multi-parameter patient monitors (MPMs) are extensively used for enhancing the quality of healthcare in both intensive care units (ICU) and in-patient wards. MPMs make use of the vital signs, respiration rate, heart rate, blood pressure and oxygen saturation (SpOinf2/inf), for predicting the condition of patients. Support vector machine (SVM) is one of the most popularly used classification algorithms for developing MPMs. The kernel function, used in an SVM is a measure of similarity between any two examples, either belonging to same class or different classes. The selection of the kernel is an important aspect for the optimization of the system using SVM. If two patients have heart rates of 60 bpm and 80 bpm, intuition suggests that their heart rate similarity is 60 bpm. Extending this to n features, we may say that the total similarity is a summation of the individual similarities over n features, suggesting that intersection kernel is an ideal choice for MPM. In this paper, we explore the effectiveness of using intersection kernel SVM (IKSVM) for improving the performance of MPMs. We also compare the performance improvement of the MPM using IKSVM with the popularly used linear, polynomial and radial basis function (RBF) kernel MPMs. The results suggest that the use of intersection kernel can help enhance the performance of the MPMs significantly. Using IKSVM system, we obtained an improvement of 2.74%absolute for overall accuracy, 1.86% absolute for sensitivity and 3.00% absolute for specificity over the best baseline MPM using RBF kernel. © Research India Publications.