Hybrid Hybrid wavelet and entropy features to monitor happy hypoxia based on photoplethysmogram signals

Abstract

Happy hypoxia is a condition where patients experience decreasing oxygen saturation in their brains. In worst cases, Happy hypoxia can reduce the patient's consciousness and even death. Covid-19 has increased cases of happy hypoxia. Several studies have been conducted to detect the happy hypoxia. Existing research projects generally use photo plethysmography signals. However, the results show that the accuracy of happy hypoxia detection is still low. This study provides a solution to the above problems, by proposing a happy hypoxia detection system based on entropy and Discrete Wavele Transform (DWT) features that are combined with a classifier based on K Nearest Neighbor (KNN). The method used in this research is as below Hybrid Wavelet and Entropy Features method.Experiments on the proposed system have been carried out using data on Covid-19 patients from Haji Adam Malik Hospital in Medan.The experimental results show that the system proposed has an accuracy of 87%, sensitivity of 90% and specificity of 85