A feeling classification model in a blood draw situation using power spectrum density and a random forest algorithm

Abstract

Feelings and expressions such as pain, anxiety, and excitement can occur while getting blood drawn. These are the physical symptoms that can occur in some patients. A medical provider cannot know pain or anxiety symptoms, which could cause harm to the patients throughout the procedure. However, electroencephalography (EEG) changes, such as Delta, Theta, Alpha, Beta, and Gamma, are essential to identify the patient’s feelings. These can assist in decreasing danger during the procedures. Therefore, this research aims to investigate the patterns in the power spectrum density (PSD) form to classify two feeling states during blood drawing: normal and anxious feelings. This research focused on alpha, beta, and gamma of the PSD. Thus, a method was designed based on the changing values of alpha, beta, and gamma. Each PSD of three waves was derived at 56 minutes. The pattern from this dataset was applied to classify feeling expressions using a random forest (RF) algorithm. This algorithm was used to create a feeling classification model (FCM). The accuracy of the FCM in classifying feeling differences between normal and anxious feelings was 100%. Thus, this proves that the FCM is highly efficient.