Abstract
Hospitals today make the effort to assess acute pain with self-report assessments such as the quantitative pain the level of intensity Index and visual input index. However, because these techniques rely on patient input, they are imprecise. Thus, an objective, statistical approach to ongoing pain monitoring is necessary. In computer vision research, identifying pain intensity is a difficult challenge to solve. However, current subjective pain evaluation is unreliable because it heavily relies on the patient’s response. In order to improve the standardization of pain evaluation, automated pain identification using physiological data might provide essential objective information. In the present study, we provide an objective pain recognition approach based on physiological signals that can extract novel features from electromyography (EMG), electrodermal activity (EDA), and electrocardiogram (ECG) data that have not previously been utilized for pain recognition. Utilizing the Bio-Vid Heat Pain Database (Part A) for evaluation and clinical validation, the proposed machine learning logistic regression-based method performs significantly better than previous techniques recorded in the literature for both the electrodermal activity (EDA) and fusion approaches, with average performances of 82.36% and 83.20% for the binary classification experiment that discriminates within the baseline and the pain tolerance level (T0 vs. T4). Our result shows that it outperforms most of the previously proposed methods in related works.
Keywords: Bio-potential, Classification, Feature, Logistic regression, Pain.