A Novel Approach to Objectively Quantify the Subjective Perception of Pain Through Electroencephalogram Signal Analysis

Pain is a complex subjective unpleasant experience that can potentially cause tissue damage. In clinical practice, the main method used for assessing pain is self-report; however, it is not possibly adopted in a huge number of vulnerable populations or by non-communicative patients such as those with disorders of speech and consciousness. Thus, the availability of an objective measure of the subjective pain's perception that complements the self-report pain assessments is a great significant demand in several clinical applications. The aim of this paper is to propose a novel approach to objectively quantify the subjective perception of pain. We integrated signal processing techniques and machine learning principles to learn brain signals associated with pain and classify them into one of four pain intensities (no pain, low, moderate, and high). We found that the signal processing revealed a direct correlation between Alpha frequency band power and the pain intensity, and the classifier could achieve an accuracy of 94.83%. This study provides a clue for the betterment of the collective scientific understanding of the brain's activities inflicted by the physical pain and helps in building a reliable automated prediction of pain.