Pain Monitoring Using Plurality of Non-invasive Physiological Measurement

Pain is an unpleasant sensation, ranging from slight discomfort to intense suffering. However, since a great extent of pain is a subjective phenomenon, it has frequently defied objective, quantitative measurement. Traditionally, physicians have had to assess a patient's pain by relying on the patient's own description. Self-description is not only subjective by definition; it is often inaccurate, in part because it is difficult for subjects to precisely articulate their pain while in the midst of a pain experience. Moreover, the report might be impossible when the subject cannot communicate

Presently, in order to quantify pain, the care provider asks the patient to rate his/her pain intensity using one-dimensional scale usually scored from 0 to 10. This scale is known as Numeric Pain Scale. This and other measures are used by the care providers to estimate the correct treatment dose and or to track a treatment progress. Due to its impact on care provider decision to prescribe painkiller mediation, some patients also intentionally misrepresent the existence or extent of their pain. Yet, without any reliable basis for denying such prescriptions, physicians generally must assume that the claims are truthful, even when they may suspect a lack of sincerity. Otherwise, the care provider may be accused of inhumane treatment. Conversely, other patients may underreport their pain, again for a variety of reasons.

The presented clinical trial is intended to implement, validate performances and evaluate efficacy of the pain monitoring device for automated assessment of patient's pain level. During the trial, up to 100 healthy young adults will be voluntarily inflicted by pain stimuli. The pain stimuli will be thermal heat pain stimulus and cold water pain stimulus applied with different intensities. Plurality of Non-Invasive Physiological Measurements will be recorded from volunteers and their Numeric Pain Scale reports will be monitored before, during and after the pain induction. Additional information such as age, gender, ethnicity, etc. will be collected as well. The collected database will be used to implement the algorithm that applies modern signal processing and machine learning methods in order to differentiate between different pain levels. The algorithm will be later integrated into pain monitoring device. The efficacy of the algorithm of the pain monitor will be tested by comparing its results to the patient pain reports towards a given pain stimuli.