Alfonso Maria Ponsiglione

Perception and expression of pain in cancer patients are influenced by distress levels, tumor type and progression, and the underlying pathophysiology of pain. Relying on traditional pain assessment tools can present limitations due to the highly subjective and multifaceted nature of the symptoms. In this scenario, objective pain assessment is an open research challenge. This work introduces a framework for automatic pain assessment. The proposed method is based on a wearable biosignal platform to extract quantitative indicators of the patient pain experience, evaluated through a self-assessment report. Two preliminary case studies focused on the simultaneous acquisition of electrocardiography (ECG), electrodermal activity (EDA), and accelerometer signals are illustrated and discussed. The results demonstrate the feasibility of the approach, highlighting the potential of EDA in capturing skin conductance responses (SCR) related to pain events in chronic cancer pain. A weak correlation (R = 0.2) is found between SCR parameters and the standard deviation of the interbeat interval series (SDRR), selected as the Heart Rate Variability index. A statistically significant (p < 0.001) increase in both EDA signal and SDRR is detected in movement with respect to rest conditions (assessed by means of the accelerometer signals) in the case of motion-associated cancer pain, thus reflecting the relationship between motor dynamics, which trigger painful responses, and the subsequent activation of the autonomous nervous system. With the objective of integrating parameters obtained from biosignals to establish pain signatures within different clinical scenarios, the proposed framework proves to be a promising research approach to define pain signatures in different clinical contexts.