# Linking Cancer Pain Features and Biosignals for Automatic Pain Assessment

## TL;DR

This study shows that electrodermal activity can help assess cancer pain intensity and type, offering a potential objective complement to self-reported pain scales.

## Contribution

The study identifies specific electrodermal activity parameters as novel objective markers for cancer pain assessment.

## Key findings

- Electrodermal activity parameters like MaxCDA differ significantly across pain intensity levels.
- EDA features such as number and amplitude of skin conductance responses distinguish mixed pain from other types.
- Heart rate variability did not show significant associations with pain intensity or type.

## Abstract

Although pain is a frequent and burdensome symptom in people with cancer, it is commonly evaluated using self-reported scales that may be unreliable in patients with cognitive, communicative, or clinical limitations. This study explored whether objective physiological signals could enhance cancer pain assessment. We analyzed electrodermal activity and heart rate variability recorded in cancer patients and examined their relationships with pain intensity and pain type. The results indicate that specific electrodermal activity parameters are associated with both pain intensity and distinct pain phenotypes (mainly mixed pain). In contrast, heart rate variability failed to provide meaningful discrimination in this context. Despite limitations, these findings suggest that electrodermal activity may represent a valuable objective marker to complement conventional pain scales and support the development of automated pain assessment approaches in oncology.

Background: Pain remains one of the most debilitating and prevalent symptoms in cancer patients. However, assessment based solely on subjective self-report tools is limited by cognitive impairment and the heterogeneous nature of cancer pain. Since evidence on the ability of physiological biosignals to discriminate cancer pain intensity and pain phenotypes in real clinical settings remains limited, this study explored the potential of biosignals to discriminate between pain intensity and pain type. Methods: Electrodermal activity (EDA) and electrocardiogram (ECG) signals were recorded in cancer patients using the BITalino (r)evolution board (sampling frequency 1000 Hz). EDA was processed to extract skin conductance responses (SCRs) using continuous decomposition analysis (CDA) and trough-to-peak (TTP) methods. Heart rate variability (HRV) features were extracted in both time and frequency domains, including low frequency (LF), high frequency (HF), and the LF/HF ratio. Non-parametric Kruskal–Wallis tests were performed to compare biosignal parameters across pain intensity (Numeric Rating Scale, NRS: low 1–3; medium 4–6; and high 7–10) and pain types (nociceptive, neuropathic, mixed, and breakthrough cancer pain—BTCP). Results: Data from 61 patients were analyzed. For EDA, the maximum skin conductance response amplitude (MaxCDA) significantly differed across intensity groups (p = 0.037). Post hoc analysis showed a significant difference between the low- and high-intensity groups (p = 0.015), with the low-intensity group exhibiting a higher mean MaxCDA (0.063 µS) than the high-intensity group (0.024 µS). Several EDA parameters were significantly associated with pain type. The number of SCRs (TTP) (p = 0.015) and maximum SCR amplitude (TTP) (p = 0.040) were significantly lower in the mixed pain group compared with the nociceptive and neuropathic groups. No HRV parameters showed significant associations with pain intensity or pain type. BTCP did not significantly affect any biosignal parameters. Subgroup analyses showed that EDA features discriminating mixed pain were preserved in patients without bone metastases, BTCP, or high opioid burden, whereas no clinical variable modified the association between biosignals and pain intensity and type. Conclusions: In this investigation, selected EDA parameters were associated with cancer pain intensity and pain type, whereas heart rate variability measures did not show significant discrimination under the present methodological conditions. These findings suggest that EDA may provide complementary information on pain-related autonomic alterations in oncology patients. However, biosignals should not be considered standalone indicators of pain, and their interpretation requires integration with clinical variables and pharmacological context. Further studies adopting multimodal and longitudinal approaches are needed to clarify their role in automatic pain assessment in cancer care.