## 2 Materials and Methods
### Machine Learning in Pain Research  
Machine learning approaches to pain research utilize the well-established autonomic response to noxious or emotionally stressful events, captured through ECG, PPG, EDA, EMG, and similar sensors. These data are then used to map physiological measures to pain labels [24]. Framed as a supervised classification task, algorithms such as support vector machines (SVMs), gradient boosting, and RF learn nonlinear decision boundaries from feature vectors that encompass HRV, skin conductance (SC), muscle activity, and respiratory patterns [24]. RFs are rooted in ensemble learning theory, employing techniques like bootstrap aggregation and random subspace selection, which produce low-variance, asymptotically consistent predictors that perform well on the small, noisy datasets commonly found in biomedical research [25].  
This machine learning framework for pain assessment offers three key advantages. Machine learning models can simultaneously integrate multiple physiological signals, enhancing multimodal assessments for improved robustness and accuracy [26]. By fusing data from various physiological signals and even modalities such as facial expressions, these models increase information density and offer a comprehensive view of a patient's pain experience. Analyses based on multi-sensor time-series data have been successfully applied across a wide range of domains beyond the biomedical field [27]. Additionally, they can be customized to an individual's baseline physiology, effectively addressing the inter-subject variability that often limits rule-based approaches [28]. Furthermore, efficient algorithms, implemented in wearables or bedside monitors, provide continuous pain scores, enabling clinicians to adjust analgesic prescriptions, therapists to adapt rehabilitation exercises, and consumer apps to alert users before pain escalates [28].  
As summarized in Table 1, machine learning models have demonstrated accuracies of 0.80-0.85 in detecting nociceptive heat and electrical pain. Recent narrative reviews indicate a shift in the field toward multimodal, explainable approaches, emphasizing the need to explore emotional or subjective pain domains [26]. Our study addresses this need  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signals** & **Features** & **Definition** \\ \hline \multirow{8}{*}{ECG} & HR (beat/min) & Average of HR \\ \cline{2-3}  & SDNN (ms) & Standard deviation of NN intervals \\ \cline{2-3}  & RMSSD (ms) & Root mean square of successive differences between adjacent NNs \\ \cline{2-3}  & pNN50 (\%) & Percentage of successive NNs that differed by more than 50 ms \\ \cline{2-3}  & HFnu & HFnu = HF power/(LF power + HF power), where LF power and HF power are the absolute powers in the LF (0.04–0.15 Hz) and HF (0.15–0.4 Hz) bands, respectively. \\ \cline{2-3}  & LF/HF & Ratio of LFnu to HFnu \\ \hline RESP & RR (breath/min) & Number of breaths per minute \\ \hline PPG & BVP (V) & Average of BVP range \\ \hline ECG, PPG & PTT (ms) & Elapsed time between the R-peak of the ECG and the arrival of the pulse wave at the finger \\ \hline \multirow{2}{*}{EDA} & SCL (\(\mu\)S) & Tonic level of electrical conductivity of skin \\ \cline{2-3}  & SCR (\(\mu\)S) & Average of the SCR amplitude (0.05 \(\mu\)S or greater) of all the specific SCR events \\ \hline Temperature & FT (\({}^{\circ}\)C) & Average of FT \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of the physiological features.  
by focusing on the discrimination of perceived intensity levels of the same pain stimulus through a multimodal physiological signal and machine learning approach.