Unravelling the physiological and psychosocial signatures of pain by machine learning

<p>These datasets include information from 118 subjects, with 81 chronic pain patients across three different cohorts, Complex Regional Pain Syndrome (CRPS), Low Back Pain (LBP) and Spinal Cord Injury with Neuropathic Pain (SCI NP) and 37 healthy subjects. Each participant underwent 40 repetitions of experimentally induced pain, resulting in a total of 4,697 pain trials. Physiological signals (EDA and EEG) and psychosocial information have been recorded and collected. Age, gender, height, weight, BMI, medications, fatigue, sleep quality, perceived health, quality of life, sleep quality, and sick leave and validated questionnaires: Hospital Anxiety and Depression Scale (HADS), Pain Catastrophizing Score (PCS), and Pain Self-Efficacy Questionnaire (PSEQ) and Multidimensional Assessment of Interoceptive Awareness (MAIA).</p> <p>This information has been used for the publication "Unravelling the physiological and psychosocial signatures of pain by machine learning".</p> <p><span><em><strong>Cite this dataset as:</strong></em></span><br>N. Gozzi, G. Preatoni, F. Ciotti, M. Hubli, P. Schweinhardt, A. Curt, S. Raspopovic, Unraveling the physiological and psychosocial signatures of pain by machine learning. Med 0 (2024). &nbsp;<a href="https://doi.org/10.1016/j.medj.2024.07.016" target="_blank" rel="noopener">10.1016/j.medj.2024.07.016</a></p> <p>&nbsp;</p> <p>&nbsp;</p>