Authors:
E. Pattyn
1
;
2
;
E. Vergaelen
3
;
E. Lutin
2
;
R. Van Stiphout
4
;
H. Davidoff
1
;
2
;
W. De Raedt
2
and
C. Van Hoof
1
;
2
;
4
Affiliations:
1
Department of Electrical Engineering, KU Leuven, Leuven, Belgium
;
2
Imec, Leuven, Belgium
;
3
Center for Mind-Body Research, KU Leuven, Leuven, Belgium
;
4
OnePlanet Research Centre, Wageningen, The Netherlands
Keyword(s):
Chronic Pain, Machine Learning, Physiological Signals, Pain Classification.
Abstract:
Chronic pain is a complex and personal condition that imposes a substantial burden on both individuals and society. Potentially, wearable technology could enable continuous monitoring of pain in real-world settings, offering insights into the complex relationship between physiological states and chronic pain. In this pilot study, we evaluated the practicability of collecting physiological data, from ten individuals with chronic pain and ten healthy controls, using wearable wristbands and digital pain diaries for one week in their everyday lives. Additionally, we trained various machine learning classifiers to classify pain levels and evaluated which feature modalities, e.g., heart rate-derived features, yielded the highest balanced accuracy. Our results demonstrated satisfactory data quantity, with wristband data being available for patients and controls approximately 92% to 82% of the time, and data quality, with high-quality physiology ranging from 80% to 72% for the respective gro
ups. The median balanced accuracies in distinguishing pain intensity classes ranged between 0.27 and 0.40. Furthermore, we found that individual modalities did not outperform the combined modalities. Nonetheless, further research with larger sample sizes is necessary to elucidate these relationships and improve pain management strategies for individuals with chronic pain.
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