Investigating the Analgesic Mechanisms of Acupuncture for Cancer Pain: Insights From Multimodal Bioelectrical Signal Analysis

Jianhao Huang,1,* LiuYang Zhao,2,* YuFeng Xie,1,3,* Chi Wang,4 XinJing Yang,1 HaiFu Huang,1 Dian Zhang2 1Department of Acupuncture and Moxibustion, Shenzhen Hospital (Fu Tian) of Guangzhou University of Chinese Medicine, Shenzhen, People’s Republic of China; 2School of Computer and Software, Shenzhen University, Shenzhen, People’s Republic of China; 3Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macao, People’s Republic of China; 4, Ganzhou Traditional Chinese Medicine Hospital, Ganzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: HaiFu Huang, Shenzhen Hospital (Fu Tian) of Guangzhou University of Chinese Medicine, 6001 Beihuan Avenue, Futian District, Shenzhen, People’s Republic of China, Email [email protected] Dian Zhang, School of Computer and Software, Shenzhen University, No. 3688, Nanhai Avenue, Nanshan District, Shenzhen, People’s Republic of China, Email [email protected]: Cancer pain management remains a significant clinical challenge. While acupuncture has shown potential in alleviating cancer pain, its underlying mechanisms are not yet fully understood. This study investigates the neurophysiological mechanisms underlying acupuncture’s analgesic effects using multimodal bioelectrical signal analysis.Patients and Methods: Fifteen cancer pain patients underwent acupuncture while wearing portable, multi-sensor devices to capture bioelectrical signals. Pain levels were assessed using the Numerical Rating Scale (NRS) before and during needle retention. Neurophysiological changes were evaluated using Principal Component Analysis, Joint Time-Frequency Analysis, power spectrum analysis, spectral analysis, and dynamic functional network analysis.Results: There was a significant reduction in NRS scores from pre-treatment to the retention period, indicating pain relief. Principal component analysis showed significant differences in bioelectrical signals between these periods. Power spectrum analysis revealed decreased signal power during retention. Functional network analysis demonstrated a reduction in connectivity strength between electroencephalography and electromyography signals. Spectral analysis identified distinct real-time and staged characteristics of bioelectrical signals, with correlation analysis confirming a positive relationship between NRS score changes and bioelectrical signal alterations.Conclusion: Acupuncture alleviates cancer pain by reducing functional connectivity between injured tissues and the brain, with immediate effects. Prolonging needle retention may enhance therapeutic outcomes. These findings provide new insights into the neurophysiological basis of acupuncture’s analgesic effects, supporting its role in cancer pain management.Keywords: cancer pain, acupuncture, multimodal bioelectrical signals, analgesic mechanism, deep learning