This study is a single-center, randomized controlled trial aiming to evaluate the analgesic mechanism of Transcutaneous Electrical Nerve Stimulation based on Wrist-Ankle Acupuncture (TENS-WAA) during unsedated colonoscopy using EEG-fNIRS technology to assess neural activity in brain regions associated with pain perception. Sixty patients aged 18-75 years, with stable cardiopulmonary function and a baseline visual analog scale (VAS) pain score \<3, will be enrolled and randomly allocated into the intervention and control groups. The intervention group will receive TENS stimulation based on the Wrist-Ankle Acupuncture theory 10 minutes before the colonoscopy, with a frequency of 2 Hz and adjustable current intensity ranging from 1 to 9 mA. The control group will receive minimal-intensity sham stimulation under identical conditions. All participants will wear EEG-fNIRS devices to monitor neural activity in key pain-related brain areas, including the prefrontal cortex, anterior cingulate cortex, motor cortex, and parietal cortex. Primary outcomes include EEG-fNIRS data, while secondary outcomes are VAS scores at the four colonic bends, colonoscopy duration, and the correlation between EEG-fNIRS signals and pain perception. Statistical analyses will include multivariable linear regression, generalized estimating equations, and mixed-effects models to investigate the analgesic effects and neural mechanisms of TENS-WAA. This study seeks to provide innovative pain management strategies for patients undergoing unsedated colonoscopy and further explore the neuroregulatory potential of TENS-WAA technology.
Patients undergoing unsedated colonoscopy are eligible for inclusion in this study.
Aged 18 to 75 years.
Meeting the requirements for colonoscopy, with good cardiopulmonary function and stable vital signs.
A pre-procedural Visual Analog Scale (VAS) pain score of less than 3.
Participants with speech or cognitive impairments.
Those with acute anal or rectal stenosis, acute perianal or rectal infections, acute diverticulitis, or active inflammatory bowel disease.
Women who are menstruating, pregnant, or breastfeeding.
Patients with active tuberculosis, hemophilia, or advanced malignant tumors.
Parallel
Using an electrical stimulation device to relieve pain during colonoscopy
DeviceIn the electrical stimulation group, the device's current intensity will be adjusted to the maximum tolerance below the participant's pain threshold, while in the control group, the current intensity will be set to the minimum.
NeuroActivity Correlation Index
The NeuroActivity Correlation Index is a metric calculated using machine learning models to quantify the correlation between EEG and fNIRS data. Data preprocessing involves selecting appropriate frequency bands of EEG Power Spectral Density and corresponding brain regions' ΔHbO2 and ΔHb changes, with normalization of the data. Feature engineering involves extracting statistical and temporal window features, possibly using dimensionality reduction techniques such as Principal Component Analysis to enhance model efficiency. The model training phase utilizes regression models such as linear regression or support vector machine regression, or methods like Canonical Correlation Analysis to discover correlations between EEG and fNIRS data, optimizing model parameters through cross-validation. The correlation index is calculated based on model outcomes, typically expressed as a percentage, reflecting the statistical correlation strength between the datasets.
Pain VAS score
VAS is used to assess pain. It is widely used in clinical practice in China. The basic method is to use a 10-cm long floating ruler with 10 scale marks on one side, with 0 and 10 marks at the two ends. 0 means no pain, and 10 means the most severe pain that is unbearable.
Colonoscopy time
Correlation between EEG-fNIRS and pain VAS scores
The neurophysiological and hemodynamic indicators were correlated with pain scores using AI machine learning methods.
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First Affiliated Hospital of the Chinese People's Liberation Army Naval Medical University
Lead sponsor
Shanghai Shuli Intelligent Technology Co., Ltd
Collaborator
