Neuropathic pain (NP) is a common symptom arising as a direct consequence of a lesion or disease affecting the somatosensory system. The traditional approach to manage NP patients is to initiate treatment with conservative pharmacological therapy before interventional strategies. However, first-line drug treatments have shown modest efficacy with less than 50% of pain relief. Since NP is present in ~70% of patients with spinal cord injury (SCI), people with this pathology represent a reliable population to study NP. Interestingly, previous studies have shown a clear correlation between NP and changes in electroencephalography (EEG), which is a good indicator of the state of the central nervous system. Hence, I hypothesise that NP episodes in SCI patients can be predicted based on the classification and identification of features extracted from EEG recordings in resting state and during an imaginary motor task. In recent years, digital health technology has emerged as a useful tool to improve data management strategy under the full control of the patient. In this project, I will employ state-of-the-art digital health technology (a smartphone app and a portable EEG) to collect data from SCI patients daily for one month, including pain self-assessment scales and physiological indicators. I will set up a digital-health-based study using a software platform already established by the host institution. The collection of these data will allow me to develop a personalised model to predict the onset of NP episodes using machine learning techniques. Predicting the occurrence of NP episodes will increase the medication efficacy, which in turn will prevent an aggressive development of pain events while minimising the side effects produced by excessive drug doses. The expected results of this project will remarkably improve the quality of life of SCI patients with NP.
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