Project description
Personalised digital health technology for the prediction of neuropathic pain
Neuropathic pain (NP) is a direct consequence of a lesion or disease affecting the somatosensory system. Previous studies have shown a correlation between NP and changes in electroencephalography (EEG), which is an indicator of the state of the central nervous system. The working hypothesis of the EU-funded Pain_App project is that the classification and identification of features from EEG recordings can predict NP episodes in patients with spinal cord injuries. The study will employ a smartphone app and a portable EEG device to collect data from patients, including pain self-assessment and physiological indicators. These data will enable the development of a personalised model to predict the onset of NP episodes using machine-learning techniques.
Objective
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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences biological sciences neurobiology
- medical and health sciences basic medicine pharmacology and pharmacy pharmaceutical drugs
- medical and health sciences basic medicine pathology
- engineering and technology electrical engineering, electronic engineering, information engineering information engineering telecommunications mobile phones
- natural sciences computer and information sciences artificial intelligence machine learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2020
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
205 06 MALMOE
Sweden
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.