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Pain_App

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Predicting neuropathic pain episodes in spinal cord injury patients through port...

Predicting neuropathic pain episodes in spinal cord injury patients through portable EEG and machine learning 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 pharmac... ver más

31/03/2023

MALMO UNIVERSITY

204K€

Presupuesto del proyecto: 204K€

Líder del proyecto

MALMO UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-03-31

MSCA-IF-2020: Individual Fellowships

I+D

Cerrada hace 4 años

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No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

MALMO UNIVERSITY

203.85K€ | Lider

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Duración del proyecto: 24 meses Fecha Inicio: 2021-03-30
Fecha Fin: 2023-03-31

Líder del proyecto

MALMO UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 204K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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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