Progress in the treatment of Parkinson's disease through on-demand brain stimulation

Researchers from Universidad Politécnica de Madrid, in collaboration with other universities, have developed a system to alleviate the symptoms associated with Parkinson's disease.

A multidisciplinary team of researchers from Centre for Biomedical Technology (CTB) at Universidad Politécnica de Madrid (UPM), Universidad Carlos III de Madrid (UC3M), Oxford (Oxford University) and Coventry (Coventry University), has developed an on-demand deep brain stimulation system that can be implemented in the neurostimulation systems used for the treatment of Parkinson's disease.

The performance of the proposed system has achieved a 100% of accuracy, that is, this system is able to detect the medical condition of the patient at any moment with no error.

Parkinson's disease is currently the second most important neurodegenerative disease in incidence, but it is estimated to become the first disease by 2040 by overcoming the Alzheimer's disease. Thus there is a need to find solutions by implementing new measures.

The first option to alleviate the symptoms associated with this disease is usually a pharmacological treatment. However, this solution can be not suitable for all patients and it can have side effects such as dyskinesias (abnormal and involuntary movement disorders).

“On-demand brain stimulation is a better strategy since the device is only activated when the patient needs it”, Carmen Cámara, a CTB-UPM researchers, says.

“The development of this type of systems it means to unravel the functioning of the brain networks involved, that it, to understand the neural behavior in every clinical state. For example, how a patient behaves when has the symptoms and how when he has not” Carmen Cámara continues.

The researchers have studied this behavior by observing the neuron synchronization in the different clinical states using mathematical methods of functional connectivity. They found that when a patient has tremors, the neuron synchronization changes. This change can be used as a decision element so the device knows when to start the stimulation.

The system developed by these researchers was designed within the data stream mining paradigm. “This paradigm is an innovative algorithm which is able to work in demanding scenarios and to process and provide a quick response. This is the case of neurostimulators that register a continuous stimulation throughout the life of the patient by conducting a permanent monitoring and decision making” the researcher says.

These promising results could help develop future intelligent systems to improve treatments. As Parkinson's disease is likely to keep growing, the proposed system is expected to be included in healthcare in the coming years.


C.Camara, K.Warwick, R.Bruña, T.Aziz, E.Pereda. Closed-loop deep brain stimulation based on a stream-clustering system. Expert Systems With Applications 126(2019):187–199


Add new comment

Para el envío de comentarios, Ud. deberá rellenar todos los campos solicitados. Así mismo, le informamos que su nombre aparecerá publicado junto con su comentario, por lo que en caso que no quiera que se publique, le sugerimos introduzca un alias.

    Normas de uso:  
  • Las opiniones vertidas serán responsabilidad de su autor y en ningún caso de  
  • No se admitirán comentarios contrarios a las leyes españolas o buen uso.  
  • El administrador podrá eliminar comentarios no apropiados, intentando respetar siempre el derecho a la libertad de expresión.  

Enter the characters shown in the image.
Los datos personales recogidos en este formulario serán tratados de conformidad con el nuevo Reglamento Europeo (UE) 2016/679 de Protección de Datos. La información relativa a los destinatarios de los datos, la finalidad y las medidas de seguridad, así como cualquier información adicional relativa a la protección de sus datos personales podrá consultarla en el siguientes enlace Ante el responsable del tratamiento podrá ejercer, entre otros, sus derechos de acceso, rectificación, supresión, oposición y limitación de tratamiento.