About this Research Topic
Deep Brain Stimulation (DBS) has emerged as a relevant therapy for the treatment of several disorders, including chronic pain and neuromuscular diseases such as Essential Tremor and Parkinson’s Disease. Over the years we have seen a continuous evolution of the devices and successful applications of DBS.
However, like any technology in health, DBS suffers from limitations and there is a constant need for innovations to improve therapeutic results and security for patients. In this context, the application of Biomedical Signal Processing is of paramount importance.
In this context, the Research Topic Biomedical Signal Processing for Uncovering Deep Brain Stimulation, aims to gather studies and connect researchers whose main interest is to employ and illustrate the practical use of Biomedical Signal Processing to: (i) explain underlying effects of DBS on the central, peripheral and neuromuscular systems; (ii) evaluate the long and short-term performance of neurostimulation devices; (iii) improve security and efficacy of surgical and clinical procedures; (iv) allow for continuous and objective evaluation of patients in the clinical and domestic and remote scenarios and remote.
Studies using adaptive analysis, data science, machine learning, and other methods integrating a practical framework to address relevant issues related to the use of DBS are welcomed.
Although Deep Brain Stimulation is a therapy widely employed in the treatment of essential tremor, Parkinson’s disease, dystonia and pain, the use of this technology may involve surgical complications, hardware failure and infections. Considering the complexity of the surgical procedures and follow-up of patients with implanted sensors, it is necessary for the development of technology to predict failure in hardware, to ease the clinical and remote evaluation of patients, and to improve the results of surgical procedures. Recent advances in Biomedical Signal Processing, adaptive analysis, data science, and machine learning open new windows of opportunities to integrate these tools in a framework capable of addressing practical problems related to DBS. It is expected that some solutions and the results of studies published in the Research Topic Uncovering Deep Brain Stimulation can be transferred to the clinical routine of physicians and patients.
The Research Topic welcomes review and original research articles which clearly illustrate how the use of Biomedical Signal Processing is contributing to the development of DBS-based therapies. The authors can address issues such as (not limited to these): (i) understanding of the underlying effects of DBS on the central, peripheral and neuromuscular systems; (ii) evaluation of long and short-term performance of neurostimulation devices; (iii) improvement of security and efficacy of surgical and clinical procedures; (iv) continuous and objective evaluation of patients in the clinical and domestic and remote scenarios and remote.
We believe this is the right time to bring together a group of experts in Signal Processing and Deep Brain Stimulation, and create a new collection to help define the future of the field (not limited to devices, surgeries, central and peripheral nervous system, neuromuscular data, cognitive-sensory-motor assessment, short and long term performance, machine learning, and AI).
Given the importance of this Research Topic, there is a 50% discount on Original Research and Review submissions for a limited time (up until February 25th, 2022). We hope you will find the opportunity to contribute to our collection in Frontiers in Signal Processing.
Keywords: Deep Brain Stimulation, Adaptive Analysis, Data Science, Neurostimulation, Neuromuscular disorders, Pain, Machine Learning, Biomedical Instrumentation
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.