Jun, 2021 - By SMI
Through directly delivering little electrical pulses towards the brain, deep brain stimulation (DBS) could ease Parkinson's disease associated tremors or help aid chronic pain. Brown University bioengineers developed the novel algorithm that could prove to be a vital step towards adaptive DBS. This algorithm eliminates a major hurdle which makes sensing brain signals hard for DBS systems while delivering stimulation simultaneously. Published in Cell Reports Methods journal, the research was co-led by a Ph.D. student working at Borton's lab in Brown, Nicole Provenza, and a Ph.D. candidate from University of Minnesota, Evan Dastin-van Rijn, who worked for this project since being undergraduate at Brown, guided by Matthew Harrison, an applied mathematics associate professor.
There are various factors making it difficult for DBS devices to stimulate and sense simultaneously, say the researchers. Among the first being, stimulation artifact’s frequency signature that sometimes can overlap with the brain signal. Hence, simply cutting out frequency bands for eliminating artifacts could even eliminate important signals. For eliminating this artifact and leaving intact the other data, artifact’s exact waveform needs to be recognized, which presents additional issue. Implanted sensors of brain are typically designed to function on least power, so as to rate at which sensors sample electrical signals makes for fairly low-resolution data.
In a range of computer simulations and lab experiments, the team demonstrated that this algorithm outperforms several other techniques within its ability for separating signals from artifact. Researchers even used their algorithm on data that was previously collected from animals and human models for demonstrating that they accurately could identify artifacts along with removing them. Other advantage, according to the researchers, is that their algorithm isn't computationally costly. It can potentially function on present DBS devices in real time.
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