AIMC Topic: Deep Brain Stimulation
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Identifying controllable cortical neural markers with machine learning for adaptive deep brain stimulation in Parkinson's disease.
NeuroImage. Clinical
Aug 12, 2020
The identification of oscillatory neural markers of Parkinson's disease (PD) can contribute not only to the understanding of functional mechanisms of the disorder, but may also serve in adaptive deep brain stimulation (DBS) systems. These systems see...
Implementation of a Novel Bluetooth Technology for Remote Deep Brain Stimulation Programming: The Pre- and Post-COVID-19 Beijing Experience.
Movement disorders : official journal of the Movement Disorder Society
Jun 1, 2020
LFP-Net: A deep learning framework to recognize human behavioral activities using brain STN-LFP signals.
Journal of neuroscience methods
Feb 3, 2020
BACKGROUND: Recognition of human behavioral activities using local field potential (LFP) signals recorded from the Subthalamic Nuclei (STN) has applications in developing the next generation of deep brain stimulation (DBS) systems. DBS therapy is oft...
Real-time machine learning classification of pallidal borders during deep brain stimulation surgery.
Journal of neural engineering
Jan 6, 2020
OBJECTIVE: Deep brain stimulation (DBS) of the internal segment of the globus pallidus (GPi) in patients with Parkinson's disease and dystonia improves motor symptoms and quality of life. Traditionally, pallidal borders have been demarcated by electr...
Computational analysis of non-invasive deep brain stimulation based on interfering electric fields.
Physics in medicine and biology
Dec 5, 2019
Neuromodulation modalities are used as effective treatments for some brain disorders. Non-invasive deep brain stimulation (NDBS) via temporally interfering electric fields has emerged recently as a non-invasive strategy for electrically stimulating d...
Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Nov 5, 2019
OBJECTIVE: Accurate and reliable detection of tremor onset in Parkinson's disease (PD) is critical to the success of adaptive deep brain stimulation (aDBS) therapy. Here, we investigated the potential use of feature engineering and machine learning m...
Online identification of functional regions in deep brain stimulation based on an unsupervised random forest with feature selection.
Journal of neural engineering
Oct 23, 2019
OBJECTIVE: The identification of functional regions, in particular the subthalamic nucleus, through microelectrode recording (MER) is the key step in deep brain stimulation (DBS). To eliminate variability in a neurosurgeon's judgment, this study pres...
Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms.
World neurosurgery
Oct 18, 2019
BACKGROUND: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurologic symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorit...
Non-Linear Dynamical Analysis of Resting Tremor for Demand-Driven Deep Brain Stimulation.
Sensors (Basel, Switzerland)
May 31, 2019
Parkinson's Disease (PD) is currently the second most common neurodegenerative disease. One of the most characteristic symptoms of PD is resting tremor. Local Field Potentials (LFPs) have been widely studied to investigate deviations from the typical...
Machine learning-aided personalized DTI tractographic planning for deep brain stimulation of the superolateral medial forebrain bundle using HAMLET.
Acta neurochirurgica
May 30, 2019
BACKGROUND: Growing interest exists for superolateral medial forebrain bundle (slMFB) deep brain stimulation (DBS) in psychiatric disorders. The surgical approach warrants tractographic rendition. Commercial stereotactic planning systems use determin...
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