Preliminary Results of Neuromorphic Controller Design and a Parkinson's Disease Dataset Building for Closed-Loop Deep Brain Stimulation

• URL: http://arxiv.org/abs/2407.17756v1
• Date: Thu, 25 Jul 2024 04:10:15 GMT
• Title: Preliminary Results of Neuromorphic Controller Design and a Parkinson's Disease Dataset Building for Closed-Loop Deep Brain Stimulation
• Authors: Ananna Biswas, Hongyu An,
• Abstract summary: Closed-loop Deep Brain Stimulation (CL-DBS) aims to alleviate motor symptoms in Parkinson's Disease patients. Current CL-DBS systems utilize energy-inefficient approaches, including reinforcement learning, fuzzy interface, and field-programmable gate array (FPGA) This research proposes a novel neuromorphic approach that builds upon Leaky Integrate and Fire neuron (LIF) controllers to adjust the magnitude of DBS electric signals according to the various severities of PD patients.
• Score: 1.3044677039636754
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Parkinson's Disease afflicts millions of individuals globally. Emerging as a promising brain rehabilitation therapy for Parkinson's Disease, Closed-loop Deep Brain Stimulation (CL-DBS) aims to alleviate motor symptoms. The CL-DBS system comprises an implanted battery-powered medical device in the chest that sends stimulation signals to the brains of patients. These electrical stimulation signals are delivered to targeted brain regions via electrodes, with the magnitude of stimuli adjustable. However, current CL-DBS systems utilize energy-inefficient approaches, including reinforcement learning, fuzzy interface, and field-programmable gate array (FPGA), among others. These approaches make the traditional CL-DBS system impractical for implanted and wearable medical devices. This research proposes a novel neuromorphic approach that builds upon Leaky Integrate and Fire neuron (LIF) controllers to adjust the magnitude of DBS electric signals according to the various severities of PD patients. Our neuromorphic controllers, on-off LIF controller, and dual LIF controller, successfully reduced the power consumption of CL-DBS systems by 19% and 56%, respectively. Meanwhile, the suppression efficiency increased by 4.7% and 6.77%. Additionally, to address the data scarcity of Parkinson's Disease symptoms, we built Parkinson's Disease datasets that include the raw neural activities from the subthalamic nucleus at beta oscillations, which are typical physiological biomarkers for Parkinson's Disease.

Related papers

• Using AI to Measure Parkinson's Disease Severity at Home [2.656113227046603]
  We present an artificial intelligence system to assess the motor performance of individuals with Parkinson's disease (PD) Data from 250 global participants were rated by three expert neurologists following the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Our machine learning model trained on these measures outperformed an MDS-UPDRS certified rater, with a mean absolute error (MAE) of 0.59 compared to the rater's MAE of 0.79.
  arXiv  Detail & Related papers  (2023-03-30T17:40:14Z)
• Parkinsons Disease Detection via Resting-State Electroencephalography Using Signal Processing and Machine Learning Techniques [0.0]
  Parkinsons Disease (PD) is a neurodegenerative disorder resulting in motor deficits due to advancing degeneration of dopaminergic neurons. EEG indicates abnormalities in PD patients. One major challenge is the lack of a consistent, accurate, and systemic biomarker for PD in order to closely monitor the disease with therapeutic treatments and medication.
  arXiv  Detail & Related papers  (2023-03-29T06:03:05Z)
• Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease Treatment [6.576864734526406]
  Deep brain stimulation (DBS) has shown great promise toward treating motor symptoms caused by Parkinson's disease (PD) DBS devices approved by the U.S. Food and Drug Administration (FDA) can only deliver continuous DBS (cDBS) stimuli at a fixed amplitude. This energy inefficient operation reduces battery lifetime of the device, cannot adapt treatment dynamically for activity, and may cause significant side-effects.
  arXiv  Detail & Related papers  (2023-02-05T20:29:53Z)
• Subgroup discovery of Parkinson's Disease by utilizing a multi-modal smart device system [63.20765930558542]
  We used smartwatches and smartphones to collect multi-modal data from 504 participants, including PD patients, DD and HC. We were able to show that by combining various modalities, classification accuracy improved and further PD clusters were discovered.
  arXiv  Detail & Related papers  (2022-05-12T08:59:57Z)
• Characterizing TMS-EEG perturbation indexes using signal energy: initial study on Alzheimer's Disease classification [48.42347515853289]
  Transcranial Magnetic Stimulation (TMS) combined with EEG recordings (TMS-EEG) has shown great potential in the study of the brain and in particular of Alzheimer's Disease (AD) In this study, we propose an automatic method of determining the duration of TMS induced perturbation of the EEG signal as a potential metric reflecting the brain's functional alterations.
  arXiv  Detail & Related papers  (2022-04-29T19:27:06Z)
• Electrostimulation of Brain Deep Structures in Parkinson's Disease [0.0]
  The study involved 56 patients with advanced and late stages of Parkinsons disease. Electro stimulation of deep brain structures in Parkinsons disease significantly improved the condition of patients.
  arXiv  Detail & Related papers  (2021-09-18T06:01:46Z)
• Transfer Learning improves MI BCI models classification accuracy in Parkinson's disease patients [50.591267188664666]
  Motor-motorry based BCI (MIBCI) can improve ability and reduce deficit symptoms in Parkinson's Disease patients. Advanced MotorImagery BCI methods are needed to overcome accuracy and time-related calibration challenges. This study proposes a transfer learning-based FBCSP approach which allowed to significantly improve accuracy accuracy in MI BCI performed on PD patients.
  arXiv  Detail & Related papers  (2020-10-29T19:28:00Z)
• Review of Machine Learning Algorithms for Brain Stroke Diagnosis and Prognosis by EEG Analysis [50.591267188664666]
  Strokes are the leading cause of adult disability in the United States. Brain-Computer Interfaces (BCIs) help the patient either restore neurologic pathways or effectively communicate with an electronic prosthetic. The various machine learning techniques and algorithms that are addressed and combined with BCIs technology show that the use of BCIs for stroke treatment is a promising and rapidly expanding field.
  arXiv  Detail & Related papers  (2020-08-06T19:50:29Z)
• Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity [39.51722822896373]
  Parkinson's disease (PD) is a progressive neurological disorder affecting motor function. Physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson's Disease Rating Scale. We propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores.
  arXiv  Detail & Related papers  (2020-07-17T11:49:30Z)
• A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks [59.15734147867412]
  Characterizing the subtle changes of functional brain networks associated with Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G) We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
  arXiv  Detail & Related papers  (2020-05-08T02:29:24Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.