A Translational Wireless Deep Brain Stimulation Monitoring System for Chronic Brain Signal Recording to Automate Neural Disorder Onset Recording

William Drew, T. Denison, S. Stanslaski
{"title":"A Translational Wireless Deep Brain Stimulation Monitoring System for Chronic Brain Signal Recording to Automate Neural Disorder Onset Recording","authors":"William Drew, T. Denison, S. Stanslaski","doi":"10.1109/CBMS.2019.00027","DOIUrl":null,"url":null,"abstract":"Millions of people worldwide suffer from neurological disorders such as epilepsy, movement disorders, and obsessive-compulsive disorder (OCD), depression, and delirium. To provide relief from these disorders, brain stimulation therapies have been shown to be effective at controlling onsets of seizures, tremors, dyskinesia, dystonia, and OCD episodes. Current development of brain stimulation therapies has pivoted toward closed-loop control of sensing onset events and correspondingly delivering adaptive stimulation. Development of closed-loop brain stimulation therapies for neurological disorders rely on the identification of neural biomarkers. As such, a brain signal monitoring system that can chronically record these neurological events is essential to the continued development of neuromodulation systems and therapies. Through analyzing clinical data, neural disorder biomarkers can be identified and novel therapies can be optimized. This paper outlines the development of a translational deep brain stimulation monitoring system utilizing Medtronic's RC+S System to help clinicians and patients accurately record and document neural disorder onset events. With this neural data, stimulation therapy parameters can be adjusted using the system without requiring an in-person office visit. The system is capable of wirelessly communicating with multiple implanted neurostimulators, monitoring disorder onset biomarkers, and periodically downloading real-time brain signal data as well as loop recordings triggered by device-detected disorder onset events. This translational system and neural disorder onset data can be used to optimize therapies, minimize symptom onsets, enable episodic care management, and improve chronic care management.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2019.00027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Millions of people worldwide suffer from neurological disorders such as epilepsy, movement disorders, and obsessive-compulsive disorder (OCD), depression, and delirium. To provide relief from these disorders, brain stimulation therapies have been shown to be effective at controlling onsets of seizures, tremors, dyskinesia, dystonia, and OCD episodes. Current development of brain stimulation therapies has pivoted toward closed-loop control of sensing onset events and correspondingly delivering adaptive stimulation. Development of closed-loop brain stimulation therapies for neurological disorders rely on the identification of neural biomarkers. As such, a brain signal monitoring system that can chronically record these neurological events is essential to the continued development of neuromodulation systems and therapies. Through analyzing clinical data, neural disorder biomarkers can be identified and novel therapies can be optimized. This paper outlines the development of a translational deep brain stimulation monitoring system utilizing Medtronic's RC+S System to help clinicians and patients accurately record and document neural disorder onset events. With this neural data, stimulation therapy parameters can be adjusted using the system without requiring an in-person office visit. The system is capable of wirelessly communicating with multiple implanted neurostimulators, monitoring disorder onset biomarkers, and periodically downloading real-time brain signal data as well as loop recordings triggered by device-detected disorder onset events. This translational system and neural disorder onset data can be used to optimize therapies, minimize symptom onsets, enable episodic care management, and improve chronic care management.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种用于慢性脑信号记录的平移式无线深部脑刺激监测系统,实现神经紊乱发作记录的自动化
全世界有数百万人患有神经系统疾病,如癫痫、运动障碍、强迫症、抑郁症和谵妄。为了缓解这些疾病,脑刺激疗法已被证明对控制癫痫发作、震颤、运动障碍、肌张力障碍和强迫症发作有效。目前脑刺激疗法的发展已转向闭环控制的感觉发作事件和相应的提供适应性刺激。神经系统疾病的闭环脑刺激疗法的发展依赖于神经生物标志物的鉴定。因此,能够长期记录这些神经事件的脑信号监测系统对于神经调节系统和治疗的持续发展至关重要。通过分析临床数据,可以识别神经障碍的生物标志物,并优化新的治疗方法。本文概述了利用美敦力的RC+S系统开发一种转化式脑深部刺激监测系统,以帮助临床医生和患者准确记录和记录神经疾病的发病事件。有了这些神经数据,可以使用该系统调整刺激治疗参数,而不需要亲自去办公室。该系统能够与多个植入的神经刺激器进行无线通信,监测疾病发作的生物标志物,并定期下载实时脑信号数据,以及由设备检测到的疾病发作事件触发的循环记录。该转化系统和神经疾病发病数据可用于优化治疗,最小化症状发作,实现发作性护理管理,并改善慢性护理管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Analysing the Performance of a Real-Time Healthcare 4.0 System using Shared Frailty Time to Event Models Performance of Data Enhancements and Training Optimization for Neural Network: A Polyp Detection Case Study I Know How you Feel Now, and Here's why!: Demystifying Time-Continuous High Resolution Text-Based Affect Predictions in the Wild Identifying Diabetic Retinopathy from OCT Images using Deep Transfer Learning with Artificial Neural Networks Towards an Analysis of Post-Transcriptional Gene Regulation in Psoriasis via microRNAs using Machine Learning Algorithms
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1