Stimulation-Induced Artifact Removal of the Local Field Potential Through Hardware Design: Toward the Implantable Closed-Loop Deep Brain Stimulation

被引:0
作者
Wu, Yi-Hui [1 ]
Lin, Hsiao-Chun [1 ]
Huang, Chi-Wei [1 ]
Wu, Chung-Yu [1 ,2 ]
Ker, Ming-Dou [1 ,2 ]
机构
[1] Natl Yang Ming Chiao Tung Univ, Biomed Elect Translat Res Ctr, Hsinchu 300, Taiwan
[2] Natl Yang Ming Chiao Tung Univ, Inst Elect, Hsinchu 300, Taiwan
来源
IEEE ACCESS | 2024年 / 12卷
关键词
Electrodes; Motors; Local field potentials; Sensors; Electrical stimulation; Physiology; Deep brain stimulation; Oscillators; Pathology; Neural activity; Closed loop systems; deep brain stimulation; implants; local field potential; Parkinson's disease; stimulation-induced artifact; NEUROMODULATION DEVICE; DISEASE;
D O I
10.1109/ACCESS.2024.3498053
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Deep brain stimulation is a standard neurosurgery to treat advanced Parkinson's disease patients. An innovative technology known as closed-loop deep brain stimulation is under development. This technology aims to identify abnormal biomarker signals within the brain, and create novel systems featuring sophisticated hardware configurations to generate improved therapeutic approaches and more favorable outcomes. The primary challenge faced in advancing closed-loop deep brain stimulation is managing artifacts induced by electrical stimulation within the signal detection module. A notable circuit design challenge involves continuously monitoring local field potential alterations during electrical stimulation. The artifacts arising from the stimulation can be categorized into common-mode artifact voltage and differential-mode artifact voltage. Within this article, a comprehensive review encompasses recent methodologies designed to mitigate common-mode artifact voltage and differential-mode artifact voltage in local field potential through hardware-centric techniques, including filtering, template removal, blanking, and selective sampling. The inherent strengths and limitations of these strategies are compared and discussed. This article allows engineers to recognize appropriate artifact removal techniques to achieve an implantable closed-loop deep brain stimulation system. To this end, a more intelligent and more precise system could be developed for the treatment of Parkinson's disease and other neurological disorders.
引用
收藏
页码:171488 / 171499
页数:12
相关论文
共 49 条
  • [31] Real-time removal of stimulation artifacts in closed-loop deep brain stimulation
    Nie, Yingnan
    Guo, Xuanjun
    Li, Xiao
    Geng, Xinyi
    Li, Yan
    Quan, Zhaoyu
    Zhu, Guanyu
    Yin, Zixiao
    Zhang, Jianguo
    Wang, Shouyan
    [J]. JOURNAL OF NEURAL ENGINEERING, 2021, 18 (06)
  • [32] A high-performance 4 nV (√Hz)-1 analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation
    Petkos, Konstantinos
    Guiho, Thomas
    Degenaar, Patrick
    Jackson, Andrew
    Brown, Peter
    Denison, Timothy
    Drakakis, Emmanuel M.
    [J]. JOURNAL OF NEURAL ENGINEERING, 2019, 16 (06)
  • [33] Neural closed-loop deep brain stimulation for freezing of gait
    Petrucci, Matthew N.
    Neuville, Raumin S.
    Afzal, M. Furqan
    Velisar, Anca
    Anidi, Chioma M.
    Anderson, Ross W.
    Parker, Jordan E.
    O'Day, Johanna J.
    Wilkins, Kevin B.
    Bronte-Stewart, Helen M.
    [J]. BRAIN STIMULATION, 2020, 13 (05) : 1320 - 1322
  • [34] A Method for Removal of Deep Brain Stimulation Artifact From Local Field Potentials
    Qian, Xing
    Chen, Yue
    Feng, Yuan
    Ma, Bozhi
    Hao, Hongwei
    Li, Luming
    [J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2017, 25 (12) : 2217 - 2226
  • [35] An electronic device for artefact suppression in human local field potential recordings during deep brain stimulation
    Rossi, L.
    Foffani, G.
    Marceglia, S.
    Bracchi, F.
    Barbieri, S.
    Priori, A.
    [J]. JOURNAL OF NEURAL ENGINEERING, 2007, 4 (02) : 96 - 106
  • [36] A 0.338 cm3, Artifact-Free, 64-Contact Neuromodulation Platform for Simultaneous Stimulation and Sensing
    Rozgic, Dejan
    Hokhikyan, Vahagn
    Jiang, Wenlong
    Akita, Ippei
    Basir-Kazeruni, Sina
    Chandrakumar, Hariprasad
    Markovic, Dejan
    [J]. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, 2019, 13 (01) : 38 - 55
  • [37] Samiei A, 2021, IEEE J SOLID-ST CIRC, V56, P2142, DOI [10.1109/jssc.2021.3056040, 10.1109/JSSC.2021.3056040]
  • [38] Design and Validation of a Fully Implantable, Chronic, Closed-Loop Neuromodulation Device With Concurrent Sensing and Stimulation
    Stanslaski, Scott
    Afshar, Pedram
    Cong, Peng
    Giftakis, Jon
    Stypulkowski, Paul
    Carlson, Dave
    Linde, Dave
    Ullestad, Dave
    Avestruz, Al-Thaddeus
    Denison, Timothy
    [J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2012, 20 (04) : 410 - 421
  • [39] Adaptive deep brain stimulation for Parkinson's disease using motor cortex sensing
    Swann, Nicole C.
    de Hemptinne, Coralie
    Thompson, Margaret C.
    Miocinovic, Svjetlana
    Miller, Andrew M.
    Gilron, Ro'ee
    Ostrem, Jill L.
    Chizeck, Howard J.
    Starr, Philip A.
    [J]. JOURNAL OF NEURAL ENGINEERING, 2018, 15 (04)
  • [40] The modulatory effect of adaptive deep brain stimulation on beta bursts in Parkinson's disease
    Tinkhauser, Gerd
    Pogosyan, Alek
    Little, Simon
    Beudel, Martijn
    Herz, Damian M.
    Tan, Huiling
    Brown, Peter
    [J]. BRAIN, 2017, 140 : 1053 - 1067