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Carlino MF, Gielen G. Brain Feature Extraction With an Artifact-Tolerant Multiplexed Time-Encoding Neural Frontend for True Real-Time Closed-Loop Neuromodulation. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2024; 18:511-522. [PMID: 38117616 DOI: 10.1109/tbcas.2023.3344889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
Closed-loop neuromodulation is emerging as a more effective and targeted solution for the treatment of neurological symptoms compared to traditional open-loop stimulation. The majority of the present designs lack the ability to continuously record brain activity during electrical stimulation; hence they cannot fully monitor the treatment's effectiveness. This is due to the large stimulation artifacts that can saturate the sensitive readout circuits. To overcome this challenge, this work presents a rapid-artifact-recovery time-multiplexed neural readout frontend in combination with backend linear interpolation to reconstruct the artifact-corrupted local field potentials' (LFP) features. Our hybrid technique is an alternative approach to avoid power-hungry large-dynamic-range readout architectures or large and complex artifact template subtraction circuits. We discuss the design and measurements of a prototype implementation of the proposed readout frontend in 180-nm CMOS. It combines time multiplexing and time-domain conversion in a novel 13-bit incremental ADC, requiring only 0.0018 mm2/channel of readout area despite the large 180-nm CMOS process used, while consuming only 4.51 μW/channel. This is the smallest reported area for stimulation-voltage-compatible technologies (i.e. ≥ 65 nm). The frontend also yields a best-in-class peak total harmonic distortion of -72.6 dB @2.5-mVpp input, thanks to its implicit DAC mismatch-error shaping property. We employ the chip to measure brain LFP signals corrupted with artifacts, then perform linear interpolation and feature extraction on the measured signals and evaluate the reconstruction quality, using a set of sixteen commonly used features and three stimulation scenarios. The results show relative accuracies above 95% with respect to the situation without artifacts. This work is an ideal candidate for integration in high-channel-count true closed-loop neuromodulation systems.
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Fleming JE, Benjaber M, Toth R, Zamora M, Landin K, Kavoosi A, Ottoway J, Gillbe T, Piper RJ, Noone T, Campbell H, Gillbe I, Kaliakatsos M, Tisdall M, Valentín A, Denison T. An embedded intracranial seizure monitor for objective outcome measurements and rhythm identification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38083730 PMCID: PMC7615373 DOI: 10.1109/embc40787.2023.10340850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Providing clinicians with objective outcomes of neuromodulation therapy is a key unmet need, especially in emerging areas such as epilepsy and mood disorders. These diseases have episodic behavior and circadian/multidien rhythm characteristics that are difficult to capture in short clinical follow-ups. This work presents preliminary validation evidence for an implantable neuromodulation system with integrated physiological event monitoring, with an initial focus on seizure tracking for epilepsy. The system was developed to address currently unmet requirements for patients undergoing neuromodulation therapy for neurological disorders, specifically the ability to sense physiological data during stimulation and track events with seconds-level granularity. The system incorporates an interactive software tool to enable optimal configuration of the signal processing chain on an embedded implantable device (the Picostim-DyNeuMo Mk-2) including data ingestion from the device loop recorder, event labeling, generation of filter and classification parameters, as well as summary statistics. When the monitor parameters are optimized, the user can wirelessly update the system for chronic event tracking. The simulated performance of the device was assessed using an in silico model with human data to predict the real-time device performance at tracking recorded seizure activity. The in silico performance was then compared against its performance in an in vitro model to capture the full environmental constraints such as sensing during stimulation at the tissue electrode interface. In vitro modeling demonstrated comparable results to the in silico model, providing verification of the software tool and model. This study provides validation evidence of the suitability of the proposed system for tracking longitudinal seizure activity. Given its flexibility, the event monitor can be adapted to track other disorders with episodic and rhythmic symptoms represented by bioelectrical behavior.Clinical relevance-An implantable neuromodulation system is presented that enables chronic tracking of physiological events in disease. This physiological monitor provides the basis for longitudinal assessments of therapy outcomes for patients, such as those with epilepsy where objective identification of patient seizure activity and rhythms might help guide therapy optimization. The system is configurable for other disease states such as Parkinson's disease and mood disorders.
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Affiliation(s)
- John E. Fleming
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Moaad Benjaber
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Robert Toth
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Mayela Zamora
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Kei Landin
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Ali Kavoosi
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | | | | | - Rory J. Piper
- Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Department of Neurosurgery, Great Ormond Street Hospital, London WC1N 3JH, UK
| | | | | | | | - Marios Kaliakatsos
- Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
| | - Martin Tisdall
- Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Department of Neurosurgery, Great Ormond Street Hospital, London WC1N 3JH, UK
| | - Antonio Valentín
- Department of Basic and Clinical Neuroscience, King’s College London, London SE5 9RT, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
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Bahador N, Saha J, Rezaei MR, Utpal S, Ghahremani A, Chen R, Lankarany M. Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering. Bioengineering (Basel) 2023; 10:719. [PMID: 37370650 DOI: 10.3390/bioengineering10060719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/07/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience, wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on the ways in which a burst of DBS pulses, usually delivered at a clinical frequency, i.e., 130 Hz, perturb participants' choices. It was observed that neural activities recorded during DBS were contaminated with large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal (~1-2 Hz) that can persist for hundreds of milliseconds. While the focus of most of methods for removing DBS artifacts was on the former, the artifact removal capabilities of the slow signal have not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials, recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) normalized least mean square adaptive filtering, (2) optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) moving average. The algorithm's capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications.
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Affiliation(s)
- Nooshin Bahador
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON M5S 2E8, Canada
| | - Josh Saha
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Department of Electrical and Computer Engineering, University of Waterloo, Toronto, ON N2L 3G1, Canada
| | - Mohammad R Rezaei
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON M5S 2E8, Canada
| | - Saha Utpal
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
| | - Ayda Ghahremani
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Robert Chen
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Department of Medicine, Division of Neurology, University of Toronto, Toronto, ON M5S 2E8, Canada
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network (UHN), Toronto, ON M5G 2A2, Canada
| | - Milad Lankarany
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON M5S 2E8, Canada
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network (UHN), Toronto, ON M5G 2A2, Canada
- Department of Physiology, University of Toronto, Toronto, ON M5S 2E8, Canada
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Ansó J, Benjaber M, Parks B, Parker S, Oehrn CR, Petrucci M, Gilron R, Little S, Wilt R, Bronte-Stewart H, Gunduz A, Borton D, Starr PA, Denison T. Concurrent stimulation and sensing in bi-directional brain interfaces: a multi-site translational experience. J Neural Eng 2022; 19:10.1088/1741-2552/ac59a3. [PMID: 35234664 PMCID: PMC9095704 DOI: 10.1088/1741-2552/ac59a3] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/01/2022] [Indexed: 11/12/2022]
Abstract
Objective. To provide a design analysis and guidance framework for the implementation of concurrent stimulation and sensing during adaptive deep brain stimulation (aDBS) with particular emphasis on artifact mitigations.Approach. We defined a general architecture of feedback-enabled devices, identified key components in the signal chain which might result in unwanted artifacts and proposed methods that might ultimately enable improved aDBS therapies. We gathered data from research subjects chronically-implanted with an investigational aDBS system, Summit RC + S, to characterize and explore artifact mitigations arising from concurrent stimulation and sensing. We then used a prototype investigational implantable device, DyNeuMo, and a bench-setup that accounts for tissue-electrode properties, to confirm our observations and verify mitigations. The strategies to reduce transient stimulation artifacts and improve performance during aDBS were confirmed in a chronic implant using updated configuration settings.Main results.We derived and validated a 'checklist' of configuration settings to improve system performance and areas for future device improvement. Key considerations for the configuration include (a) active instead of passive recharge, (b) sense-channel blanking in the amplifier, (c) high-pass filter settings, (d) tissue-electrode impedance mismatch management, (e) time-frequency trade-offs in the classifier, (f) algorithm blanking and transition rate limits. Without proper channel configuration, the aDBS algorithm was susceptible to limit-cycles of oscillating stimulation independent of physiological state. By applying the checklist, we could optimize each block's performance characteristics within the overall system. With system-level optimization, a 'fast' aDBS prototype algorithm was demonstrated to be feasible without reentrant loops, and with noise performance suitable for subcortical brain circuits.Significance. We present a framework to study sources and propose mitigations of artifacts in devices that provide chronic aDBS. This work highlights the trade-offs in performance as novel sensing devices translate to the clinic. Finding the appropriate balance of constraints is imperative for successful translation of aDBS therapies.Clinical trial:Institutional Review Board and Investigational Device Exemption numbers: NCT02649166/IRB201501021 (University of Florida), NCT04043403/IRB52548 (Stanford University), NCT03582891/IRB1824454 (University of California San Francisco). IDE #180 097.
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Affiliation(s)
- Juan Ansó
- Department of Neurological Surgery, University of California, San Francisco, CA, United States of America
- Shared first author
| | - Moaad Benjaber
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Shared first author
| | - Brandon Parks
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States of America
- Shared first author
| | - Samuel Parker
- School of Engineering and Carney Institute, Brown University, Providence, RI, United States of America
| | - Carina Renate Oehrn
- Department of Neurological Surgery, University of California, San Francisco, CA, United States of America
| | - Matthew Petrucci
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Ro’ee Gilron
- Department of Neurological Surgery, University of California, San Francisco, CA, United States of America
| | - Simon Little
- Department of Neurology, University of California San Francisco, San Francisco, CA, United States of America
| | - Robert Wilt
- Department of Neurological Surgery, University of California, San Francisco, CA, United States of America
| | - Helen Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Aysegul Gunduz
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America
| | - David Borton
- School of Engineering and Carney Institute, Brown University, Providence, RI, United States of America
| | - Philip A Starr
- Department of Neurological Surgery, University of California, San Francisco, CA, United States of America
- Shared senior author
| | - Timothy Denison
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- Shared senior author
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Mivalt F, Kremen V, Sladky V, Balzekas I, Nejedly P, Gregg N, Lundstrom B, Lepkova K, Pridalova T, Brinkmann BH, Jurak P, Van Gompel JJ, Miller K, Denison T, Louis ES, Worrell GA. Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans. J Neural Eng 2022; 19:10.1088/1741-2552/ac4bfd. [PMID: 35038687 PMCID: PMC9070680 DOI: 10.1088/1741-2552/ac4bfd] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 01/17/2022] [Indexed: 11/11/2022]
Abstract
Objective.Electrical deep brain stimulation (DBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy, and DBS may actually further disturb normal sleep patterns and sleep quality. Novel implantable devices capable of DBS and streaming of continuous intracranial electroencephalography (iEEG) signals enable detailed assessments of therapy efficacy and tracking of sleep related comorbidities. Here, we investigate the feasibility of automated sleep classification using continuous iEEG data recorded from Papez's circuit in four patients with drug resistant mesial temporal lobe epilepsy using an investigational implantable sensing and stimulation device with electrodes implanted in bilateral hippocampus (HPC) and anterior nucleus of thalamus (ANT).Approach.The iEEG recorded from HPC is used to classify sleep during concurrent DBS targeting ANT. Simultaneous polysomnography (PSG) and sensing from HPC were used to train, validate and test an automated classifier for a range of ANT DBS frequencies: no stimulation, 2 Hz, 7 Hz, and high frequency (>100 Hz).Main results.We show that it is possible to build a patient specific automated sleep staging classifier using power in band features extracted from one HPC iEEG sensing channel. The patient specific classifiers performed well under all thalamic DBS frequencies with an average F1-score 0.894, and provided viable classification into awake and major sleep categories, rapid eye movement (REM) and non-REM. We retrospectively analyzed classification performance with gold-standard PSG annotations, and then prospectively deployed the classifier on chronic continuous iEEG data spanning multiple months to characterize sleep patterns in ambulatory patients living in their home environment.Significance.The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing DBS for epilepsy and associated sleep, cognitive and mood comorbidities.
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Affiliation(s)
- Filip Mivalt
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
| | - Vaclav Kremen
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Vladimir Sladky
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Irena Balzekas
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Mayo Clinic School of Medicine and the Mayo Clinic Medical Scientist Training Program, Rochester, MN, USA
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, USA
| | - Petr Nejedly
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- The Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czech Republic
| | - Nick Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Brian Lundstrom
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Kamila Lepkova
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
| | - Tereza Pridalova
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Pavel Jurak
- The Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czech Republic
| | | | - Kai Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA
| | - Timothy Denison
- Department of Biomedical Engineering, Oxford University, Oxford, UK
| | - Erik St Louis
- Center for Sleep Medicine, Departments of Neurology and Medicine, Divisions of Sleep Neurology & Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
| | - Gregory A. Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
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