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Hopfner F, Buhmann C, Classen J, Holtbernd F, Klebe S, Koschel J, Kohl Z, Paus S, Pedrosa DJ. Tips and tricks in tremor treatment. J Neural Transm (Vienna) 2024:10.1007/s00702-024-02806-x. [PMID: 39043978 DOI: 10.1007/s00702-024-02806-x] [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: 04/09/2024] [Accepted: 07/04/2024] [Indexed: 07/25/2024]
Abstract
Tremor, whether arising from neurological diseases, other conditions, or medication side effects, significantly impacts patients' lives. Treatment complexities necessitate clear algorithms and strategies. Levodopa remains pivotal for Parkinson's tremor, though response variability exists. Some dopamine agonists offer notable tremor reduction targeting D2 receptors. Propranolol effectively manages essential tremor and essential tremor plus (ET/ET +), sometimes with primidone for added benefits, albeit dose-dependent side effects. As reserve medications anticholinergics and clozapine are used for treatment of parkinsonian tremor, 1-Octanol and certain anticonvulsant drugs for tremor of other orign, especially ET. Therapies such as invasive deep brain stimulation and lesional focused ultrasound serve for resistant cases. A medication review is crucial for all forms of tremor, but it is particularly important if medication may have triggered the tremor. Sensor-based detection and non-drug interventions like wristbands and physical therapy broaden diagnostic and therapeutic horizons, promising future tremor care enhancements. Understanding treatment nuances is a key for tailored tremor management respecting patient needs and tolerability. Successful strategies integrate pharmacological, non-invasive, and technological modalities, aiming for optimal symptom control and improved quality of life.
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Affiliation(s)
- Franziska Hopfner
- Department of Neurology, Neurologische Klinik und Poliklinik mit Friedrich Baur Institut, Ludwig-Maximilians University, Campus Großhadern, Marchioninistraße 15, 81377, Munich, Germany.
| | - Carsten Buhmann
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Joseph Classen
- Department of Neurology, Leipzig University Medical Center, Liebigstraße 20, 04103, Leipzig, Germany
| | - Florian Holtbernd
- Department of Neurology, RWTH Aachen University, Pauwelsstraße 30, Aachen, Germany
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Juelich Research Center GmbH and RWTH Aachen University, Aachen, Germany
| | - Stephan Klebe
- Department of Neurology, Essen University Hospital, 45147, Essen, Germany
- Department of Neurology, Knappschaftskrankenhaus Recklinghausen, Recklinghausen, Germany
| | - Jiri Koschel
- Parkinson-Klinik Ortenau, GmbH & Co KG, Kreuzbergstraße 12-16, 77709, Wolfach, Germany
| | - Zacharias Kohl
- Department of Neurology, University of Regensburg, Regensburg, Germany
| | - Sebastian Paus
- Department of Neurology, GFO Clinics Troisdorf, Troisdorf, Germany
| | - David J Pedrosa
- Department of Neurology, Philipps University Marburg, Marburg, Germany
- Centre for Mind, Brain and Behaviour, Philipps University Marburg, Marburg, Germany
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2
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Koirala N, Hossen A, Isaias IU, Volkmann J, Muthuraman M. Assistive techniques and their added value for tremor classification in multiple sclerosis. Neural Regen Res 2024; 19:977-978. [PMID: 37862196 PMCID: PMC10749605 DOI: 10.4103/1673-5374.382988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 07/06/2023] [Accepted: 07/25/2023] [Indexed: 10/22/2023] Open
Affiliation(s)
- Nabin Koirala
- Child Study Center, School of Medicine, Yale University, New Haven, CT, USA
| | - Abdulnasir Hossen
- Department of Electrical & Computer Engineering, Sultan Qaboos University, Al-Khod, Muscat, Oman
| | - Ioannis U. Isaias
- Neural Engineering with Signal Analytics and Artificial Intelligence, Department of Neurology, University Hospital of Würzburg, Würzburg, Germany
- Centro Parkinson e Parkinsonism, Azienda Socio Sanitaria Territoriale G. Pini-CTO, 20126 Milan, Italy
| | - Jens Volkmann
- Neural Engineering with Signal Analytics and Artificial Intelligence, Department of Neurology, University Hospital of Würzburg, Würzburg, Germany
| | - Muthuraman Muthuraman
- Neural Engineering with Signal Analytics and Artificial Intelligence, Department of Neurology, University Hospital of Würzburg, Würzburg, Germany
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3
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Okelberry T, Lyons KE, Pahwa R. Updates in essential tremor. Parkinsonism Relat Disord 2024; 122:106086. [PMID: 38538475 DOI: 10.1016/j.parkreldis.2024.106086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 03/02/2024] [Indexed: 05/05/2024]
Abstract
Essential tremor (ET) is one of the most common tremor disorders and can be disabling in its affect on daily activities. There have been major breakthroughs in the treatment of tremor and ET is the subject of important ongoing research. This review will present recent advancements in the epidemiology, genetics, pathophysiology, diagnosis, comorbidities, and imaging of ET. Current and future treatment options in the management of ET will also be reviewed. The need for continued innovation and scientific inquiry to address the unmet needs of persons of ET will be highlighted.
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Affiliation(s)
- Tyler Okelberry
- University of Kansas Medical Center, 3599 Rainbow Blvd, Kansas City, KS, 66160, USA.
| | - Kelly E Lyons
- University of Kansas Medical Center, 3599 Rainbow Blvd, Kansas City, KS, 66160, USA
| | - Rajesh Pahwa
- University of Kansas Medical Center, 3599 Rainbow Blvd, Kansas City, KS, 66160, USA
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Xu T, Jost E, Messer LH, Cook PF, Forlenza GP, Sankaranarayanan S, Fiesler C, Voida S. "Obviously, Nothing's Gonna Happen in Five Minutes": How Adolescents and Young Adults Infrastructure Resources to Learn Type 1 Diabetes Management. PROCEEDINGS OF THE SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS. CHI CONFERENCE 2024; 2024:139. [PMID: 38846748 PMCID: PMC11153724 DOI: 10.1145/3613904.3642612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/09/2024]
Abstract
Learning personalized self-management routines is pivotal for people with type 1 diabetes (T1D), particularly early in diagnosis. Context-aware technologies, such as hybrid closed-loop (HCL) insulin pumps, are important tools for diabetes self-management. However, clinicians have observed that practices using these technologies involve significant individual differences. We conducted interviews with 20 adolescents and young adults who use HCL insulin pump systems for managing T1D, and we found that these individuals leverage both technological and non-technological means to maintain situational awareness about their condition. We discuss how these practices serve to infrastructure their self-management routines, including medical treatment, diet, and glucose measurement-monitoring routines. Our study provides insights into adolescents' and young adults' lived experiences of using HCL systems and related technology to manage diabetes, and contributes to a more nuanced understanding of how the HCI community can support the contextualized management of diabetes through technology design.
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Affiliation(s)
- Tian Xu
- Information Science, University of Colorado Boulder, Boulder, Colorado, USA
| | - Emily Jost
- University of Colorado Anschutz, Medical Campus, Aurora, Colorado, USA
| | - Laurel H Messer
- University of Colorado Anschutz, Medical Campus, Aurora, Colorado, USA
| | - Paul F Cook
- University of Colorado Anschutz, Medical Campus, Aurora, Colorado, USA
| | | | | | - Casey Fiesler
- Information Science, University of Colorado Boulder, Boulder, Colorado, USA
| | - Stephen Voida
- Information Science, University of Colorado Boulder, Boulder, Colorado, USA
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Paredes-Acuna N, Utpadel-Fischler D, Ding K, Thakor NV, Cheng G. Upper limb intention tremor assessment: opportunities and challenges in wearable technology. J Neuroeng Rehabil 2024; 21:8. [PMID: 38218890 PMCID: PMC10787996 DOI: 10.1186/s12984-023-01302-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 12/26/2023] [Indexed: 01/15/2024] Open
Abstract
BACKGROUND Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature. METHODOLOGY & RESULTS This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table. CONCLUSIONS Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.
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Affiliation(s)
- Natalia Paredes-Acuna
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany.
| | - Daniel Utpadel-Fischler
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Keqin Ding
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Nitish V Thakor
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Gordon Cheng
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany
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Angelini L, Terranova R, Lazzeri G, van den Berg KRE, Dirkx MF, Paparella G. The role of laboratory investigations in the classification of tremors. Neurol Sci 2023; 44:4183-4192. [PMID: 37814130 PMCID: PMC10641063 DOI: 10.1007/s10072-023-07108-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 09/28/2023] [Indexed: 10/11/2023]
Abstract
INTRODUCTION Tremor is the most common movement disorder. Although clinical examination plays a significant role in evaluating patients with tremor, laboratory tests are useful to classify tremors according to the recent two-axis approach proposed by the International Parkinson and Movement Disorders Society. METHODS In the present review, we will discuss the usefulness and applicability of the various diagnostic methods in classifying and diagnosing tremors. We will evaluate a number of techniques, including laboratory and genetic tests, neurophysiology, and neuroimaging. The role of newly introduced innovative tremor assessment methods will also be discussed. RESULTS Neurophysiology plays a crucial role in tremor definition and classification, and it can be useful for the identification of specific tremor syndromes. Laboratory and genetic tests and neuroimaging may be of paramount importance in identifying specific etiologies. Highly promising innovative technologies are being developed for both clinical and research purposes. CONCLUSIONS Overall, laboratory investigations may support clinicians in the diagnostic process of tremor. Also, combining data from different techniques can help improve understanding of the pathophysiological bases underlying tremors and guide therapeutic management.
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Affiliation(s)
- Luca Angelini
- Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università 30, 00185, Rome, Italy.
| | - Roberta Terranova
- Department of Medical, Surgical Sciences and Advanced Technologies "GF Ingrassia," University of Catania, Catania, Italy
| | - Giulia Lazzeri
- IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurology Unit, Milan, Italy
| | - Kevin R E van den Berg
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Michiel F Dirkx
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Giulia Paparella
- Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università 30, 00185, Rome, Italy
- IRCCS Neuromed, Pozzilli (IS), Italy
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Battista L, Casali M, Brusa L, Radicati FG, Stocchi F. Clinical assessment of a new wearable tool for continuous and objective recording of motor fluctuations and ON/OFF states in patients with Parkinson's disease. PLoS One 2023; 18:e0287139. [PMID: 37796842 PMCID: PMC10553324 DOI: 10.1371/journal.pone.0287139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 05/30/2023] [Indexed: 10/07/2023] Open
Abstract
Clinical rating scales typically includes subjective evaluations, and their time-limited duration may fail to capture daily fluctuations in motor symptoms resulting from Parkinson's disease (PD). Recently, a new tool (i.e. the PD-Watch) has been proposed for the objective and continuous assessment of PD motor manifestations based on evaluating frequency data from a wrist-worn tri-axial accelerometer and identifying specific movement patterns typically associated with disorders. This reduces the probability of confusing physiological or pathological movements occurring at the same frequency. In this work, we present a new method for assessing motor fluctuations through a wrist-worn accelerometer. We also explore the agreement between the continuous data generated by the proposed method and data reported in the patient diaries. In this study, twelve PD patients were recruited with an overall recording duration of 528 hours. Results of this preliminary study show that the proposed tool has suitable and adequate performances for analysing the motor signs of PD patients, and the estimated sensitivity, specificity, and accuracy of the tool are 85%, 94%, and 91%, respectively.
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Affiliation(s)
| | - Miriam Casali
- Department of Neurology, Institute of Research and Medical Care IRCCS San Raffaele, Rome, Italy
| | - Livia Brusa
- Department of Neurology, Ospedale S. Eugenio, Rome, Italy
| | - Fabiana Giada Radicati
- Department of Neurology, Institute of Research and Medical Care IRCCS San Raffaele, Rome, Italy
| | - Fabrizio Stocchi
- Department of Neurology, Institute of Research and Medical Care IRCCS San Raffaele, Rome, Italy
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Vescio B, De Maria M, Crasà M, Nisticò R, Calomino C, Aracri F, Quattrone A, Quattrone A. Development of a New Wearable Device for the Characterization of Hand Tremor. Bioengineering (Basel) 2023; 10:1025. [PMID: 37760127 PMCID: PMC10525186 DOI: 10.3390/bioengineering10091025] [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: 07/19/2023] [Revised: 08/17/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
Rest tremor (RT) is observed in subjects with Parkinson's disease (PD) and Essential Tremor (ET). Electromyography (EMG) studies have shown that PD subjects exhibit alternating contractions of antagonistic muscles involved in tremors, while the contraction pattern of antagonistic muscles is synchronous in ET subjects. Therefore, the RT pattern can be used as a potential biomarker for differentiating PD from ET subjects. In this study, we developed a new wearable device and method for differentiating alternating from a synchronous RT pattern using inertial data. The novelty of our approach relies on the fact that the evaluation of synchronous or alternating tremor patterns using inertial sensors has never been described so far, and current approaches to evaluate the tremor patterns are based on surface EMG, which may be difficult to carry out for non-specialized operators. This new device, named "RT-Ring", is based on a six-axis inertial measurement unit and a Bluetooth Low-Energy microprocessor, and can be worn on a finger of the tremulous hand. A mobile app guides the operator through the whole acquisition process of inertial data from the hand with RT, and the prediction of tremor patterns is performed on a remote server through machine learning (ML) models. We used two decision tree-based algorithms, XGBoost and Random Forest, which were trained on features extracted from inertial data and achieved a classification accuracy of 92% and 89%, respectively, in differentiating alternating from synchronous tremor segments in the validation set. Finally, the classification response (alternating or synchronous RT pattern) is shown to the operator on the mobile app within a few seconds. This study is the first to demonstrate that different electromyographic tremor patterns have their counterparts in terms of rhythmic movement features, thus making inertial data suitable for predicting the muscular contraction pattern of tremors.
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Affiliation(s)
- Basilio Vescio
- Biotecnomed S.C.aR.L., Viale Europa, 88100 Catanzaro, Italy;
| | - Marida De Maria
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Graecia”, Viale Europa, 88100 Catanzaro, Italy; (M.D.M.); (M.C.); (R.N.); (C.C.); (F.A.); (A.Q.)
| | - Marianna Crasà
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Graecia”, Viale Europa, 88100 Catanzaro, Italy; (M.D.M.); (M.C.); (R.N.); (C.C.); (F.A.); (A.Q.)
| | - Rita Nisticò
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Graecia”, Viale Europa, 88100 Catanzaro, Italy; (M.D.M.); (M.C.); (R.N.); (C.C.); (F.A.); (A.Q.)
| | - Camilla Calomino
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Graecia”, Viale Europa, 88100 Catanzaro, Italy; (M.D.M.); (M.C.); (R.N.); (C.C.); (F.A.); (A.Q.)
| | - Federica Aracri
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Graecia”, Viale Europa, 88100 Catanzaro, Italy; (M.D.M.); (M.C.); (R.N.); (C.C.); (F.A.); (A.Q.)
| | - Aldo Quattrone
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Graecia”, Viale Europa, 88100 Catanzaro, Italy; (M.D.M.); (M.C.); (R.N.); (C.C.); (F.A.); (A.Q.)
| | - Andrea Quattrone
- Institute of Neurology, Department of Medical and Surgical Sciences, University “Magna Graecia”, Viale Europa, 88100 Catanzaro, Italy
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Guerra A, D'Onofrio V, Ferreri F, Bologna M, Antonini A. Objective measurement versus clinician-based assessment for Parkinson's disease. Expert Rev Neurother 2023; 23:689-702. [PMID: 37366316 DOI: 10.1080/14737175.2023.2229954] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/18/2023] [Accepted: 06/22/2023] [Indexed: 06/28/2023]
Abstract
INTRODUCTION Although clinician-based assessment through standardized clinical rating scales is currently the gold standard for quantifying motor impairment in Parkinson's disease (PD), it is not without limitations, including intra- and inter-rater variability and a degree of approximation. There is increasing evidence supporting the use of objective motion analyses to complement clinician-based assessment. Objective measurement tools hold significant potential for improving the accuracy of clinical and research-based evaluations of patients. AREAS COVERED The authors provide several examples from the literature demonstrating how different motion measurement tools, including optoelectronics, contactless and wearable systems allow for both the objective quantification and monitoring of key motor symptoms (such as bradykinesia, rigidity, tremor, and gait disturbances), and the identification of motor fluctuations in PD patients. Furthermore, they discuss how, from a clinician's perspective, objective measurements can help in various stages of PD management. EXPERT OPINION In our opinion, sufficient evidence supports the assertion that objective monitoring systems enable accurate evaluation of motor symptoms and complications in PD. A range of devices can be utilized not only to support diagnosis but also to monitor motor symptom during the disease progression and can become relevant in the therapeutic decision-making process.
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Affiliation(s)
- Andrea Guerra
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | | | - Florinda Ferreri
- Unit of Neurology, Unit of Clinical Neurophysiology, Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
- Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Matteo Bologna
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Angelo Antonini
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
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Wang S, Zhu G, Shi L, Zhang C, Wu B, Yang A, Meng F, Jiang Y, Zhang J. Closed-Loop Adaptive Deep Brain Stimulation in Parkinson's Disease: Procedures to Achieve It and Future Perspectives. JOURNAL OF PARKINSON'S DISEASE 2023:JPD225053. [PMID: 37182899 DOI: 10.3233/jpd-225053] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Parkinson's disease (PD) is a neurodegenerative disease with a heavy burden on patients, families, and society. Deep brain stimulation (DBS) can improve the symptoms of PD patients for whom medication is insufficient. However, current open-loop uninterrupted conventional DBS (cDBS) has inherent limitations, such as adverse effects, rapid battery consumption, and a need for frequent parameter adjustment. To overcome these shortcomings, adaptive DBS (aDBS) was proposed to provide responsive optimized stimulation for PD. This topic has attracted scientific interest, and a growing body of preclinical and clinical evidence has shown its benefits. However, both achievements and challenges have emerged in this novel field. To date, only limited reviews comprehensively analyzed the full framework and procedures for aDBS implementation. Herein, we review current preclinical and clinical data on aDBS for PD to discuss the full procedures for its achievement and to provide future perspectives on this treatment.
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Affiliation(s)
- Shu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Guanyu Zhu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lin Shi
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunkui Zhang
- Center of Cognition and Brain Science, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Bing Wu
- Center of Cognition and Brain Science, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Anchao Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fangang Meng
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Yin Jiang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
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Lu T, Ji S, Jin W, Yang Q, Luo Q, Ren TL. Biocompatible and Long-Term Monitoring Strategies of Wearable, Ingestible and Implantable Biosensors: Reform the Next Generation Healthcare. SENSORS (BASEL, SWITZERLAND) 2023; 23:2991. [PMID: 36991702 PMCID: PMC10054135 DOI: 10.3390/s23062991] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/31/2022] [Accepted: 01/04/2023] [Indexed: 06/19/2023]
Abstract
Sensors enable the detection of physiological indicators and pathological markers to assist in the diagnosis, treatment, and long-term monitoring of diseases, in addition to playing an essential role in the observation and evaluation of physiological activities. The development of modern medical activities cannot be separated from the precise detection, reliable acquisition, and intelligent analysis of human body information. Therefore, sensors have become the core of new-generation health technologies along with the Internet of Things (IoTs) and artificial intelligence (AI). Previous research on the sensing of human information has conferred many superior properties on sensors, of which biocompatibility is one of the most important. Recently, biocompatible biosensors have developed rapidly to provide the possibility for the long-term and in-situ monitoring of physiological information. In this review, we summarize the ideal features and engineering realization strategies of three different types of biocompatible biosensors, including wearable, ingestible, and implantable sensors from the level of sensor designing and application. Additionally, the detection targets of the biosensors are further divided into vital life parameters (e.g., body temperature, heart rate, blood pressure, and respiratory rate), biochemical indicators, as well as physical and physiological parameters based on the clinical needs. In this review, starting from the emerging concept of next-generation diagnostics and healthcare technologies, we discuss how biocompatible sensors revolutionize the state-of-art healthcare system unprecedentedly, as well as the challenges and opportunities faced in the future development of biocompatible health sensors.
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Affiliation(s)
- Tian Lu
- School of Integrated Circuit and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Shourui Ji
- School of Integrated Circuit and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Weiqiu Jin
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Qisheng Yang
- School of Integrated Circuit and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Qingquan Luo
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Tian-Ling Ren
- School of Integrated Circuit and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
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12
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Kleinholdermann U, Bacara B, Timmermann L, Pedrosa DJ. Prediction of Movement Ratings and Deep Brain Stimulation Parameters in Idiopathic Parkinson's Disease. Neuromodulation 2023; 26:356-363. [PMID: 36396526 DOI: 10.1016/j.neurom.2022.09.010] [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: 06/06/2022] [Revised: 08/24/2022] [Accepted: 09/13/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Deep brain stimulation (DBS) parameter fine-tuning after lead implantation is laborious work because of the almost uncountable possible combinations. Patients and practitioners often gain the perception that assistive devices could be beneficial for adjusting settings effectively. OBJECTIVE We aimed at a proof-of-principle study to assess the benefits of noninvasive movement recordings as a means to predict best DBS settings. MATERIALS AND METHODS For this study, 32 patients with idiopathic Parkinson's disease, under chronic subthalamic nucleus stimulation with directional leads, were recorded. During monopolar review, each available contact was activated with currents between 0.5 and 5 mA, and diadochokinesia, rigidity, and tapping ability were rated clinically. Moreover, participants' movements were measured during four simple hand movement tasks while wearing a commercially available armband carrying an inertial measurement unit (IMU). We trained random forest models to learn the relations between clinical ratings, electrode settings, and movement features obtained from the IMU. RESULTS Firstly, we could show that clinical mobility ratings can be predicted from IMU features with correlations of up to r = 0.68 between true and predicted values. Secondly, these features also enabled a prediction of DBS parameters, which showed correlations of up to approximately r = 0.8 with clinically optimal DBS settings and were associated with congruent volumes of tissue activated. CONCLUSION Movement recordings from customer-grade mobile IMU carrying devices are promising candidates, not only for remote symptom assessment but also for closed-loop DBS parameter adjustment, and could thus extend the list of available aids for effective programming beyond imaging techniques.
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Affiliation(s)
- Urs Kleinholdermann
- Department of Neurology, University Hospital of Marburg and Gießen, Baldingerstraße, Marburg, Germany
| | - Bugrahan Bacara
- Department of Neurology, University Hospital of Marburg and Gießen, Baldingerstraße, Marburg, Germany
| | - Lars Timmermann
- Department of Neurology, University Hospital of Marburg and Gießen, Baldingerstraße, Marburg, Germany; Center of Mind, Brain and Behaviour, Philipps University Marburg, Hans-Meerwein-Straße, Marburg, Germany
| | - David J Pedrosa
- Department of Neurology, University Hospital of Marburg and Gießen, Baldingerstraße, Marburg, Germany; Center of Mind, Brain and Behaviour, Philipps University Marburg, Hans-Meerwein-Straße, Marburg, Germany.
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13
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Sahin G, Halje P, Uzun S, Jakobsson A, Petersson P. Tremor evaluation using smartphone accelerometry in standardized settings. Front Neurosci 2022; 16:861668. [PMID: 35979340 PMCID: PMC9376601 DOI: 10.3389/fnins.2022.861668] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 07/12/2022] [Indexed: 11/13/2022] Open
Abstract
Tremor can be highly incapacitating in everyday life and typically fluctuates depending on motor state, medication status as well as external factors. For tremor patients being treated with deep-brain stimulation (DBS), adapting the intensity and pattern of stimulation according the current needs therefore has the potential to generate better symptomatic relief. We here describe a procedure for how patients independently could perform self-tests in their home to generate sensor data for on-line adjustments of DBS parameters. Importantly, the inertia sensor technology needed exists in any standard smartphone, making the procedure widely accessible. Applying this procedure, we have characterized detailed features of tremor patterns displayed by both Parkinson’s disease and essential tremor patients and directly compared measured data against both clinical ratings (Fahn-Tolosa-Marin) and finger-attached inertia sensors. Our results suggest that smartphone accelerometry, when used in a standardized testing procedure, can provide tremor descriptors that are sufficiently detailed and reliable to be used for closed-loop control of DBS.
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Affiliation(s)
- Gürdal Sahin
- Integrative Neurophysiology and Neurotechnology, Department of Experimental Medical Sciences, Lund University, Lund, Sweden
- Department of Internal Medicine, Hässleholm Hospital, Region Skåne, Hässleholm, Sweden
- Skåneuro Neurology Clinic, Lund, Sweden
| | - Pär Halje
- Integrative Neurophysiology and Neurotechnology, Department of Experimental Medical Sciences, Lund University, Lund, Sweden
| | - Sena Uzun
- Skåneuro Neurology Clinic, Lund, Sweden
- Department of Clinical Sciences of Malmö and Lund, Lund University, Lund, Sweden
| | - Andreas Jakobsson
- Centre for Mathematical Sciences, Mathematical Statistics, Lund University, Lund, Sweden
| | - Per Petersson
- Integrative Neurophysiology and Neurotechnology, Department of Experimental Medical Sciences, Lund University, Lund, Sweden
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
- *Correspondence: Per Petersson,
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14
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Polat EO, Cetin MM, Tabak AF, Bilget Güven E, Uysal BÖ, Arsan T, Kabbani A, Hamed H, Gül SB. Transducer Technologies for Biosensors and Their Wearable Applications. BIOSENSORS 2022; 12:385. [PMID: 35735533 PMCID: PMC9221076 DOI: 10.3390/bios12060385] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/16/2022] [Accepted: 05/27/2022] [Indexed: 05/17/2023]
Abstract
The development of new biosensor technologies and their active use as wearable devices have offered mobility and flexibility to conventional western medicine and personal fitness tracking. In the development of biosensors, transducers stand out as the main elements converting the signals sourced from a biological event into a detectable output. Combined with the suitable bio-receptors and the miniaturization of readout electronics, the functionality and design of the transducers play a key role in the construction of wearable devices for personal health control. Ever-growing research and industrial interest in new transducer technologies for point-of-care (POC) and wearable bio-detection have gained tremendous acceleration by the pandemic-induced digital health transformation. In this article, we provide a comprehensive review of transducers for biosensors and their wearable applications that empower users for the active tracking of biomarkers and personal health parameters.
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Affiliation(s)
- Emre Ozan Polat
- Faculty of Engineering and Natural Sciences, Kadir Has University, Cibali, Istanbul 34083, Turkey; (M.M.C.); (A.F.T.); (E.B.G.); (B.Ö.U.); (T.A.); (A.K.); (H.H.); (S.B.G.)
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15
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Evaluation of rest tremor in different positions in Parkinson’s disease and essential tremor plus. Neurol Sci 2022; 43:3621-3627. [DOI: 10.1007/s10072-022-05885-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 01/11/2022] [Indexed: 10/19/2022]
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16
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Novel Device Used to Monitor Hand Tremors during Nocturnal Hypoglycemic Events. INVENTIONS 2022. [DOI: 10.3390/inventions7020032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Diabetes is one of the lifelong diseases that require systematic medical care to avoid life-menacing ramifications. Uncontrolled diabetes can cause severe damage to most internal body organs, probably leading to death. Particularly, nocturnal hypoglycemic that occur usually at night during sleep. Severe cases of these events can lead to seizures, fainting, loss of consciousness, and death. The current medical devices lack to give the warning to reduce the risk of acquiring nocturnal hypoglycemic events because they use only for glucose monitoring during waking times. Consequently, the main goal of this work is to design and implement a new wearable device to detect and monitor tremors, which occur when a user has hypoglycemia (low blood sugar). The device can detect a frequency range of 4–12 Hz by using the accelerometer of Arduino Nano 33 BLE. It can send a signal to the phone application (app) via Bluetooth Low Energy (BLE). Once the phone receives a signal, the phone application can activate an alarm system to wake up the patient, call three selected contacts number, and universal emergency number. In case of the user is unresponsive, the app can provide the patient’s location, name, and date of birth to the emergency contacts numbers and universal emergency number. Additionally, the device cost is economically feasible and competitive compared to other medical devices.
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17
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Pascual-Valdunciel A, Rajagopal A, Pons JL, Delp S. Non-invasive electrical stimulation of peripheral nerves for the management of tremor. J Neurol Sci 2022; 435:120195. [PMID: 35220113 PMCID: PMC9590374 DOI: 10.1016/j.jns.2022.120195] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 12/06/2021] [Accepted: 02/17/2022] [Indexed: 12/22/2022]
Abstract
Pathological tremor in patients with essential tremor and Parkinsons disease is typically treated using medication or neurosurgical interventions. There is a widely recognized need for new treatments that avoid the side effects of current medications and do not carry the risks of surgical interventions. Building on decades of research and engineering development, non-invasive electrical stimulation of peripheral nerves has emerged as a safe and effective strategy for reducing pathologic tremor in essential tremor. This review surveys the peripheral electrical stimulation (PES) literature and summarizes effectiveness, safety, clinical translatability, and hypothesized tremor-reduction mechanisms of various PES approaches. The review also proposes guidelines for assessing tremor in the context of evaluating new therapies that combine the strengths of clinician assessments, patient evaluations, and novel motion sensing technology. The review concludes with a summary of future directions for PES, including expanding clinical access for patients with Parkinson's disease and leveraging large, at-home datasets to learn more about tremor physiology and treatment effect that will better characterize the state of tremor management and accelerate discovery of new therapies. Growing evidence suggests that non-invasive electrical stimulation of afferent neural pathways provides a viable new option for management of pathological tremor, with one specific PES therapy cleared for prescription and home use, suggesting that PES be considered along with medication and neurosurgical interventions for treatment of tremor. This article is part of the Special Issue "Tremor" edited by Daniel D. Truong, Mark Hallett, and Aasef Shaikh.
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Affiliation(s)
- Alejandro Pascual-Valdunciel
- Northwestern University, Evanston, IL, USA; E.T.S. Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Spain
| | | | - Jose L Pons
- Northwestern University, Evanston, IL, USA; Shirley Ryan AbilityLab, Chicago, IL, USA.
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