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Rodriguez F, Krauss P, Kluckert J, Ryser F, Stieglitz L, Baumann C, Gassert R, Imbach L, Bichsel O. Continuous and Unconstrained Tremor Monitoring in Parkinson's Disease Using Supervised Machine Learning and Wearable Sensors. PARKINSON'S DISEASE 2024; 2024:5787563. [PMID: 38803413 PMCID: PMC11129907 DOI: 10.1155/2024/5787563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 03/24/2024] [Accepted: 04/24/2024] [Indexed: 05/29/2024]
Abstract
Background Accurately assessing the severity and frequency of fluctuating motor symptoms is important at all stages of Parkinson's disease management. Contrarily to time-consuming clinical testing or patient self-reporting with uncertain reliability, recordings with wearable sensors show promise as a tool for continuously and objectively assessing PD symptoms. While wearables-based clinical assessments during standardised and scripted tasks have been successfully implemented, assessments during unconstrained activity remain a challenge. Methods We developed and implemented a supervised machine learning algorithm, trained and tested on tremor scores. We evaluated the algorithm on a 67-hour database comprising sensor data and clinical tremor scores for 24 Parkinson patients at four extremities for periods of about 3 hours. A random 25% subset of the labelled samples was used as test data, the remainder as training data. Based on features extracted from the sensor data, a Support Vector Machine was trained to predict tremor severity. Due to the inherent imbalance in tremor scores, we applied dataset rebalancing techniques. Results Our classifier demonstrated robust performance in detecting tremor events with a sensitivity of 0.90 on the test-portion of the resampled dataset. The overall classification accuracy was high at 0.88. Conclusion We implemented an accurate classifier for tremor monitoring in free-living environments that can be trained even with modestly sized and imbalanced datasets. This advancement offers significant clinical value in continuously monitoring Parkinson's disease symptoms beyond the hospital setting, paving the way for personalized management of PD, timely therapeutic adjustments, and improved patient quality of life.
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Affiliation(s)
- Fernando Rodriguez
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Philipp Krauss
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Clinical Neuroscience Centre, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Neurosurgery, University Hospital Augsburg, Augsburg, Germany
| | - Jonas Kluckert
- Clinical Neuroscience Centre, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Neurology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Franziska Ryser
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Lennart Stieglitz
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Clinical Neuroscience Centre, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Christian Baumann
- Clinical Neuroscience Centre, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Neurology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Roger Gassert
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Lukas Imbach
- Swiss Epilepsy Center, Klinik Lengg, Zurich, Switzerland
| | - Oliver Bichsel
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Clinical Neuroscience Centre, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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Sánchez-Fernández LP, Sánchez-Pérez LA, Concha-Gómez PD, Shaout A. Kinetic tremor analysis using wearable sensors and fuzzy inference systems in Parkinson's disease. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
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Ahmed Kamal M, Ismail Z, Shehata IM, Djirar S, Talbot NC, Ahmadzadeh S, Shekoohi S, Cornett EM, Fox CJ, Kaye AD. Telemedicine, E-Health, and Multi-Agent Systems for Chronic Pain Management. Clin Pract 2023; 13:470-482. [PMID: 36961067 PMCID: PMC10037594 DOI: 10.3390/clinpract13020042] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/12/2023] [Accepted: 03/16/2023] [Indexed: 03/25/2023] Open
Abstract
Telemedicine, telehealth, and E-health all offer significant benefits for pain management and healthcare services by fostering the physician-patient relationship in otherwise challenging circumstances. A critical component of these artificial-intelligence-based health systems is the "agent-based system", which is rapidly evolving as a means of resolving complicated or straightforward problems. Multi-Agent Systems (MAS) are well-established modeling and problem-solving modalities that model and solve real-world problems. MAS's core concept is to foster communication and cooperation among agents, which are broadly considered intelligent autonomous factors, to address diverse challenges. MAS are used in various telecommunications applications, including the internet, robotics, healthcare, and medicine. Furthermore, MAS and information technology are utilized to enhance patient-centered palliative care. While telemedicine, E-health, and MAS all play critical roles in managing chronic pain, the published research on their use in treating chronic pain is currently limited. This paper discusses why telemedicine, E-health, and MAS are the most critical novel technologies for providing healthcare and managing chronic pain. This review also provides context for identifying the advantages and disadvantages of each application's features, which may serve as a useful tool for researchers.
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Affiliation(s)
| | - Zainab Ismail
- Faculty of Medicine, Menoufia University, Shiben El Kom 51123, Egypt
| | - Islam Mohammad Shehata
- Department of Anesthesiology, Faculty of Medicine, Ain Shams University, Cairo 11517, Egypt
| | - Soumia Djirar
- Department of Ophthalmology, University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Norris C Talbot
- School of Medicine, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA 71103, USA
| | - Shahab Ahmadzadeh
- Department of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA 71103, USA
| | - Sahar Shekoohi
- Department of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA 71103, USA
| | - Elyse M Cornett
- Department of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA 71103, USA
| | - Charles J Fox
- Department of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA 71103, USA
| | - Alan D Kaye
- Department of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA 71103, USA
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Truong D, Shaikh A, Hallett M. Editorial: Tremors. J Neurol Sci 2022; 435:120189. [DOI: 10.1016/j.jns.2022.120189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 02/17/2022] [Indexed: 11/16/2022]
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