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Bibbo D, De Marchis C, Schmid M, Ranaldi S. Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review. Physiol Meas 2023; 44:12TR01. [PMID: 38061062 DOI: 10.1088/1361-6579/ad133b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 12/07/2023] [Indexed: 12/27/2023]
Abstract
This article presents a systematic review aimed at mapping the literature published in the last decade on the use of machine learning (ML) for clinical decision-making through wearable inertial sensors. The review aims to analyze the trends, perspectives, strengths, and limitations of current literature in integrating ML and inertial measurements for clinical applications. The review process involved defining four research questions and applying four relevance assessment indicators to filter the search results, providing insights into the pathologies studied, technologies and setups used, data processing schemes, ML techniques applied, and their clinical impact. When combined with ML techniques, inertial measurement units (IMUs) have primarily been utilized to detect and classify diseases and their associated motor symptoms. They have also been used to monitor changes in movement patterns associated with the presence, severity, and progression of pathology across a diverse range of clinical conditions. ML models trained with IMU data have shown potential in improving patient care by objectively classifying and predicting motor symptoms, often with a minimally encumbering setup. The findings contribute to understanding the current state of ML integration with wearable inertial sensors in clinical practice and identify future research directions. Despite the widespread adoption of these technologies and techniques in clinical applications, there is still a need to translate them into routine clinical practice. This underscores the importance of fostering a closer collaboration between technological experts and professionals in the medical field.
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Affiliation(s)
- Daniele Bibbo
- Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, Italy
| | | | - Maurizio Schmid
- Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, Italy
| | - Simone Ranaldi
- Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, Italy
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ZhuParris A, de Goede AA, Yocarini IE, Kraaij W, Groeneveld GJ, Doll RJ. Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115243. [PMID: 37299969 DOI: 10.3390/s23115243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/23/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. OBJECTIVE This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers. METHODS This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed. RESULTS This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials. CONCLUSION mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.
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Affiliation(s)
- Ahnjili ZhuParris
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Annika A de Goede
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
| | - Iris E Yocarini
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Wessel Kraaij
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- The Netherlands Organisation for Applied Scientific Research (TNO), Anna van Buerenplein 1, 2595 DA, Den Haag, The Netherlands
| | - Geert Jan Groeneveld
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Robert Jan Doll
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
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Zhang X, Zhong X, Wang L, Li H, Yang L, Li X, Yu X, Xie A. Effects of soluble TREM2 on motor progression in Parkinson's disease. Neurosci Lett 2023; 807:137277. [PMID: 37105353 DOI: 10.1016/j.neulet.2023.137277] [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: 02/24/2023] [Revised: 04/07/2023] [Accepted: 04/23/2023] [Indexed: 04/29/2023]
Abstract
OBJECTIVES To determine whether sTREM2 is changed during the pathogenesis of PD and reflect motor decline in PD individuals METHODS: The cerebrospinal fluid (CSF) from PD and healthy individuals were obtained to measure the expression of sTREM2 and further to evaluate the motor function at baseline and after four years of follow-up using the Parkinson's Progression Markers Initiative (PPMI) database. The relationship between motor disease progression at baseline and longitudinal CSF sTREM2 was evaluated by linear mixed-effects (LME) and multiple linear regression (MLR) models. The change rates of the motor symptoms and sTREM2 level in CSF were further rigorously analyzed using the LME model. Cox proportional-hazards regression models were used to evaluate the predictive values of sTREM2 in CSF for motor progression. The regulatory role of CSF α-syn and Tau between sTREM2 and motor assessments was evaluated by Mediating effect analysis. RESULTS We found no significant difference in CSF sTREM2 levels between the PD and HC groups (p = 0.155). However, late-onset PD patients had higher CSF sTREM2 levels than early-onset PD patients (p = 0.044). The basal levels of sTREM2 could predict motor progression over the four years of follow-up. The change rate of CSF sTREM2 was correlated with the progressive deterioration of motor function in PD individuals. Our observations also showed that CSF Tau was a significant mediator of the association between CSF sTREM2 and total UPDRS scores and UPDRS III and tremor at baseline with 26.5% and 33.5%, and 28.7% mediation, respectively. CONCLUSION Our results indicated that CSF sTREM2 was associated with the prognosis of PD motor symptoms. Besides, CSF Tau could effectively mediate the association of sTREM2 with motor progression.
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Affiliation(s)
- Xue Zhang
- Department of Neurology, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao Cancer Hospital
| | - Xiaoling Zhong
- Department of Neurology, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao Cancer Hospital
| | - Ling Wang
- Department of Neurology, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao Cancer Hospital
| | - Haiyan Li
- Department of Neurology, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao Cancer Hospital
| | - Liying Yang
- Department of Neurology, Affiliated Hospital of Qingdao University
| | - Xiaoyuan Li
- Department of Neurology, Affiliated Hospital of Qingdao University
| | - Xiaolong Yu
- Department of Neurology, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao Cancer Hospital
| | - Anmu Xie
- Department of Neurology, Affiliated Hospital of Qingdao University.
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Rastegari E, Ali H, Marmelat V. Detection of Parkinson's Disease Using Wrist Accelerometer Data and Passive Monitoring. SENSORS (BASEL, SWITZERLAND) 2022; 22:9122. [PMID: 36501823 PMCID: PMC9738242 DOI: 10.3390/s22239122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 11/11/2022] [Accepted: 11/18/2022] [Indexed: 06/17/2023]
Abstract
Parkinson's disease is a neurodegenerative disorder impacting patients' movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitoring of this population's movement patterns over time. The ubiquity of wrist-worn accelerometry and the fact that the wrist is the most common and acceptable body location to wear the accelerometer for continuous monitoring suggests that wrist-worn accelerometers are the best choice for early detection of the disease and also tracking the severity of it over time. In this study, we use a dataset consisting of one-week wrist-worn accelerometry data collected from individuals with Parkinson's disease and healthy elderlies for early detection of the disease. Two feature engineering methods, including epoch-based statistical feature engineering and the document-of-words method, were used. Using various machine learning classifiers, the impact of different windowing strategies, using the document-of-words method versus the statistical method, and the amount of data in terms of number of days were investigated. Based on our results, PD was detected with the highest average accuracy value (85% ± 15%) across 100 runs of SVM classifier using a set of features containing features from every and all windowing strategies. We also found that the document-of-words method significantly improves the classification performance compared to the statistical feature engineering model. Although the best performance of the classification task between PD and healthy elderlies was obtained using seven days of data collection, the results indicated that with three days of data collection, we can reach a classification performance that is not significantly different from a model built using seven days of data collection.
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Affiliation(s)
- Elham Rastegari
- Department of Business Intelligence and Analytics, Business College, Creighton University, Omaha, NE 68178, USA
| | - Hesham Ali
- Department of Biomedical Informatics, College of Information Systems and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
| | - Vivien Marmelat
- Department of Biomechanics, College of Education, Health and Human Sciences, University of Nebraska at Omaha, Omaha, NE 68182, USA
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Lai J, Cai Y, Yang L, Xia M, Cheng X, Chen Y. Effects of Baduanjin exercise on motor function, balance and gait in Parkinson's disease: a systematic review and meta-analysis. BMJ Open 2022; 12:e067280. [PMID: 36379643 PMCID: PMC9668024 DOI: 10.1136/bmjopen-2022-067280] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE This study aims to systematically evaluate the effects of Baduanjin on motor function, balance and gait in patients with Parkinson's disease (PD). DESIGN Systematic review and meta-analysis. STUDY SELECTION All eligible randomised controlled trials (RCTs) published in the English and Chinese language were included. DATA SOURCES Ten electronic databases were systematically searched, from inception to 17 March 2022: PubMed, Web of Science, Cochrane Library, Embase, EBSCOhost, OVID, SinoMed, China National Knowledge Infrastructure, Wanfang Data and China Science Journal Database (VIP). REVIEW METHODS Methodological quality assessment and meta-analysis were performed for the included studies using the Cochrane Review Manager V.5.4 software. RESULTS Ten RCTs with 804 participants were included. The results revealed the following: (1) Baduanjin significantly improved the motor function of patients with PD, based on the Unified Parkinson's Disease Rating Scale Part III (mean difference, MD -5.37, 95% CI -8.96 to -1.78, p=0.003) and Fugl-Meyer Assessment of Lower Extremity (MD 5.39, 95% CI 2.71 to 8.07, p<0.0001); (2) Baduanjin significantly improved the ability of balance of patients with PD, based on the Berg Balance Scale (MD 4.40, 95% CI 3.08 to 5.73, p<0.00001); (3) Baduanjin significantly improved the gait of patients with PD, based on the 6 min walk distance (MD 21.62, 95% CI 11.14 to 32.10, p<0.0001). After the further subgroup and sensitivity analyses, the heterogeneity was identified to be potentially due to the different degrees of disease severity in patients with PD and the difference in Baduanjin intervention durations. CONCLUSIONS The analysis of this systematic evaluation indicates that Baduanjin might have a positive effect in improving the motor function, balance and gait of patients with PD. However, due to the quantity and clinical heterogeneity limitations of the included studies, this conclusion still warrants more high-quality and multicentre RCTs for further verification.
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Affiliation(s)
- Jinghui Lai
- Rehabilitation Hospital affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
- Fujian Key Laboratory of Rehabilitation Technology, Fuzhou, Fujian, China
| | - Yangfan Cai
- Rehabilitation Hospital affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
- Fujian Key Laboratory of Rehabilitation Technology, Fuzhou, Fujian, China
| | - Liyan Yang
- Rehabilitation Hospital affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
- Fujian Key Laboratory of Rehabilitation Technology, Fuzhou, Fujian, China
| | - Min Xia
- Rehabilitation Hospital affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
- Fujian Key Laboratory of Rehabilitation Technology, Fuzhou, Fujian, China
| | - Xi Cheng
- Rehabilitation Hospital affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
- Fujian Key Laboratory of Rehabilitation Technology, Fuzhou, Fujian, China
| | - Ying Chen
- Chinese Medicine, Fujian Agriculture and Forestry University Hospital, Fuzhou, Fujian, China
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Ates HC, Nguyen PQ, Gonzalez-Macia L, Morales-Narváez E, Güder F, Collins JJ, Dincer C. End-to-end design of wearable sensors. NATURE REVIEWS. MATERIALS 2022; 7:887-907. [PMID: 35910814 PMCID: PMC9306444 DOI: 10.1038/s41578-022-00460-x] [Citation(s) in RCA: 173] [Impact Index Per Article: 86.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/15/2022] [Indexed: 05/03/2023]
Abstract
Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their capability for the early detection and monitoring of the progression and treatment of various diseases, such as COVID-19 and Parkinson disease, through biophysical signals. Next-generation wearable sensors that enable the multimodal and/or multiplexed measurement of physical parameters and biochemical markers in real time and continuously could be a transformative technology for diagnostics, allowing for high-resolution and time-resolved historical recording of the health status of an individual. In this Review, we examine the building blocks of such wearable sensors, including the substrate materials, sensing mechanisms, power modules and decision-making units, by reflecting on the recent developments in the materials, engineering and data science of these components. Finally, we synthesize current trends in the field to provide predictions for the future trajectory of wearable sensors.
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Affiliation(s)
- H. Ceren Ates
- FIT Freiburg Center for Interactive Materials and Bioinspired Technology, University of Freiburg, Freiburg, Germany
- IMTEK – Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany
| | - Peter Q. Nguyen
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA USA
| | | | - Eden Morales-Narváez
- Biophotonic Nanosensors Laboratory, Centro de Investigaciones en Óptica, León, Mexico
| | - Firat Güder
- Department of Bioengineering, Imperial College London, London, UK
| | - James J. Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA USA
- Institute of Medical Engineering & Science, Department of Biological Engineering, MIT, Cambridge, MA USA
- Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Can Dincer
- FIT Freiburg Center for Interactive Materials and Bioinspired Technology, University of Freiburg, Freiburg, Germany
- IMTEK – Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany
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Giannakopoulou KM, Roussaki I, Demestichas K. Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:1799. [PMID: 35270944 PMCID: PMC8915040 DOI: 10.3390/s22051799] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/17/2022] [Accepted: 02/21/2022] [Indexed: 12/15/2022]
Abstract
Parkinson's disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. It manifests with motor, cognitive and other types of symptoms, decreasing significantly the patients' quality of life. The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson's disease patients, their caregivers and clinicians at every stage of the disease, maximizing the treatment effectiveness and minimizing the respective healthcare costs at the same time. In this review, the considered studies propose machine learning models, trained on data acquired via smart devices, wearable or non-wearable sensors and other Internet of Things technologies, to provide predictions or estimations regarding Parkinson's disease aspects. Seven hundred and seventy studies have been retrieved from three dominant academic literature databases. Finally, one hundred and twelve of them have been selected in a systematic way and have been considered in the state-of-the-art systematic review presented in this paper. These studies propose various methods, applied on various sensory data to address different Parkinson's disease-related problems. The most widely deployed sensors, the most commonly addressed problems and the best performing algorithms are highlighted. Finally, some challenges are summarized along with some future considerations and opportunities that arise.
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Affiliation(s)
- Konstantina-Maria Giannakopoulou
- School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (K.-M.G.); (K.D.)
- Institute of Communication and Computer Systems, 10682 Athens, Greece
| | - Ioanna Roussaki
- School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (K.-M.G.); (K.D.)
- Institute of Communication and Computer Systems, 10682 Athens, Greece
| | - Konstantinos Demestichas
- School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (K.-M.G.); (K.D.)
- Institute of Communication and Computer Systems, 10682 Athens, Greece
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Ebbehoj A, Thunbo MØ, Andersen OE, Glindtvad MV, Hulman A. Transfer learning for non-image data in clinical research: A scoping review. PLOS DIGITAL HEALTH 2022; 1:e0000014. [PMID: 36812540 PMCID: PMC9931256 DOI: 10.1371/journal.pdig.0000014] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/15/2021] [Indexed: 01/14/2023]
Abstract
BACKGROUND Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. METHODS AND FINDINGS We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%). CONCLUSIONS In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.
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Affiliation(s)
- Andreas Ebbehoj
- Department of Endocrinology and Internal Medicine, Aarhus University Hospital, Denmark
- Department of Clinical Medicine, Aarhus University, Denmark
| | | | | | | | - Adam Hulman
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Denmark
- * E-mail:
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Habets JGV, Herff C, Kubben PL, Kuijf ML, Temel Y, Evers LJW, Bloem BR, Starr PA, Gilron R, Little S. Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson's Disease Using a Wrist-Worn Accelerometer. SENSORS 2021; 21:s21237876. [PMID: 34883886 PMCID: PMC8659489 DOI: 10.3390/s21237876] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/07/2023]
Abstract
Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson’s patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.
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Affiliation(s)
- Jeroen G. V. Habets
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
- Correspondence: ; Tel.: +31-433-876-052
| | - Christian Herff
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Pieter L. Kubben
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Mark L. Kuijf
- Department of Neurology, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands;
| | - Yasin Temel
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Luc J. W. Evers
- Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 GC Nijmegen, The Netherlands; (L.J.W.E.); (B.R.B.)
| | - Bastiaan R. Bloem
- Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 GC Nijmegen, The Netherlands; (L.J.W.E.); (B.R.B.)
| | - Philip A. Starr
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
| | - Ro’ee Gilron
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
| | - Simon Little
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
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