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Beigi OM, Nóbrega LR, Houghten S, Alves Pereira A, de Oliveira Andrade A. Freezing of gait in Parkinson's disease: Classification using computational intelligence. Biosystems 2023; 232:105006. [PMID: 37634658 DOI: 10.1016/j.biosystems.2023.105006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 08/20/2023] [Accepted: 08/20/2023] [Indexed: 08/29/2023]
Abstract
Parkinson's disease (PD) is a neurodegenerative disease represented by the progressive loss of dopamine producing neurons, with motor and non-motor symptoms that may be hard to distinguish from other disorders. Affecting millions of people across the world, its symptoms include bradykinesia, tremors, depression, rigidity, postural instability, cognitive decline, and falls. Furthermore, changes in gait can be used as a primary diagnosis factor. A dataset is described that records data on healthy individuals and on PD patients, including those who experience freezing of gait, in both the ON and OFF-medication states. The dataset is comprised of data for four separate tasks: voluntary stop, timed up and go, simple motor task, and dual motor and cognitive task. Seven different classifiers are applied to two problems relating to this data. The first problem is to distinguish PD patients from healthy individuals, both overall and per task. The second problem is to determine the effectiveness of medication. A thorough analysis on the classifiers and their results is performed. Overall, multilayer perceptron and decision tree provide the most consistent results.
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Affiliation(s)
- Omid Mohamad Beigi
- Computer Science Department, Brock University, St. Catharines, Ontario, Canada
| | - Lígia Reis Nóbrega
- Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Brazil
| | - Sheridan Houghten
- Computer Science Department, Brock University, St. Catharines, Ontario, Canada.
| | - Adriano Alves Pereira
- Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Brazil
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Nóbrega LR, Cabral AM, Oliveira FHM, de Oliveira Andrade A, Krishnan S, Pereira AA. Wrist Movement Variability Assessment in Individuals with Parkinson's Disease. Healthcare (Basel) 2022; 10:healthcare10091656. [PMID: 36141268 PMCID: PMC9498573 DOI: 10.3390/healthcare10091656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/24/2022] [Accepted: 08/24/2022] [Indexed: 12/05/2022] Open
Abstract
(1) Background: Parkinson’s disease (PD) is a neurodegenerative disorder represented by the progressive loss of dopamine-producing neurons, it decreases the individual’s motor functions and affects the execution of movements. There is a real need to include quantitative techniques and reliable methods to assess the evolution of PD. (2) Methods: This cross-sectional study assessed the variability of wrist RUD (radial and ulnar deviation) and FE (flexion and extension) movements measured by two pairs of capacitive sensors (PS25454 EPIC). The hypothesis was that PD patients have less variability in wrist movement execution than healthy individuals. The data was collected from 29 participants (age: 62.13 ± 9.7) with PD and 29 healthy individuals (60.70 ± 8). Subjects performed the experimental tasks at normal and fast speeds. Six features that captured the amplitude of the hand movements around two axes were estimated from the collected signals. (3) Results: The movement variability was greater for healthy individuals than for PD patients (p < 0.05). (4) Conclusion: The low variability seen in the PD group may indicate they execute wrist RUD and FE in a more restricted way. The variability analysis proposed here could be used as an indicator of patient progress in therapeutic programs and required changes in medication dosage.
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Affiliation(s)
- Lígia Reis Nóbrega
- Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, MG, Brazil
| | - Ariana Moura Cabral
- Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, MG, Brazil
| | | | | | - Sridhar Krishnan
- Electrical and Computer Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
| | - Adriano Alves Pereira
- Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, MG, Brazil
- Correspondence: ; Tel.: +55-34-3239-4711
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Ren K, Chen Z, Ling Y, Zhao J. Recognition of freezing of gait in Parkinson's disease based on combined wearable sensors. BMC Neurol 2022; 22:229. [PMID: 35729546 PMCID: PMC9210754 DOI: 10.1186/s12883-022-02732-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 05/18/2022] [Indexed: 11/29/2022] Open
Abstract
Freezing of gait is a common gait disorder among patients with advanced Parkinson’s disease and is associated with falls. This paper designed the relevant experimental procedures to obtain FoG signals from PD patients. Accelerometers, gyroscopes, and force sensing resistor sensors were placed on the lower body of patients. On this basis, the research on the optimal feature extraction method, sensor configuration, and feature quantity selection in the FoG detection process is carried out. Thirteen typical features consisting of time domain, frequency domain and statistical features were extracted from the sensor signals. Firstly, we used the analysis of variance (ANOVA) to select features through comparing the effectiveness of two feature selection methods. Secondly, we evaluated the detection effects with different combinations of sensors to get the best sensors configuration. Finally, we selected the optimal features to construct FoG recognition model based on random forest. After comprehensive consideration of factors such as detection performance, cost, and actual deployment requirements, the 35 features obtained from the left shank gyro and accelerometer, and 78.39% sensitivity, 91.66% specificity, 88.09% accuracy, 77.58% precision and 77.98% f-score were achieved. This objective FoG recognition method has high recognition accuracy, which will be helpful for early FoG symptoms screening and treatment.
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Affiliation(s)
- Kang Ren
- System Informatics, Kobe University, Kobe, Hyogo, Japan. .,GYENNO SCIENCE CO., LTD., Shenzhen, Guangdong, China.
| | - Zhonglue Chen
- GYENNO SCIENCE CO., LTD., Shenzhen, Guangdong, China
| | - Yun Ling
- GYENNO SCIENCE CO., LTD., Shenzhen, Guangdong, China
| | - Jin Zhao
- Key Laboratory of Image Information Processing and Intelligent Control, Ministry of Education, and the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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Lewis S, Factor S, Giladi N, Nieuwboer A, Nutt J, Hallett M. Stepping up to meet the challenge of freezing of gait in Parkinson's disease. Transl Neurodegener 2022; 11:23. [PMID: 35490252 PMCID: PMC9057060 DOI: 10.1186/s40035-022-00298-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 03/31/2022] [Indexed: 11/20/2022] Open
Abstract
There has been a growing appreciation for freezing of gait as a disabling symptom that causes a significant burden in Parkinson’s disease. Previous research has highlighted some of the key components that underlie the phenomenon, but these reductionist approaches have yet to lead to a paradigm shift resulting in the development of novel treatment strategies. Addressing this issue will require greater integration of multi-modal data with complex computational modeling, but there are a number of critical aspects that need to be considered before embarking on such an approach. This paper highlights where the field needs to address current gaps and shortcomings including the standardization of definitions and measurement, phenomenology and pathophysiology, as well as considering what available data exist and how future studies should be constructed to achieve the greatest potential to better understand and treat this devastating symptom.
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Affiliation(s)
- Simon Lewis
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences, University of Sydney, Sydney, NSW, Australia.
| | - Stewart Factor
- Jean and Paul Amos Parkinson's Disease and Movement Disorders Program, Emory University School of Medicine, Atlanta, GA, USA
| | - Nir Giladi
- Movement Disorders Unit, Department of Neurology, Tel-Aviv Sourasky Medical Center, Sackler School of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Alice Nieuwboer
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - John Nutt
- Movement Disorder Section, Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
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Cui CK, Lewis SJG. Future Therapeutic Strategies for Freezing of Gait in Parkinson's Disease. Front Hum Neurosci 2021; 15:741918. [PMID: 34795568 PMCID: PMC8592896 DOI: 10.3389/fnhum.2021.741918] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 10/05/2021] [Indexed: 12/28/2022] Open
Abstract
Freezing of gait (FOG) is a common and challenging clinical symptom in Parkinson’s disease. In this review, we summarise the recent insights into freezing of gait and highlight the strategies that should be considered to improve future treatment. There is a need to develop individualised and on-demand therapies, through improved detection and wearable technologies. Whilst there already exist a number of pharmacological (e.g., dopaminergic and beyond dopamine), non-pharmacological (physiotherapy and cueing, cognitive training, and non-invasive brain stimulation) and surgical approaches to freezing (i.e., dual-site deep brain stimulation, closed-loop programming), an integrated collaborative approach to future research in this complex area will be necessary to systematically investigate new therapeutic avenues. A review of the literature suggests standardising how gait freezing is measured, enriching patient cohorts for preventative studies, and harnessing the power of existing data, could help lead to more effective treatments for freezing of gait and offer relief to many patients.
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Affiliation(s)
- Cathy K Cui
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences, The University of Sydney, Camperdown, NSW, Australia
| | - Simon J G Lewis
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences, The University of Sydney, Camperdown, NSW, Australia
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