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Hwang J, Youm C, Park H, Kim B, Choi H, Cheon SM. Machine learning for early detection and severity classification in people with Parkinson's disease. Sci Rep 2025; 15:234. [PMID: 39747207 PMCID: PMC11695740 DOI: 10.1038/s41598-024-83975-3] [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: 10/03/2024] [Accepted: 12/18/2024] [Indexed: 01/04/2025] Open
Abstract
Early detection of Parkinson's disease (PD) and accurate assessment of disease progression are critical for optimizing treatment and rehabilitation. However, there is no consensus on how to effectively detect early-stage PD and classify motor symptom severity using gait analysis. This study evaluated the accuracy of machine learning models in classifying early and moderate-stages of PD based on spatiotemporal gait features at different walking speeds. A total of 178 participants were recruited, including 103 individuals with PD (61 early-stage, 42 moderate-stage) and 75 healthy controls. Participants performed a walking test on a 24-m walkway at three speeds: preferred walking speed (PWS), 20% faster (HWS), and 20% slower (LWS). Key features-walking speed at PWS, stride length at HWS, and the coefficient of variation (CV) of the stride length at LWS-achieved a classification accuracy of 78.1% using the random forest algorithm. For early PD detection, the stride length at HWS and CV of the stride length at LWS provided an accuracy of 67.3% with Naïve Bayes. Walking at PWS was the most critical feature for distinguishing early from moderate PD, with an accuracy of 69.8%. These findings suggest that assessing gait over consecutive steps under different speed conditions may improve the early detection and severity assessment of individuals with PD.
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Affiliation(s)
- Juseon Hwang
- Department of Health Sciences, The Graduate School of Dong-A University, 37 Nakdong-Daero 550 beon-gil, Saha-gu, Busan, 49315, Republic of Korea
| | - Changhong Youm
- Department of Health Sciences, The Graduate School of Dong-A University, 37 Nakdong-Daero 550 beon-gil, Saha-gu, Busan, 49315, Republic of Korea.
| | - Hwayoung Park
- Biomechanics Laboratory, Dong-A University, Saha-gu, Busan, Republic of Korea
| | - Bohyun Kim
- Biomechanics Laboratory, Dong-A University, Saha-gu, Busan, Republic of Korea
| | - Hyejin Choi
- Department of Health Sciences, The Graduate School of Dong-A University, 37 Nakdong-Daero 550 beon-gil, Saha-gu, Busan, 49315, Republic of Korea
| | - Sang-Myung Cheon
- Department of Neurology, School of Medicine, Dong-A University, Seo-gu, Busan, Republic of Korea
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Krupička R, Malá C, Neťuková S, Hubená T, Havlík F, Bezdicek O, Dušek P, Růžička E. Impaired dual-task gait in Parkinson's disease is associated with brain morphology changes. J Neural Transm (Vienna) 2024; 131:1389-1395. [PMID: 38416199 PMCID: PMC11608385 DOI: 10.1007/s00702-024-02758-2] [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: 12/02/2023] [Accepted: 02/22/2024] [Indexed: 02/29/2024]
Abstract
In Parkinson's disease (PD), impaired gait and cognition affect daily activities, particularly in the more advanced stages of the disease. This study investigated the relationship between gait parameters, cognitive performance, and brain morphology in patients with early untreated PD. 64 drug-naive PD patients and 47 healthy controls (HC) participated in the study. Single- and dual-task gait (counting task) were examined using an expanded Timed Up & Go Test measured on a GaitRite walkway. Measurements included gait speed, stride length, and cadence. A brain morphometry analysis was performed on T1-weighted magnetic resonance (MR) images. In PD patients compared to HC, gait analysis revealed reduced speed (p < 0.001) and stride length (p < 0.001) in single-task gait, as well as greater dual-task cost (DTC) for speed (p = 0.007), stride length (p = 0.014) and cadence (p = 0.029). Based on the DTC measures in HC, PD patients were further divided into two subgroups with normal DTC (PD-nDTC) and abnormally increased DTC (PD-iDTC). For PD-nDTC, voxel-based morphometric correlation analysis revealed a positive correlation between a cluster in the left primary motor cortex and stride-length DTC (r = 0.57, p = 0.027). For PD-iDTC, a negative correlation was found between a cluster in the right lingual gyrus and the DTC for gait cadence (r=-0.35, pFWE = 0.018). No significant correlations were found in HC. The associations found between brain morphometry and gait performance with a concurrent cognitive task may represent the substrate for gait and cognitive impairment occurring since the early stages of PD.
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Affiliation(s)
- Radim Krupička
- Department of Biomedical Informatics, Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Christiane Malá
- Department of Biomedical Informatics, Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Slávka Neťuková
- Department of Biomedical Informatics, Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Tereza Hubená
- Department of Biomedical Informatics, Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Filip Havlík
- Department of Neurology, Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Ondrej Bezdicek
- Department of Neurology, Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Petr Dušek
- Department of Neurology, Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Evžen Růžička
- Department of Neurology, Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic.
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Lee H, Choi BJ, Kang N. Non-invasive brain stimulation enhances motor and cognitive performances during dual tasks in patients with Parkinson's disease: a systematic review and meta-analysis. J Neuroeng Rehabil 2024; 21:205. [PMID: 39581969 PMCID: PMC11587594 DOI: 10.1186/s12984-024-01505-8] [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: 07/11/2024] [Accepted: 11/12/2024] [Indexed: 11/26/2024] Open
Abstract
BACKGROUND Parkinson's disease (PD) induces progressive deficits in motor and cognitive functions as well as impaired dual-task performance requiring both motor and cognitive functions. This systematic review and meta-analysis evaluated the effects of non-invasive brain stimulation (NIBS) on dual-task performance in patients with PD. METHODS 11 studies met the following inclusion criteria: (a) patients with PD, (b) NIBS intervention, (c) comparison with the sham stimulation group, (d) motor and cognitive performance outcomes during dual tasks, and (e) randomized controlled trials with parallel or crossover designs. Individual effect size (i.e., comparison) was quantified by comparing motor and cognitive performances changes during dual tasks between active NIBS and sham stimulation conditions. Thus, higher values of the overall effect size indicate more improvements in either motor or cognitive performances after NIBS. Moreover, moderator variable analyses determined whether NIBS effects on dual-task performances differed depending on targeted brain regions. Finally, meta-regression analyses determined whether NIBS effects on dual-task performances were associated with demographic characteristics. RESULTS The random-effects model meta-analysis revealed that NIBS significantly improved motor (73 comparisons from 11 studies) and cognitive (12 comparisons from four studies) performances during dual tasks in patients with PD. Specifically, anodal transcranial direct current stimulation protocols on the dorsolateral prefrontal cortex were effective. Moreover, greater improvements in motor performance during dual tasks significantly correlated with decreased age and increased proportion of females, respectively. CONCLUSION This meta-analysis suggests that excitatory stimulation on the dorsolateral prefrontal cortex may be effective for improving dual-task performance in patients with PD.
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Affiliation(s)
- Hajun Lee
- Department of Human Movement Science, Incheon National University, Incheon, South Korea
| | - Beom Jin Choi
- Department of Human Movement Science, Incheon National University, Incheon, South Korea
| | - Nyeonju Kang
- Department of Human Movement Science, Incheon National University, Incheon, South Korea.
- Division of Sport Science, Sport Science Institute & Health Promotion Center, Incheon National University, Incheon, South Korea.
- Neuromechanical Rehabilitation Research Laboratory, Division of Sport Science & Sport Science Institute, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, South Korea.
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Tan X, Wang K, Sun W, Li X, Wang W, Tian F. A Review of Recent Advances in Cognitive-Motor Dual-Tasking for Parkinson's Disease Rehabilitation. SENSORS (BASEL, SWITZERLAND) 2024; 24:6353. [PMID: 39409390 PMCID: PMC11478396 DOI: 10.3390/s24196353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2024] [Revised: 08/15/2024] [Accepted: 09/06/2024] [Indexed: 10/20/2024]
Abstract
BACKGROUND Parkinson's disease is primarily characterized by the degeneration of motor neurons, leading to significant impairments in movement. Initially, physical therapy was predominantly employed to address these motor issues through targeted rehabilitation exercises. However, recent research has indicated that cognitive training can enhance the quality of life for patients with Parkinson's. Consequently, some researchers have posited that the simultaneous engagement in computer-assisted motor and cognitive dual-task (CADT) may yield superior therapeutic outcomes. METHODS A comprehensive literature search was performed across various databases, and studies were selected following PRISMA guidelines, focusing on CADT rehabilitation interventions. RESULTS Dual-task training enhances Parkinson's disease (PD) rehabilitation by automating movements and minimizing secondary task interference. The inclusion of a sensor system provides real-time feedback to help patients make immediate adjustments during training. Furthermore, CADT promotes more vigorous participation and commitment to training exercises, especially those that are repetitive and can lead to patient boredom and demotivation. Virtual reality-tailored tasks, closely mirroring everyday challenges, facilitate more efficient patient adaptation post-rehabilitation. CONCLUSIONS Although the current studies are limited by small sample sizes and low levels, CADT rehabilitation presents as a significant, effective, and potential strategy for PD.
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Affiliation(s)
- Xiaohui Tan
- Institute of Artificial Intelligence Education, Capital Normal University, Beijing 100048, China
| | - Kai Wang
- Information Engineering College, Capital Normal University, Beijing 100048, China;
| | - Wei Sun
- Institute of Software, Chinese Academy of Sciences, Beijing 100045, China; (W.S.); (X.L.); (W.W.); (F.T.)
| | - Xinjin Li
- Institute of Software, Chinese Academy of Sciences, Beijing 100045, China; (W.S.); (X.L.); (W.W.); (F.T.)
| | - Wenjie Wang
- Institute of Software, Chinese Academy of Sciences, Beijing 100045, China; (W.S.); (X.L.); (W.W.); (F.T.)
| | - Feng Tian
- Institute of Software, Chinese Academy of Sciences, Beijing 100045, China; (W.S.); (X.L.); (W.W.); (F.T.)
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Yang H, Liao Z, Zou H, Li K, Zhou Y, Gao Z, Mao Y, Song C. Machine learning-based gait adaptation dysfunction identification using CMill-based gait data. Front Neurorobot 2024; 18:1421401. [PMID: 39136036 PMCID: PMC11317473 DOI: 10.3389/fnbot.2024.1421401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 07/15/2024] [Indexed: 08/15/2024] Open
Abstract
Background Combining machine learning (ML) with gait analysis is widely applicable for diagnosing abnormal gait patterns. Objective To analyze gait adaptability characteristics in stroke patients, develop ML models to identify individuals with GAD, and select optimal diagnostic models and key classification features. Methods This study was investigated with 30 stroke patients (mean age 42.69 years, 60% male) and 50 healthy adults (mean age 41.34 years, 58% male). Gait adaptability was assessed using a CMill treadmill on gait adaptation tasks: target stepping, slalom walking, obstacle avoidance, and speed adaptation. The preliminary analysis of variables in both groups was conducted using t-tests and Pearson correlation. Features were extracted from demographics, gait kinematics, and gait adaptability datasets. ML models based on Support Vector Machine, Decision Tree, Multi-layer Perceptron, K-Nearest Neighbors, and AdaCost algorithm were trained to classify individuals with and without GAD. Model performance was evaluated using accuracy (ACC), sensitivity (SEN), F1-score and the area under the receiver operating characteristic (ROC) curve (AUC). Results The stroke group showed a significantly decreased gait speed (p = 0.000) and step length (SL) (p = 0.000), while the asymmetry of SL (p = 0.000) and ST (p = 0.000) was higher compared to the healthy group. The gait adaptation tasks significantly decreased in slalom walking (p = 0.000), obstacle avoidance (p = 0.000), and speed adaptation (p = 0.000). Gait speed (p = 0.000) and obstacle avoidance (p = 0.000) were significantly correlated with global F-A score in stroke patients. The AdaCost demonstrated better classification performance with an ACC of 0.85, SEN of 0.80, F1-score of 0.77, and ROC-AUC of 0.75. Obstacle avoidance and gait speed were identified as critical features in this model. Conclusion Stroke patients walk slower with shorter SL and more asymmetry of SL and ST. Their gait adaptability was decreased, particularly in obstacle avoidance and speed adaptation. The faster gait speed and better obstacle avoidance were correlated with better functional mobility. The AdaCost identifies individuals with GAD and facilitates clinical decision-making. This advances the future development of user-friendly interfaces and computer-aided diagnosis systems.
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Affiliation(s)
- Hang Yang
- Department of Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang, China
| | - Zhenyi Liao
- Department of Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang, China
| | - Hailei Zou
- College of Science, China Jiliang University, Zhejiang, China
| | - Kuncheng Li
- MeritData Technology Co., Ltd., Shanxi, China
| | - Ye Zhou
- Department of Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang, China
| | - Zhenzhen Gao
- Department of Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang, China
| | - Yajun Mao
- Department of Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang, China
| | - Caiping Song
- Department of Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang, China
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Zhang X, Wang M, Lee SY, Yue Y, Chen Z, Zhang Y, Wang L, Guan Q, Fan W, Shen T. Cholinergic nucleus degeneration and its association with gait impairment in Parkinson's disease. J Neuroeng Rehabil 2024; 21:120. [PMID: 39026279 PMCID: PMC11256459 DOI: 10.1186/s12984-024-01417-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 07/04/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND The contribution of cholinergic degeneration to gait disturbance in Parkinson's disease (PD) is increasingly recognized, yet its relationship with dopaminergic-resistant gait parameters has been poorly investigated. We investigated the association between comprehensive gait parameters and cholinergic nucleus degeneration in PD. METHODS This cross-sectional study enrolled 84 PD patients and 69 controls. All subjects underwent brain structural magnetic resonance imaging to assess the gray matter density (GMD) and volume (GMV) of the cholinergic nuclei (Ch123/Ch4). Gait parameters under single-task (ST) and dual-task (DT) walking tests were acquired using sensor wearables in PD group. We compared cholinergic nucleus morphology and gait performance between groups and examined their association. RESULTS PD patients exhibited significantly decreased GMD and GMV of the left Ch4 compared to controls after reaching HY stage > 2. Significant correlations were observed between multiple gait parameters and bilateral Ch123/Ch4. After multiple testing correction, the Ch123/Ch4 degeneration was significantly associated with shorter stride length, lower gait velocity, longer stance phase, smaller ankle toe-off and heel-strike angles under both ST and DT condition. For PD patients with HY stage 1-2, there were no significant degeneration of Ch123/4, and only right side Ch123/Ch4 were corrected with the gait parameters. However, as the disease progressed to HY stage > 2, bilateral Ch123/Ch4 nuclei showed correlations with gait performance, with more extensive significant correlations were observed in the right side. CONCLUSIONS Our study demonstrated the progressive association between cholinergic nuclei degeneration and gait impairment across different stages of PD, and highlighting the potential lateralization of the cholinergic nuclei's impact on gait impairment. These findings offer insights for the design and implementation of future clinical trials investigating cholinergic treatments as a promising approach to address gait impairments in PD.
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Affiliation(s)
- Xiaodan Zhang
- Department of Neurology, Ningbo NO.2 Hospital, NO.6 Building, 41 Xibei Street, Haishu District, Ningbo, Zhejiang Province, China
- Department of Emergency Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Mateng Wang
- Department of General Surgery, Yinzhou NO.2 Hospital, Ningbo, Zhejiang Province, China
| | - Shi Yeow Lee
- Department of Emergency Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yumei Yue
- Department of Neurology of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Zhaoying Chen
- Department of Neurology, Ningbo NO.2 Hospital, NO.6 Building, 41 Xibei Street, Haishu District, Ningbo, Zhejiang Province, China
| | - Yilin Zhang
- Department of Emergency Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Lulu Wang
- Department of Neurology, Ningbo NO.2 Hospital, NO.6 Building, 41 Xibei Street, Haishu District, Ningbo, Zhejiang Province, China
| | - Qiongfeng Guan
- Department of Neurology, Ningbo NO.2 Hospital, NO.6 Building, 41 Xibei Street, Haishu District, Ningbo, Zhejiang Province, China
| | - Weinv Fan
- Department of Neurology, Ningbo NO.2 Hospital, NO.6 Building, 41 Xibei Street, Haishu District, Ningbo, Zhejiang Province, China.
| | - Ting Shen
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Zhang X, Jin Y, Wang M, Ji C, Chen Z, Fan W, Rainer TH, Guan Q, Li Q. The impact of anxiety on gait impairments in Parkinson's disease: insights from sensor-based gait analysis. J Neuroeng Rehabil 2024; 21:68. [PMID: 38689288 PMCID: PMC11059709 DOI: 10.1186/s12984-024-01364-3] [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: 07/12/2023] [Accepted: 04/22/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Sensor-based gait analysis provides a robust quantitative tool for assessing gait impairments and their associated factors in Parkinson's disease (PD). Anxiety is observed to interfere with gait clinically, but this has been poorly investigated. Our purpose is to utilize gait analysis to uncover the effect of anxiety on gait in patients with PD. METHODS We enrolled 38 and 106 PD patients with and without anxiety, respectively. Gait parameters were quantitively examined and compared between two groups both in single-task (ST) and dual-task (DT) walking tests. Multiple linear regression was applied to evaluate whether anxiety independently contributed to gait impairments. RESULTS During ST, PD patients with anxiety presented significantly shorter stride length, lower gait velocity, longer stride time and stance time, longer stance phase, smaller toe-off (TO) and heel-strike (HS) angles than those without anxiety. While under DT status, the differences were diminished. Multiple linear regression analysis demonstrated that anxiety was an independent factor to a serials of gait parameters, particularly ST-TO (B = -2.599, (-4.82, -0.38)), ST-HS (B = -2.532, (-4.71, -0.35)), ST-TO-CV (B = 4.627, (1.71, 7.64)), ST-HS-CV(B = 4.597, (1.66, 7.53)), ST stance phase (B = 1.4, (0.22, 2.58)), and DT stance phase (B = 1.749, (0.56, 2.94)). CONCLUSION Our study discovered that anxiety has a significant impact on gait impairments in PD patients, especially exacerbating shuffling steps and prolonging stance phase. These findings highlight the importance of addressing anxiety in PD precision therapy to achieve better treatment outcomes.
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Affiliation(s)
- Xiaodan Zhang
- Department of Neurology, Ningbo NO.2 Hospital, Ningbo, Zhejiang Province, China
- Department of Emergency Medicine, University of Hong Kong, Hong Kong SAR, China
| | - Yulan Jin
- Department of Clinical Laboratory, Ningbo NO.2 Hospital, Ningbo, Zhejiang Province, China
| | - Mateng Wang
- Department of General Surgery, Yinzhou NO.2 Hospital, Ningbo, Zhejiang Province, China
| | - Chengcheng Ji
- School of Medicine, Shaoxing University, Shaoxing, Zhejiang Province, China
| | - Zhaoying Chen
- Department of Neurology, Ningbo NO.2 Hospital, Ningbo, Zhejiang Province, China
| | - Weinv Fan
- Department of Neurology, Ningbo NO.2 Hospital, Ningbo, Zhejiang Province, China
| | | | - Qiongfeng Guan
- Department of Neurology, Ningbo NO.2 Hospital, Ningbo, Zhejiang Province, China.
| | - Qianyun Li
- Department of Neurology, Ningbo NO.2 Hospital, Ningbo, Zhejiang Province, China.
- Department of Emergency Medicine, University of Hong Kong, Hong Kong SAR, China.
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