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Günaydın B, İkizoğlu S. Multifractal detrended fluctuation analysis of insole pressure sensor data to diagnose vestibular system disorders. Biomed Eng Lett 2023; 13:637-648. [PMID: 37872983 PMCID: PMC10590336 DOI: 10.1007/s13534-023-00285-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 03/20/2023] [Accepted: 05/14/2023] [Indexed: 10/25/2023] Open
Abstract
The vestibular system (VS) is a sensory system that has a vital function in human life by serving to maintain balance. In this study, multifractal detrended fluctuation analysis (MFDFA) is applied to insole pressure sensor data collected from subjects in order to extract features to identify diseases related to VS dysfunction. We use the multifractal spectrum width as the feature to distinguish between healthy and diseased people. It is observed that multifractal behavior is more dominant and thus the spectrum is wider for healthy subjects, where we explain the reason as the long-range correlations of the small and large fluctuations of the time series for this group. We directly process the instantaneous pressure values to extract features in contrast to studies in the literature where gait analysis is based on investigation of gait dynamics (stride time, stance time, etc.) requiring long walking time. Thus, as the main innovation of this work, we detrend the data to give meaningful information even for a relatively short walk. Extracted feature set was input to fundamental classification algorithms where the Support-Vector-Machine (SVM) performed best with an average accuracy of 98.2% for the binary classification as healthy or suffering. This study is a substantial part of a big project where we finally aim to identify the specific VS disease that causes balance disorder and also determine the stage of the disease, if any. Within this scope, the achieved performance gives high motivation to work more deeply on the issue.
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Affiliation(s)
- Batuhan Günaydın
- Department of Control and Automation Engineering, Faculty of Electric and Electronics, Istanbul Technical University (ITU), 34469 Maslak-Istanbul, Turkey
- Present Address: Calibration Engineer at AVL Research and Engineering TR, Abdurrahmangazi Mah., Atatürk Cad. No: 22 /11-12, 34885 Istanbul, Turkey
| | - Serhat İkizoğlu
- Department of Control and Automation Engineering, Faculty of Electric and Electronics, Istanbul Technical University (ITU), 34469 Maslak-Istanbul, Turkey
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Nair P, Shojaei Baghini M, Pendharkar G, Chung H. Detecting early-stage Parkinson's disease from gait data. Proc Inst Mech Eng H 2023; 237:1287-1296. [PMID: 37916586 DOI: 10.1177/09544119231197090] [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] [Indexed: 11/03/2023]
Abstract
Parkinson's disease is a chronic and progressive neurodegenerative disorder with an estimated 10 million people worldwide living with PD. Since early signs are benign, many patients go undiagnosed until the symptoms get severe and the treatment becomes more difficult. The symptoms start intermittently and gradually become continuous as the disease progresses. In order to detect and classify these minute differences between gaits in early PD patients, we propose to use dynamic time warping (DTW). For a given set of gait data from a patient, the DTW algorithm computes the difference between any two gait cycles in the form of a warping path, which reveals small time differences between gait cycles. Once the time-warping information between all possible pairs of gait cycles is used as the main source of gait features, K-means clustering is used to extract the final features. These final features are fed to a simple logistic regression to easily and successfully detect early PD symptoms, which was reported as challenging using conventional statistical features. In addition, the use of DTW ensures that the obtained results are not affected by the differences in the style and speed of walking of a subject. Our approach is validated for the gait data from 83 subjects at early stages of PD, 10 subjects at moderate stages of PD, and 73 controls using the Leave-One-Out and N-fold cross-validation techniques, with a detection accuracy of over 98%. The high classification accuracy validated from a large data set suggests that these new features from DTW can be effectively used to help clinicians diagnose the disease at the earliest. Even though PD is not completely curable, early diagnosis would help clinicians to start the treatment from the beginning thereby reducing the intensity of symptoms at later stages.
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Affiliation(s)
- Parvathy Nair
- IITB-Monash Research Academy, Mumbai, Maharashtra, India
- IIT Bombay, Mumbai, Maharashtra, India
- Monash University, Clayton, VIC, Australia
| | | | | | - Hoam Chung
- Monash University, Clayton, VIC, Australia
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Saljuqi M, Ghaderyan P. A novel method based on matching pursuit decomposition of gait signals for Parkinson's disease, Amyotrophic lateral sclerosis and Huntington's disease detection. Neurosci Lett 2021; 761:136107. [PMID: 34256106 DOI: 10.1016/j.neulet.2021.136107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Revised: 06/20/2021] [Accepted: 07/07/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND AND OBJECTIVE An accurate detection of neurodegenerative diseases (NDDs) definitely improves the life of patients and has attracted growing attention. METHODS In this paper, a general automatic method for detection of Parkinson's disease (PD), Amyotrophic lateral sclerosis (ALS) and Huntington's disease (HD) has been proposed based on the localized time-frequency information of gait signals. The new main part of the detection method is to obtain a small set of sparse coefficients for the local representation of gait signals with appropriate time and frequency resolution. For this purpose, a hybrid feature set based on sparse matching pursuit decomposition and two sets of nonlinear and linear features has been developed. Then, principal components of the proposed feature have been analyzed using a sparse coding classifier. Results The proposed approach has achieved high average accuracy rates of 93%, 94%, and 97% for PD, ALS, and HD detection, respectively. CONCLUSIONS The obtained results have indicated that combination of time and frequency information of the gait signals through adaptive localized window length in MP makes it more efficient in comparison with the existing time, frequency or other time-frequency gait parameters. The great potential of nonlinear sparse features for PD and HD detection and linear ones for ALS detection has also been shown.
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Affiliation(s)
- Masume Saljuqi
- Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran
| | - Peyvand Ghaderyan
- Computational Neuroscience Laboratory, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.
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Sarbaz Y, Pourakbari H. Exploring the nature of Parkinsonian rest tremor and the effects of common treatments on it: Stochastic process or chaotic behavior? Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer’s disease. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.08.009] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Khoury N, Attal F, Amirat Y, Oukhellou L, Mohammed S. Data-Driven Based Approach to Aid Parkinson's Disease Diagnosis. SENSORS (BASEL, SWITZERLAND) 2019; 19:E242. [PMID: 30634600 PMCID: PMC6359125 DOI: 10.3390/s19020242] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 01/03/2019] [Accepted: 01/04/2019] [Indexed: 11/22/2022]
Abstract
This article presents a machine learning methodology for diagnosing Parkinson's disease (PD) based on the use of vertical Ground Reaction Forces (vGRFs) data collected from the gait cycle. A classification engine assigns subjects to healthy or Parkinsonian classes. The diagnosis process involves four steps: data pre-processing, feature extraction and selection, data classification and performance evaluation. The selected features are used as inputs of each classifier. Feature selection is achieved through a wrapper approach established using the random forest algorithm. The proposed methodology uses both supervised classification methods including K-nearest neighbour (K-NN), decision tree (DT), random forest (RF), Naïve Bayes (NB), support vector machine (SVM) and unsupervised classification methods such as K-means and the Gaussian mixture model (GMM). To evaluate the effectiveness of the proposed methodology, an online dataset collected within three different studies is used. This data set includes vGRF measurements collected from eight force sensors placed under each foot of the subjects. Ninety-three patients suffering from Parkinson's disease and 72 healthy subjects participated in the experiments. The obtained performances are compared with respect to various metrics including accuracy, precision, recall and F-measure. The classification performance evaluation is performed using the leave-one-out cross validation. The results demonstrate the ability of the proposed methodology to accurately differentiate between PD subjects and healthy subjects. For the purpose of validation, the proposed methodology is also evaluated with an additional dataset including subjects with neurodegenerative diseases (Amyotrophic Lateral Sclerosis (ALS) and Huntington's disease (HD)). The obtained results show the effectiveness of the proposed methodology to discriminate PD subjects from subjects with other neurodegenerative diseases with a relatively high accuracy.
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Affiliation(s)
- Nicolas Khoury
- Laboratory of Images, Signals and Intelligent Systems (LISSI), University of Paris-Est Créteil (UPEC), 122 rue Paul Armangot, 94400 Vitry-Sur-Seine, France.
| | - Ferhat Attal
- Laboratory of Images, Signals and Intelligent Systems (LISSI), University of Paris-Est Créteil (UPEC), 122 rue Paul Armangot, 94400 Vitry-Sur-Seine, France.
| | - Yacine Amirat
- Laboratory of Images, Signals and Intelligent Systems (LISSI), University of Paris-Est Créteil (UPEC), 122 rue Paul Armangot, 94400 Vitry-Sur-Seine, France.
| | - Latifa Oukhellou
- French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR), University of Paris-Est, COSYS, GRETTIA, F-77447 Marne la Vallée, France.
| | - Samer Mohammed
- Laboratory of Images, Signals and Intelligent Systems (LISSI), University of Paris-Est Créteil (UPEC), 122 rue Paul Armangot, 94400 Vitry-Sur-Seine, France.
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Figueiredo J, Santos CP, Moreno JC. Automatic recognition of gait patterns in human motor disorders using machine learning: A review. Med Eng Phys 2018; 53:1-12. [PMID: 29373231 DOI: 10.1016/j.medengphy.2017.12.006] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 10/10/2017] [Accepted: 12/24/2017] [Indexed: 10/18/2022]
Abstract
BACKGROUND automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. PURPOSE to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. METHODS we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using "human recognition", "gait patterns'', and "feature selection methods" as relevant keywords. RESULTS analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. CONCLUSIONS automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions.
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Affiliation(s)
- Joana Figueiredo
- Center for MicroElectroMechnical Systems, University of Minho, Guimarães, Portugal.
| | - Cristina P Santos
- Center for MicroElectroMechnical Systems, University of Minho, Guimarães, Portugal.
| | - Juan C Moreno
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council, Spain.
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Zeng W, Liu F, Wang Q, Wang Y, Ma L, Zhang Y. Parkinson's disease classification using gait analysis via deterministic learning. Neurosci Lett 2016; 633:268-278. [PMID: 27693437 DOI: 10.1016/j.neulet.2016.09.043] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 09/12/2016] [Accepted: 09/25/2016] [Indexed: 11/17/2022]
Abstract
Gait analysis plays an important role in maintaining the well-being of human mobility and health care, and is a valuable tool for obtaining quantitative information on motor deficits in Parkinson's disease (PD). In this paper, we propose a method to classify (diagnose) patients with PD and healthy control subjects using gait analysis via deterministic learning theory. The classification approach consists of two phases: a training phase and a classification phase. In the training phase, gait characteristics represented by the gait dynamics are derived from the vertical ground reaction forces under the usual and self-selected paces of the subjects. The gait dynamics underlying gait patterns of healthy controls and PD patients are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. The gait patterns of healthy controls and PD patients constitute a training set. In the classification phase, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of gait dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test gait pattern of a certain PD patient to be classified (diagnosed), a set of classification errors are generated. The average L1 norms of the errors are taken as the classification measure between the dynamics of the training gait patterns and the dynamics of the test PD gait pattern according to the smallest error principle. When the gait patterns of 93 PD patients and 73 healthy controls are classified with five-fold cross-validation method, the accuracy, sensitivity and specificity of the results are 96.39%, 96.77% and 95.89%, respectively. Based on the results, it may be claimed that the features and the classifiers used in the present study could effectively separate the gait patterns between the groups of PD patients and healthy controls.
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Affiliation(s)
- Wei Zeng
- School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, PR China.
| | - Fenglin Liu
- School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, PR China
| | - Qinghui Wang
- School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, PR China
| | - Ying Wang
- School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, PR China
| | - Limin Ma
- Department of Orthopaedic Surgery, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, PR China
| | - Yu Zhang
- Department of Orthopaedic Surgery, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, PR China
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Pourhedayat A, Sarbaz Y. A Grey Box Neural Network Model of Basal Ganglia for Gait Signal of Patients with Huntington Disease. Basic Clin Neurosci 2016; 7:107-14. [PMID: 27303605 PMCID: PMC4892315 DOI: 10.15412/j.bcn.03070204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Introduction: Huntington disease (HD) is a progressive neurodegenerative disease which affects movement control system of the brain. HD symptoms lead to patient’s gait change and influence stride time intervals. In this study, we present a grey box mathematical model to simulate HD disorders. This model contains main physiological findings about BG. Methods: We used artificial neural networks (ANN) and predetermined data to model healthy state behavior, and then we trained patients with HD with this model. All blocks and relations between them were designed based on physiological findings. Results: According to the physiological findings, increasing or decreasing model connection weights are indicative of change in secretion of respective neurotransmitters. Our results show the simulating ability of the model in normal condition and different disease stages. Conclusion: Fine similarity between the presented model and BG physiological structure with its high ability in simulating HD disorders, introduces this model as a powerful tool to analyze HD behavior.
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Affiliation(s)
- Abbas Pourhedayat
- Department of Mechatronics Engineering, School of Engineering-Emerging Technologies, University of Tabriz, Tabriz, Iran
| | - Yashar Sarbaz
- Department of Mechatronics Engineering, School of Engineering-Emerging Technologies, University of Tabriz, Tabriz, Iran
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Sarbaz Y, Pourakbari H. A review of presented mathematical models in Parkinson's disease: black- and gray-box models. Med Biol Eng Comput 2015; 54:855-68. [PMID: 26546075 DOI: 10.1007/s11517-015-1401-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2014] [Accepted: 09/23/2015] [Indexed: 11/24/2022]
Abstract
Parkinson's disease (PD), one of the most common movement disorders, is caused by damage to the central nervous system. Despite all of the studies on PD, the formation mechanism of its symptoms remained unknown. It is still not obvious why damage only to the substantia nigra pars compacta, a small part of the brain, causes a wide range of symptoms. Moreover, the causes of brain damages remain to be fully elucidated. Exact understanding of the brain function seems to be impossible. On the other hand, some engineering tools are trying to understand the behavior and performance of complex systems. Modeling is one of the most important tools in this regard. Developing quantitative models for this disease has begun in recent decades. They are very effective not only in better understanding of the disease, offering new therapies, and its prediction and control, but also in its early diagnosis. Modeling studies include two main groups: black-box models and gray-box models. Generally, in the black-box modeling, regardless of the system information, the symptom is only considered as the output. Such models, besides the quantitative analysis studies, increase our knowledge of the disorders behavior and the disease symptoms. The gray-box models consider the involved structures in the symptoms appearance as well as the final disease symptoms. These models can effectively save time and be cost-effective for the researchers and help them select appropriate treatment mechanisms among all possible options. In this review paper, first, efforts are made to investigate some studies on PD quantitative analysis. Then, PD quantitative models will be reviewed. Finally, the results of using such models are presented to some extent.
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Affiliation(s)
- Yashar Sarbaz
- School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran.
| | - Hakimeh Pourakbari
- School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran
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Zeng W, Wang C. Classification of neurodegenerative diseases using gait dynamics via deterministic learning. Inf Sci (N Y) 2015. [DOI: 10.1016/j.ins.2015.04.047] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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12
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Characterizing gait asymmetry via frequency sub-band components of the ground reaction force. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2014.11.008] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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