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Khalil RM, Shulman LM, Gruber-Baldini AL, Shakya S, Fenderson R, Van Hoven M, Hausdorff JM, von Coelln R, Cummings MP. Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2024; 24:4983. [PMID: 39124030 PMCID: PMC11314738 DOI: 10.3390/s24154983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 07/21/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024]
Abstract
Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.
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Affiliation(s)
- Rana M. Khalil
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA;
| | - Lisa M. Shulman
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Ann L. Gruber-Baldini
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (A.L.G.-B.); (S.S.)
| | - Sunita Shakya
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (A.L.G.-B.); (S.S.)
| | - Rebecca Fenderson
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Maxwell Van Hoven
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv 6492416, Israel;
- Department of Physical Therapy, Faculty of Medicine & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA
| | - Rainer von Coelln
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Michael P. Cummings
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA;
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2
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Karako K. Integration of wearable devices and deep learning: New possibilities for health management and disease prevention. Biosci Trends 2024; 18:201-205. [PMID: 38925926 DOI: 10.5582/bst.2024.01170] [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: 06/28/2024]
Abstract
In recent years, the market for wearable devices has been rapidly growing, with much of the demand for health management. These devices are equipped with numerous sensors that detect inertial measurements, electrocardiograms, photoplethysmography signals, and more. Utilizing the collected data enables the monitoring and analysis of the user's health status in real time. With the proliferation of wearable devices, research on applications such as human activity recognition, anomaly detection, and disease prediction has advanced by combining these devices with deep learning technology. Analyzing heart rate variability and activity data, for example, enables the early detection of an abnormal health status and prompt, appropriate medical interventions. Much of the current research focuses on short-term predictions, but adopting a long-term perspective is essential for further development of wearable devices and deep learning. Continuously recording user behavior, anomalies, and physical information and collecting and analyzing data over an extended period will enable more accurate disease predictions and lifestyle guidance based on individual habits and physical conditions. Achieving this requires the integration of wearable devices with medical records. A system needs to be created to integrate data collected by wearable devices with medical records such as electronic health records in collaboration with medical facilities like hospitals and clinics. Overcoming this challenge will enable optimal health management and disease prediction for each user, leading to a higher quality of life.
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Affiliation(s)
- Kenji Karako
- Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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Elbatanouny H, Kleanthous N, Dahrouj H, Alusi S, Almajali E, Mahmoud S, Hussain A. Insights into Parkinson's Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:3959. [PMID: 38931743 PMCID: PMC11207947 DOI: 10.3390/s24123959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/29/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024]
Abstract
Parkinson's Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait (FOG), which significantly impairs patients' quality of life. Despite extensive research, the precise mechanisms underlying FOG remain elusive, posing challenges for effective management and treatment. This paper presents a comprehensive meta-analysis of FOG prediction and detection methodologies, with a focus on the integration of wearable sensor technology and machine learning (ML) approaches. Through an exhaustive review of the literature, this study identifies key trends, datasets, preprocessing techniques, feature extraction methods, evaluation metrics, and comparative analyses between ML and non-ML approaches. The analysis also explores the utilization of cueing devices. The limited adoption of explainable AI (XAI) approaches in FOG prediction research represents a significant gap. Improving user acceptance and comprehension requires an understanding of the logic underlying algorithm predictions. Current FOG detection and prediction research has a number of limitations, which are identified in the discussion. These include issues with cueing devices, dataset constraints, ethical and privacy concerns, financial and accessibility restrictions, and the requirement for multidisciplinary collaboration. Future research avenues center on refining explainability, expanding and diversifying datasets, adhering to user requirements, and increasing detection and prediction accuracy. The findings contribute to advancing the understanding of FOG and offer valuable guidance for the development of more effective detection and prediction methodologies, ultimately benefiting individuals affected by PD.
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Affiliation(s)
- Hagar Elbatanouny
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
| | | | - Hayssam Dahrouj
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
| | - Sundus Alusi
- The Walton Centre NHS Foundation Trust, Liverpool L9 7LJ, UK;
| | - Eqab Almajali
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
| | - Soliman Mahmoud
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
- University of Khorfakkan, Khorfakkan, Sharjah 18119, United Arab Emirates
| | - Abir Hussain
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
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4
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Salomon A, Gazit E, Ginis P, Urazalinov B, Takoi H, Yamaguchi T, Goda S, Lander D, Lacombe J, Sinha AK, Nieuwboer A, Kirsch LC, Holbrook R, Manor B, Hausdorff JM. A machine learning contest enhances automated freezing of gait detection and reveals time-of-day effects. Nat Commun 2024; 15:4853. [PMID: 38844449 PMCID: PMC11156937 DOI: 10.1038/s41467-024-49027-0] [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: 12/13/2023] [Accepted: 05/22/2024] [Indexed: 06/09/2024] Open
Abstract
Freezing of gait (FOG) is a debilitating problem that markedly impairs the mobility and independence of 38-65% of people with Parkinson's disease. During a FOG episode, patients report that their feet are suddenly and inexplicably "glued" to the floor. The lack of a widely applicable, objective FOG detection method obstructs research and treatment. To address this problem, we organized a 3-month machine-learning contest, inviting experts from around the world to develop wearable sensor-based FOG detection algorithms. 1,379 teams from 83 countries submitted 24,862 solutions. The winning solutions demonstrated high accuracy, high specificity, and good precision in FOG detection, with strong correlations to gold-standard references. When applied to continuous 24/7 data, the solutions revealed previously unobserved patterns in daily living FOG occurrences. This successful endeavor underscores the potential of machine learning contests to rapidly engage AI experts in addressing critical medical challenges and provides a promising means for objective FOG quantification.
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Affiliation(s)
- Amit Salomon
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Pieter Ginis
- KU Leuven, Department of Rehabilitation Science, Neuromotor Rehabilitation Research Group (eNRGy), Leuven, Belgium
| | | | | | | | | | | | | | | | - Alice Nieuwboer
- KU Leuven, Department of Rehabilitation Science, Neuromotor Rehabilitation Research Group (eNRGy), Leuven, Belgium
| | - Leslie C Kirsch
- Michael J. Fox Foundation for Parkinson's Research, New York, NY, USA
| | | | - Brad Manor
- Hinda and Arthur Marcus Institute for Aging Research at Hebrew SeniorLife, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, MA, Boston, USA
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
- Department of Physical Therapy, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel.
- Department of Orthopedic Surgery and Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.
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Zhang W, Sun H, Huang D, Zhang Z, Li J, Wu C, Sun Y, Gong M, Wang Z, Sun C, Cui G, Guo Y, Chan P. Detection and prediction of freezing of gait with wearable sensors in Parkinson's disease. Neurol Sci 2024; 45:431-453. [PMID: 37843692 DOI: 10.1007/s10072-023-07017-y] [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: 02/11/2023] [Accepted: 08/06/2023] [Indexed: 10/17/2023]
Abstract
Freezing of gait (FoG) is one of the most distressing symptoms of Parkinson's Disease (PD), commonly occurring in patients at middle and late stages of the disease. Automatic and accurate FoG detection and prediction have emerged as a promising tool for long-term monitoring of PD and implementation of gait assistance systems. This paper reviews the recent development of FoG detection and prediction using wearable sensors, with attention on identifying knowledge gaps that need to be filled in future research. This review searched the PubMed and Web of Science databases to collect studies that detect or predict FoG with wearable sensors. After screening, 89 of 270 articles were included. The data description, extracted features, detection/prediction methods, and classification performance were extracted from the articles. As the number of papers of this area is increasing, the performance has been steadily improved. However, small datasets and inconsistent evaluation processes still hinder the application of FoG detection and prediction with wearable sensors in clinical practice.
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Affiliation(s)
- Wei Zhang
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, Neurobiology and Geriatrics, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
- Jiangsu Key Laboratory of Brain Disease Bioinformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Hong Sun
- Department of Neurology, Neurobiology and Geriatrics, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
- Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, 100053, China
- National Clinical Research Center of Geriatric Disorders, Key Laboratory for Neurodegenerative Disease of the Ministry of Education, Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing, 100053, China
| | - Debin Huang
- Department of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
| | - Zixuan Zhang
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Jinyu Li
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Chan Wu
- Dongzhimen Hospital, Beijing University of Traditional Chinese Medicine, Beijing, 100029, China
| | - Yingying Sun
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Mengyi Gong
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Zhi Wang
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Chao Sun
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Guiyun Cui
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China.
| | - Yuzhu Guo
- Department of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China.
| | - Piu Chan
- Department of Neurology, Neurobiology and Geriatrics, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
- Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, 100053, China.
- National Clinical Research Center of Geriatric Disorders, Key Laboratory for Neurodegenerative Disease of the Ministry of Education, Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing, 100053, China.
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6
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Habib Z, Mughal MA, Khan MA, Shabaz M. WiFOG: Integrating deep learning and hybrid feature selection for accurate freezing of gait detection. ALEXANDRIA ENGINEERING JOURNAL 2024; 86:481-493. [DOI: 10.1016/j.aej.2023.11.075] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
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7
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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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8
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Li R, Agu E, Sarwar A, Grimone K, Herman D, Abrantes AM, Stein MD. Fine-Grained Intoxicated Gait Classification Using a Bilinear CNN. IEEE SENSORS JOURNAL 2023; 23:29733-29748. [PMID: 38186565 PMCID: PMC10769125 DOI: 10.1109/jsen.2023.3248868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Consuming excessive amounts of alcohol causes impaired mobility and judgment and driving accidents, resulting in more than 800 injuries and fatalities each day. Passive methods to detect intoxicated drivers beyond the safe driving limit can facilitate Just-In-Time alerts and reduce Driving Under the Influence (DUI) incidents. Popularly-owned smartphones are not only equipped with motion sensors (accelerometer and gyroscope) that can be employed for passively collecting gait (walk) data but also have the processing power to run computationally expensive machine learning models. In this paper, we advance the state-of-the-art by proposing a novel method that utilizes a Bi-linear Convolution Neural Network (BiCNN) for analyzing smartphone accelerometer and gyroscope data to determine whether a smartphone user is over the legal driving limit (0.08) from their gait. After segmenting the gait data into steps, we converted the smartphone motion sensor data to a Gramian Angular Field (GAF) image and then leveraged the BiCNN architecture for intoxication classification. Distinguishing GAF-encoded images of the gait of intoxicated vs. sober users is challenging as the differences between the classes (intoxicated vs. sober) are subtle, also known as a fine-grained image classification problem. The BiCNN neural network has previously produced state-of-the-art results on fine-grained image classification of natural images. To the best of our knowledge, our work is the first to innovatively utilize the BiCNN to classify GAF encoded images of smartphone gait data in order to detect intoxication. Prior work had explored using the BiCNN to classify natural images or explored other gait-related tasks but not intoxication Our complete intoxication classification pipeline consists of several important pre-processing steps carefully adapted to the BAC classification task, including step detection and segmentation, data normalization to account for inter-subject variability, data fusion, GAF image generation from time-series data, and a BiCNN classification model. In rigorous evaluation, our BiCNN model achieves an accuracy of 83.5%, outperforming the previous state-of-the-art and demonstrating the feasibility of our approach.
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Affiliation(s)
- Ruojun Li
- Department of Optical Information, Huazhong University of Science and Technology, Wuhan, China
- Department of Electrical and Computer Engineering, Worcester Polytechnic Institute(WPI), Worcester, MA, USA
| | - Emmanuel Agu
- Computer Science Department, Worcester Polytechnic Institute, Worcester, MA, USA
| | | | | | - Debra Herman
- Department of Psychiatry and Human Behavior and a Research Psychologist in the Behavioral Medicine and Addictions Research group at Butler Hospital
| | - Ana M Abrantes
- Behavioral Medicine and Addictions Research at Butler Hospital and a Professor in the Department of Psychiatry and Human Behavior at the Alpert Medical School of Brown University
| | - Michael D Stein
- Chair of Health Law, Policy & Management at Boston University
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9
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Romijnders R, Salis F, Hansen C, Küderle A, Paraschiv-Ionescu A, Cereatti A, Alcock L, Aminian K, Becker C, Bertuletti S, Bonci T, Brown P, Buckley E, Cantu A, Carsin AE, Caruso M, Caulfield B, Chiari L, D'Ascanio I, Del Din S, Eskofier B, Fernstad SJ, Fröhlich MS, Garcia Aymerich J, Gazit E, Hausdorff JM, Hiden H, Hume E, Keogh A, Kirk C, Kluge F, Koch S, Mazzà C, Megaritis D, Micó-Amigo E, Müller A, Palmerini L, Rochester L, Schwickert L, Scott K, Sharrack B, Singleton D, Soltani A, Ullrich M, Vereijken B, Vogiatzis I, Yarnall A, Schmidt G, Maetzler W. Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases. Front Neurol 2023; 14:1247532. [PMID: 37909030 PMCID: PMC10615212 DOI: 10.3389/fneur.2023.1247532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/18/2023] [Indexed: 11/02/2023] Open
Abstract
Introduction The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings. Methods Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data. Results and discussion The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of -0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, -0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.
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Affiliation(s)
- Robbin Romijnders
- Digital Signal Processing and System Theory, Electrical and Information Engineering, Faculty of Engineering, Kiel University, Kiel, Germany
- Arbeitsgruppe Neurogeriatrie, Department of Neurology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Clint Hansen
- Arbeitsgruppe Neurogeriatrie, Department of Neurology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Arne Küderle
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Polytechnic of Turin, Turin, Italy
| | - Lisa Alcock
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Clemens Becker
- Gesellschaft für Medizinische Forschung, Robert-Bosch Foundation GmbH, Stuttgart, Germany
| | - Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Tecla Bonci
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Philip Brown
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Ellen Buckley
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Alma Cantu
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Anne-Elie Carsin
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Marco Caruso
- Department of Electronics and Telecommunications, Polytechnic of Turin, Turin, Italy
| | - Brian Caulfield
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Lorenzo Chiari
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
- Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRISDV), University of Bologna, Bologna, Italy
| | - Ilaria D'Ascanio
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
| | - Silvia Del Din
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Björn Eskofier
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | | | | | - Judith Garcia Aymerich
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Hugo Hiden
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Emily Hume
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Cameron Kirk
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Novartis Institute of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Sarah Koch
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Claudia Mazzà
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Dimitrios Megaritis
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Encarna Micó-Amigo
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Arne Müller
- Novartis Institute of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
- Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRISDV), University of Bologna, Bologna, Italy
| | - Lynn Rochester
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Lars Schwickert
- Gesellschaft für Medizinische Forschung, Robert-Bosch Foundation GmbH, Stuttgart, Germany
| | - Kirsty Scott
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - David Singleton
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Abolfazl Soltani
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Digital Health Department, CSEM SA, Neuchâtel, Switzerland
| | - Martin Ullrich
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Alison Yarnall
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Gerhard Schmidt
- Digital Signal Processing and System Theory, Electrical and Information Engineering, Faculty of Engineering, Kiel University, Kiel, Germany
| | - Walter Maetzler
- Arbeitsgruppe Neurogeriatrie, Department of Neurology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
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10
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Uchitomi H, Ming X, Zhao C, Ogata T, Miyake Y. Classification of mild Parkinson's disease: data augmentation of time-series gait data obtained via inertial measurement units. Sci Rep 2023; 13:12638. [PMID: 37537260 PMCID: PMC10400620 DOI: 10.1038/s41598-023-39862-4] [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: 01/04/2023] [Accepted: 08/01/2023] [Indexed: 08/05/2023] Open
Abstract
Data-augmentation methods have emerged as a viable approach for improving the state-of-the-art performances for classifying mild Parkinson's disease using deep learning with time-series data from an inertial measurement unit, considering the limited amount of training datasets available in the medical field. This study investigated effective data-augmentation methods to classify mild Parkinson's disease and healthy participants with deep learning using a time-series gait dataset recorded via a shank-worn inertial measurement unit. Four magnitude-domain-transformation and three time-domain-transformation data-augmentation methods, and four methods involving mixtures of the aforementioned methods were applied to a representative convolutional neural network for the classification, and their performances were compared. In terms of data-augmentation, compared with baseline classification accuracy without data-augmentation, the magnitude-domain transformation performed better than the time-domain transformation and mixed-data augmentation. In the magnitude-domain transformation, the rotation method significantly contributed to the best performance improvement, yielding accuracy and F1-score improvements of 5.5 and 5.9%, respectively. The augmented data could be varied while maintaining the features of the time-series data obtained via the sensor for detecting mild Parkinson's in gait; this data attribute may have caused the aforementioned trend. Notably, the selection of appropriate data extensions will help improve the classification performance for mild Parkinson's disease.
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Affiliation(s)
- Hirotaka Uchitomi
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, Yokohama, 226-8502, Japan.
| | - Xianwen Ming
- Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Yokohama, 226-8502, Japan
| | - Changyu Zhao
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, Yokohama, 226-8502, Japan
| | - Taiki Ogata
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, Yokohama, 226-8502, Japan
| | - Yoshihiro Miyake
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, Yokohama, 226-8502, Japan
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11
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Wu P, Cao B, Liang Z, Wu M. The advantages of artificial intelligence-based gait assessment in detecting, predicting, and managing Parkinson's disease. Front Aging Neurosci 2023; 15:1191378. [PMID: 37502426 PMCID: PMC10368956 DOI: 10.3389/fnagi.2023.1191378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 06/05/2023] [Indexed: 07/29/2023] Open
Abstract
Background Parkinson's disease is a neurological disorder that can cause gait disturbance, leading to mobility issues and falls. Early diagnosis and prediction of freeze episodes are essential for mitigating symptoms and monitoring the disease. Objective This review aims to evaluate the use of artificial intelligence (AI)-based gait evaluation in diagnosing and managing Parkinson's disease, and to explore the potential benefits of this technology for clinical decision-making and treatment support. Methods A thorough review of published literature was conducted to identify studies, articles, and research related to AI-based gait evaluation in Parkinson's disease. Results AI-based gait evaluation has shown promise in preventing freeze episodes, improving diagnosis, and increasing motor independence in patients with Parkinson's disease. Its advantages include higher diagnostic accuracy, continuous monitoring, and personalized therapeutic interventions. Conclusion AI-based gait evaluation systems hold great promise for managing Parkinson's disease and improving patient outcomes. They offer the potential to transform clinical decision-making and inform personalized therapies, but further research is needed to determine their effectiveness and refine their use.
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Affiliation(s)
- Peng Wu
- College of Acupuncture and Orthopedics, Hubei University of Chinese Medicine, Wuhan, Hubei, China
| | - Biwei Cao
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
- Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, Hubei, China
- Hubei Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
| | - Zhendong Liang
- College of Acupuncture and Orthopedics, Hubei University of Chinese Medicine, Wuhan, Hubei, China
| | - Miao Wu
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
- Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, Hubei, China
- Hubei Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
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12
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Zhang J, Lim J, Kim MH, Hur S, Chung TM. WM-STGCN: A Novel Spatiotemporal Modeling Method for Parkinsonian Gait Recognition. SENSORS (BASEL, SWITZERLAND) 2023; 23:4980. [PMID: 37430892 DOI: 10.3390/s23104980] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/12/2023] [Accepted: 05/19/2023] [Indexed: 07/12/2023]
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder that causes gait abnormalities. Early and accurate recognition of PD gait is crucial for effective treatment. Recently, deep learning techniques have shown promising results in PD gait analysis. However, most existing methods focus on severity estimation and frozen gait detection, while the recognition of Parkinsonian gait and normal gait from the forward video has not been reported. In this paper, we propose a novel spatiotemporal modeling method for PD gait recognition, named WM-STGCN, which utilizes a Weighted adjacency matrix with virtual connection and Multi-scale temporal convolution in a Spatiotemporal Graph Convolution Network. The weighted matrix enables different intensities to be assigned to different spatial features, including virtual connections, while the multi-scale temporal convolution helps to effectively capture the temporal features at different scales. Moreover, we employ various approaches to augment skeleton data. Experimental results show that our proposed method achieved the best accuracy of 87.1% and an F1 score of 92.85%, outperforming Long short-term memory (LSTM), K-nearest neighbors (KNN), Decision tree, AdaBoost, and ST-GCN models. Our proposed WM-STGCN provides an effective spatiotemporal modeling method for PD gait recognition that outperforms existing methods. It has the potential for clinical application in PD diagnosis and treatment.
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Affiliation(s)
- Jieming Zhang
- Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jongmin Lim
- Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | | | - Sungwook Hur
- Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Tai-Myoung Chung
- Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
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13
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Borzì L, Sigcha L, Olmo G. Context Recognition Algorithms for Energy-Efficient Freezing-of-Gait Detection in Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094426. [PMID: 37177629 PMCID: PMC10181532 DOI: 10.3390/s23094426] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023]
Abstract
Freezing of gait (FoG) is a disabling clinical phenomenon of Parkinson's disease (PD) characterized by the inability to move the feet forward despite the intention to walk. It is one of the most troublesome symptoms of PD, leading to an increased risk of falls and reduced quality of life. The combination of wearable inertial sensors and machine learning (ML) algorithms represents a feasible solution to monitor FoG in real-world scenarios. However, traditional FoG detection algorithms process all data indiscriminately without considering the context of the activity during which FoG occurs. This study aimed to develop a lightweight, context-aware algorithm that can activate FoG detection systems only under certain circumstances, thus reducing the computational burden. Several approaches were implemented, including ML and deep learning (DL) gait recognition methods, as well as a single-threshold method based on acceleration magnitude. To train and evaluate the context algorithms, data from a single inertial sensor were extracted using three different datasets encompassing a total of eighty-one PD patients. Sensitivity and specificity for gait recognition ranged from 0.95 to 0.96 and 0.80 to 0.93, respectively, with the one-dimensional convolutional neural network providing the best results. The threshold approach performed better than ML- and DL-based methods when evaluating the effect of context awareness on FoG detection performance. Overall, context algorithms allow for discarding more than 55% of non-FoG data and less than 4% of FoG episodes. The results indicate that a context classifier can reduce the computational burden of FoG detection algorithms without significantly affecting the FoG detection rate. Thus, implementation of context awareness can present an energy-efficient solution for long-term FoG monitoring in ambulatory and free-living settings.
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Affiliation(s)
- Luigi Borzì
- Data Analytics and Technologies for Health Lab (ANTHEA), Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
| | - Luis Sigcha
- Data-Driven Computer Engineering (D2iCE) Group, Department of Electronic and Computer Engineering, University of Limerick, V94 T9PX Limerick, Ireland
| | - Gabriella Olmo
- Data Analytics and Technologies for Health Lab (ANTHEA), Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
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14
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Yang L, Wang Y, Zhu X, Yang X, Zheng C. A gated temporal-separable attention network for EEG-based depression recognition. Comput Biol Med 2023; 157:106782. [PMID: 36931203 DOI: 10.1016/j.compbiomed.2023.106782] [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: 08/31/2022] [Revised: 02/14/2023] [Accepted: 03/09/2023] [Indexed: 03/13/2023]
Abstract
Depression, a common mental illness worldwide, needs to be diagnosed and cured at an early stage. To assist clinical diagnosis, an EEG-based deep learning frame, which is named the gated temporal-separable attention network (GTSAN), is proposed in this paper for depression recognition. GTSAN model extracts discriminative information from EEG recordings in two ways. On the one hand, the gated recurrent unit (GRU) is used in the GTSAN model to capture the EEG historical information to form the features. On the other hand, the model digs the multilevel information by using an improved version of temporal convolutional network (TCN), called temporal-separable convolution network (TSCN), which applies causal convolution and dilated convolution to extract features from fine to coarse scales. The TSCN and GRU features can be produced in parallel. Finally, the new model introduces the attention mechanism to give different weights to these features, allowing them to be used to identify depression more effectively. Experiments on two depression datasets have demonstrated that the proposed model can mine potential depression patterns in data and obtain high recognition accuracies. The proposed model provides the possibility of using an EEG-based system to assist for diagnosing depression.
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Affiliation(s)
- Lijun Yang
- School of Mathematics and Statistics, Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Henan University, Kaifeng 475004, China; Center for Applied Mathematics of Henan Province, Henan University, Zhengzhou, 450046, China.
| | - Yixin Wang
- School of Mathematics and Statistics, Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Henan University, Kaifeng 475004, China.
| | - Xiangru Zhu
- Institute of Cognition, Brain, and Health, Henan University, Kaifeng 475004, China.
| | - Xiaohui Yang
- School of Mathematics and Statistics, Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Henan University, Kaifeng 475004, China; Center for Applied Mathematics of Henan Province, Henan University, Zhengzhou, 450046, China.
| | - Chen Zheng
- School of Mathematics and Statistics, Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Henan University, Kaifeng 475004, China; Center for Applied Mathematics of Henan Province, Henan University, Zhengzhou, 450046, China.
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15
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Kwon H, Clifford GD, Genias I, Bernhard D, Esper CD, Factor SA, McKay JL. An Explainable Spatial-Temporal Graphical Convolutional Network to Score Freezing of Gait in Parkinsonian Patients. SENSORS (BASEL, SWITZERLAND) 2023; 23:1766. [PMID: 36850363 PMCID: PMC9968199 DOI: 10.3390/s23041766] [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: 01/13/2023] [Revised: 01/27/2023] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
Freezing of gait (FOG) is a poorly understood heterogeneous gait disorder seen in patients with parkinsonism which contributes to significant morbidity and social isolation. FOG is currently measured with scales that are typically performed by movement disorders specialists (i.e., MDS-UPDRS), or through patient completed questionnaires (N-FOG-Q) both of which are inadequate in addressing the heterogeneous nature of the disorder and are unsuitable for use in clinical trials The purpose of this study was to devise a method to measure FOG objectively, hence improving our ability to identify it and accurately evaluate new therapies. A major innovation of our study is that it is the first study of its kind that uses the largest sample size (>30 h, N = 57) in order to apply explainable, multi-task deep learning models for quantifying FOG over the course of the medication cycle and at varying levels of parkinsonism severity. We trained interpretable deep learning models with multi-task learning to simultaneously score FOG (cross-validated F1 score 97.6%), identify medication state (OFF vs. ON levodopa; cross-validated F1 score 96.8%), and measure total PD severity (MDS-UPDRS-III score prediction error ≤ 2.7 points) using kinematic data of a well-characterized sample of N = 57 patients during levodopa challenge tests. The proposed model was able to explain how kinematic movements are associated with each FOG severity level that were highly consistent with the features, in which movement disorders specialists are trained to identify as characteristics of freezing. Overall, we demonstrate that deep learning models' capability to capture complex movement patterns in kinematic data can automatically and objectively score FOG with high accuracy. These models have the potential to discover novel kinematic biomarkers for FOG that can be used for hypothesis generation and potentially as clinical trial outcome measures.
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Affiliation(s)
- Hyeokhyen Kwon
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Imari Genias
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Doug Bernhard
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Christine D. Esper
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Stewart A. Factor
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - J. Lucas McKay
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA
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16
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Early diagnosis of Parkinson's disease: A combined method using deep learning and neuro-fuzzy techniques. Comput Biol Chem 2023; 102:107788. [PMID: 36410240 DOI: 10.1016/j.compbiolchem.2022.107788] [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: 06/03/2022] [Revised: 10/28/2022] [Accepted: 11/08/2022] [Indexed: 11/11/2022]
Abstract
Predicting Unified Parkinson's Disease Rating Scale (UPDRS) in Total- UPDRS and Motor-UPDRS clinical scales is an important part of controlling PD. Computational intelligence approaches have been used effectively in the early diagnosis of PD by predicting UPDRS. In this research, we target to present a combined approach for PD diagnosis using an ensemble learning approach with the ability of online learning from clinical large datasets. The method is developed using Deep Belief Network (DBN) and Neuro-Fuzzy approaches. A clustering approach, Expectation-Maximization (EM), is used to handle large datasets. The Principle Component Analysis (PCA) technique is employed for noise removal from the data. The UPDRS prediction models are constructed for PD diagnosis. To handle the missing data, K-NN is used in the proposed method. We use incremental machine learning approaches to improve the efficiency of the proposed method. We assess our approach on a real-world PD dataset and the findings are assessed compared to other PD diagnosis approaches developed by machine learning techniques. The findings revealed that the approach can improve the UPDRS prediction accuracy and the time complexity of previous methods in handling large datasets.
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17
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Tripathi S, Arroyo-Gallego T, Giancardo L. Keystroke-Dynamics for Parkinson's Disease Signs Detection in an At-Home Uncontrolled Population: A New Benchmark and Method. IEEE Trans Biomed Eng 2023; 70:182-192. [PMID: 35767495 PMCID: PMC9904385 DOI: 10.1109/tbme.2022.3187309] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Parkinson's disease (PD) is the second most prevalent neurodegenerative disease disorder in the world. A prompt diagnosis would enable clinical trials for disease-modifying neuroprotective therapies. Recent research efforts have unveiled imaging and blood markers that have the potential to be used to identify PD patients promptly, however, the idiopathic nature of PD makes these tests very hard to scale to the general population. To this end, we need an easily deployable tool that would enable screening for PD signs in the general population. In this work, we propose a new set of features based on keystroke dynamics, i.e., the time required to press and release keyboard keys during typing, and used to detect PD in an ecologically valid data acquisition setup at the subject's homes, without requiring any pre-defined task. We compare and contrast existing models presented in the literature and present a new model that combines a new type of keystroke dynamics signal representation using hold time and flight time series as a function of key types and asymmetry in the time series using a convolutional neural network. We show how this model achieves an Area Under the Receiving Operating Characteristic curve ranging from 0.80 to 0.83 on a dataset of subjects who actively interacted with their computers for at least 5 months and positively compares against other state-of-the-art approaches previously tested on keystroke dynamics data acquired with mechanical keyboards.
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18
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Bansal SK, Basumatary B, Bansal R, Sahani AK. Techniques for the detection and management of freezing of gait in Parkinson's disease - A systematic review and future perspectives. MethodsX 2023; 10:102106. [PMID: 36942282 PMCID: PMC10023964 DOI: 10.1016/j.mex.2023.102106] [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: 12/05/2022] [Accepted: 03/01/2023] [Indexed: 03/07/2023] Open
Abstract
Freezing of Gait (FoG) is one of the most critical debilitating motor symptoms of advanced Parkinson's disease (PD) with a higher rate of occurrence in aged people. PD affects the cardinal motor functioning and leads to non-motor symptoms, including cognitive and neurobehavioral abnormalities, autonomic dysfunctions and sleep disorders. Since its pathogenesis is complex and unclear yet, this paper targets the studies done on the pathophysiology and epidemiology of FoG in PD. Gait disorder and cardinal features vary from festination (involuntary hurrying in walking) to freezing of gait (breakdown of repetitive movement of steps despite the intention to walk) in patients. Hence, it is difficult to assess the FoG in clinical trials. Therefore, the current research emphasizes wearable sensor-based systems over pharmacology and surgical methods.•This paper presents a technological review of various techniques used for the assessment of FoG with a comprehensive comparison.•Researchers are aiming at the development of wireless sensor-based assistive devices to (a) predict the FoG episode in a different environment, (b) acquire the long-term data for real-time analysis, and (c) cue the FoG patients.•We summarize the work done till now and future research directions needed for a suitable cueing mechanism to overcome FoG.
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Affiliation(s)
- Sunil Kumar Bansal
- Department of Electrical Engineering, Indian Institute of Technology Ropar, Rupnagar, India
- Department of Electrical and Instrumentation Engineering, SLIET Longowal, Sangrur, India
| | - Bijit Basumatary
- Department of Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar, India
- Corresponding author.
| | - Rajinder Bansal
- Department of Neurology, Dayanand Medical College and Hospital, Ludhiana, India
| | - Ashish Kumar Sahani
- Department of Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar, India
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Borzì L, Sigcha L, Rodríguez-Martín D, Olmo G. Real-time detection of freezing of gait in Parkinson's disease using multi-head convolutional neural networks and a single inertial sensor. Artif Intell Med 2023; 135:102459. [PMID: 36628783 DOI: 10.1016/j.artmed.2022.102459] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Freezing of gait (FOG) is one of the most disabling symptoms of Parkinson's disease (PD), contributing to poor quality of life and increased risk of falls. Wearable sensors represent a valuable means for detecting FOG in the home environment. Moreover, real-time feedback has proven to help reduce the duration of FOG episodes. This work proposes a robust real-time FOG detection algorithm, which is easy to implement in stand-alone devices working in non-supervised conditions. METHOD Data from three different data sets were used in this study, with two employed as independent test sets. Acceleration recordings from 118 PD patients and 21 healthy elderly subjects were collected while they performed simulated daily living activities. A single inertial sensor was attached to the waist of each subject. More than 17 h of valid data and a total number of 1110 FOG episodes were analyzed in this study. The implemented algorithm consisted of a multi-head convolutional neural network, which exploited different spatial resolutions in the analysis of inertial data. The architecture and the model parameters were designed to provide optimal performance while reducing computational complexity and testing time. RESULTS The developed algorithm demonstrated good to excellent classification performance, with more than 50% (30%) of FOG episodes predicted on average 3.1 s (1.3 s) before the actual onset in the main (independent) data set. Around 50% of FOG was detected with an average delay of 0.8 s (1.1 s) in the main (independent) data set. Moreover, a specificity above 88% (93%) was obtained when testing the algorithm on the main (independent) test set, while 100% specificity was obtained on healthy elderly subjects. CONCLUSION The algorithm proved robust, with low computational complexity and processing time, thus paving the way to a real-time implementation in a stand-alone device that can be used in non-supervised environments.
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Affiliation(s)
- Luigi Borzì
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.
| | - Luis Sigcha
- Instrumentation and Applied Acoustics Research Group (I2A2), Universidad Politecnica de Madrid, Ctra. Valencia, Km 7, 28031 Madrid, Spain; ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal.
| | - Daniel Rodríguez-Martín
- Sense4Care S.L., Cornellà de Llobregat, 08940 Barcelona, Spain; Technical Research Centre for Dependency Care and Autonomous Living (CETPD), Universitat Politecnica de Catalunya, 08800 Vilanova i la Geltrù, Spain.
| | - Gabriella Olmo
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.
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20
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Huang T, Li M, Huang J. Recent trends in wearable device used to detect freezing of gait and falls in people with Parkinson's disease: A systematic review. Front Aging Neurosci 2023; 15:1119956. [PMID: 36875701 PMCID: PMC9975590 DOI: 10.3389/fnagi.2023.1119956] [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: 12/09/2022] [Accepted: 01/23/2023] [Indexed: 02/17/2023] Open
Abstract
Background The occurrence of freezing of gait (FOG) is often observed in moderate to last-stage Parkinson's disease (PD), leading to a high risk of falls. The emergence of the wearable device has offered the possibility of FOG detection and falls of patients with PD allowing high validation in a low-cost way. Objective This systematic review seeks to provide a comprehensive overview of existing literature to establish the forefront of sensors type, placement and algorithm to detect FOG and falls among patients with PD. Methods Two electronic databases were screened by title and abstract to summarize the state of art on FOG and fall detection with any wearable technology among patients with PD. To be eligible for inclusion, papers were required to be full-text articles published in English, and the last search was completed on September 26, 2022. Studies were excluded if they; (i) only examined cueing function for FOG, (ii) only used non-wearable devices to detect or predict FOG or falls, and (iii) did not provide sufficient details about the study design and results. A total of 1,748 articles were retrieved from two databases. However, only 75 articles were deemed to meet the inclusion criteria according to the title, abstract and full-text reviewed. Variable was extracted from chosen research, including authorship, details of the experimental object, type of sensor, device location, activities, year of publication, evaluation in real-time, the algorithm and detection performance. Results A total of 72 on FOG detection and 3 on fall detection were selected for data extraction. There were wide varieties of the studied population (from 1 to 131), type of sensor, placement and algorithm. The thigh and ankle were the most popular device location, and the combination of accelerometer and gyroscope was the most frequently used inertial measurement unit (IMU). Furthermore, 41.3% of the studies used the dataset as a resource to examine the validity of their algorithm. The results also showed that increasingly complex machine-learning algorithms had become the trend in FOG and fall detection. Conclusion These data support the application of the wearable device to access FOG and falls among patients with PD and controls. Machine learning algorithms and multiple types of sensors have become the recent trend in this field. Future work should consider an adequate sample size, and the experiment should be performed in a free-living environment. Moreover, a consensus on provoking FOG/fall, methods of assessing validity and algorithm are necessary.Systematic Review Registration: PROSPERO, identifier CRD42022370911.
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Affiliation(s)
- Tinghuai Huang
- Laboratory of Laser Sports Medicine, South China Normal University, Guangzhou, Guangdong, China
| | - Meng Li
- Laboratory of Laser Sports Medicine, South China Normal University, Guangzhou, Guangdong, China
| | - Jianwei Huang
- Department of Gastroenterology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China
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Zhang W, Yang Z, Li H, Huang D, Wang L, Wei Y, Zhang L, Ma L, Feng H, Pan J, Guo Y, Chan P. Multimodal Data for the Detection of Freezing of Gait in Parkinson's Disease. Sci Data 2022; 9:606. [PMID: 36207427 PMCID: PMC9546845 DOI: 10.1038/s41597-022-01713-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 09/21/2022] [Indexed: 11/15/2022] Open
Abstract
Freezing of gaits (FOG) is a very disabling symptom of Parkinson’s Disease (PD), affecting about 50% of PD patients and 80% of advanced PD patients. Studies have shown that FOG is related to a complex interplay between motor, cognitive and affective factors. A full characterization of FOG is crucial for FOG detection/prediction and prompt intervention. A protocol has been designed to acquire multimodal physical and physiological information during FOG, including gait acceleration (ACC), electroencephalogram (EEG), electromyogram (EMG), and skin conductance (SC). Two tasks were designed to trigger FOG, including gait initiation failure and FOG during walking. A total number of 12 PD patients completed the experiments and produced a length of 3 hours and 42 minutes of valid data including 2 hours and 14 minutes of normal gait and 1 hour and 28 minutes of freezing of gait. The FOG episodes were labeled by two qualified physicians. The multimodal data have been validated by a FOG detection task. Measurement(s) | electroencephalography (EEG) • electromyography(EMG) • acceleration • skin conductance | Technology Type(s) | electroencephalography (EEG) • electromyography (EMG) • accelerometer • skin conductance |
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Affiliation(s)
- Wei Zhang
- Xuanwu Hospital of Capital Medical University, Beijing Institute of Geriatrics, Department of Neurology, Neurobiology and Geriatrics, Beijing, 100053, China.,The Affiliated Hospital of Xuzhou Medical University, Department of Neurology, Xuzhou, 221006, China
| | - Zhuokun Yang
- Beihang University, Department of Automation Science and Electrical Engineering, Beijing, 100191, China
| | - Hantao Li
- Beihang University, Department of Automation Science and Electrical Engineering, Beijing, 100191, China
| | - Debin Huang
- Beihang University, Department of Automation Science and Electrical Engineering, Beijing, 100191, China
| | - Lipeng Wang
- Beihang University, Department of Automation Science and Electrical Engineering, Beijing, 100191, China
| | - Yanzhao Wei
- Beihang University, Department of Automation Science and Electrical Engineering, Beijing, 100191, China
| | - Lei Zhang
- Beihang University, Department of Automation Science and Electrical Engineering, Beijing, 100191, China
| | - Lin Ma
- Xuanwu Hospital of Capital Medical University, Beijing Institute of Geriatrics, Department of Neurology, Neurobiology and Geriatrics, Beijing, 100053, China
| | - Huanhuan Feng
- Xuanwu Hospital of Capital Medical University, Beijing Institute of Geriatrics, Department of Neurology, Neurobiology and Geriatrics, Beijing, 100053, China
| | - Jing Pan
- Xuanwu Hospital of Capital Medical University, Beijing Institute of Geriatrics, Department of Neurology, Neurobiology and Geriatrics, Beijing, 100053, China
| | - Yuzhu Guo
- Beihang University, Department of Automation Science and Electrical Engineering, Beijing, 100191, China.
| | - Piu Chan
- Xuanwu Hospital of Capital Medical University, Beijing Institute of Geriatrics, Department of Neurology, Neurobiology and Geriatrics, Beijing, 100053, China.
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22
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Ramesh V, Bilal E. Detecting motor symptom fluctuations in Parkinson's disease with generative adversarial networks. NPJ Digit Med 2022; 5:138. [PMID: 36085350 PMCID: PMC9463161 DOI: 10.1038/s41746-022-00674-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 08/08/2022] [Indexed: 11/09/2022] Open
Abstract
Parkinson's disease is a neurodegenerative disorder characterized by several motor symptoms that develop gradually: tremor, bradykinesia, limb rigidity, and gait and balance problems. While there is no cure, levodopa therapy has been shown to mitigate symptoms. A patient on levodopa experiences cycles in the severity of their symptoms, characterized by an ON state-when the drug is active-and an OFF state-when symptoms worsen as the drug wears off. The longitudinal progression of the disease is monitored using episodic assessments performed by trained physicians in the clinic, such as the Unified Parkinson's Disease Rating Scale (UPDRS). Lately, there has been an effort in the field to develop continuous, objective measures of motor symptoms based on wearable sensors and other remote monitoring devices. In this work, we present an effort towards such a solution that uses a single wearable inertial sensor to automatically assess the postural instability and gait disorder (PIGD) of a Parkinson's disease patient. Sensor data was collected from two independent studies of subjects performing the UPDRS test and then used to train and validate a convolutional neural network model. Given the typical limited size of such studies we also employed the use of generative adversarial networks to improve the performance of deep-learning models that usually require larger amounts of data for training. We show that for a 2-min walk test, our method's predicted PIGD scores can be used to identify a patient's ON/OFF states better than a physician evaluated on the same criteria. This result paves the way for more reliable, continuous tracking of Parkinson's disease symptoms.
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Affiliation(s)
- Vishwajith Ramesh
- Department of Biomedical Informatics, University of California, San Diego, CA, USA.
| | - Erhan Bilal
- T.J. Watson Research Center, IBM Research, Yorktown Heights, NY, USA
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Guo Y, Yang J, Liu Y, Chen X, Yang GZ. Detection and assessment of Parkinson's disease based on gait analysis: A survey. Front Aging Neurosci 2022; 14:916971. [PMID: 35992585 PMCID: PMC9382193 DOI: 10.3389/fnagi.2022.916971] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.
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Affiliation(s)
- Yao Guo
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Jianxin Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Yuxuan Liu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Xun Chen
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Guang-Zhong Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
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24
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Chen X. Deep Learning-Based Intelligent Robot in Sentencing. Front Psychol 2022; 13:901796. [PMID: 35923731 PMCID: PMC9341297 DOI: 10.3389/fpsyg.2022.901796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
This work aims to explore the application of deep learning-based artificial intelligence technology in sentencing, to promote the reform and innovation of the judicial system. First, the concept and the principles of sentencing are introduced, and the deep learning model of intelligent robot in trials is proposed. According to related concepts, the issues that need to be solved in artificial intelligence sentencing based on deep learning are introduced. The deep learning model is integrated into the intelligent robot system, to assist in the sentencing of cases. Finally, an example is adopted to illustrate the feasibility of the intelligent robot under deep learning in legal sentencing. The results show that the general final trial periods for cases of traffic accidents, copyright information, trademark infringement, copyright protection, and theft are 1,049, 796, 663, 847, and 201 days, respectively; while the final trial period under artificial intelligence evaluation based on the restricted Boltzmann deep learning model is 458, 387, 376, 438, and 247 days, respectively. The accuracy of trials is above 92%, showing a high application value. It can be observed that expect theft cases, the final trial period for others cases has been effectively reduced. The intelligent robot assistance under the restricted Boltzmann deep learning model can shorten the trial period of cases. The deep learning intelligent robot has a certain auxiliary role in legal sentencing, and this outcome provides a theoretical basis for the research of artificial intelligence technology in legal sentencing.
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25
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Kotei E, Thirunavukarasu R. Computational techniques for the automated detection of mycobacterium tuberculosis from digitized sputum smear microscopic images: A systematic review. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2022; 171:4-16. [PMID: 35339515 DOI: 10.1016/j.pbiomolbio.2022.03.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 02/10/2022] [Accepted: 03/18/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Tuberculosis is an infectious disease that is caused by Mycobacterium tuberculosis (MTB), which mostly affects the lungs of humans. Bright-field microscopy and fluorescence microscopy are two major testing techniques used for tuberculosis (TB) detection. TB bacilli were identified and counted manually from sputum under a microscope and were found to be tedious, laborious and error prone. To eliminate this problem, traditional image processing techniques and deep learning (DL) models were deployed here to build computer-aided diagnosis (CADx) systems for TB detection. METHODS In this paper, we performed a systematic review on image processing techniques used in developing computer-aided diagnosis systems for TB detection. Articles selected for this review were retrieved from publication databases such as Science Direct, ACM, IEEE Xplore, Springer Link and PubMed. After a rigorous pruning exercise, 42 articles were selected, of which 21 were journal articles and 21 were conference articles. RESULT Image processing techniques and deep neural networks such as CNN and DCNN proposed in the literature along with clinical applications are presented and discussed. The performance of these techniques has been evaluated on metrics such as accuracy, sensitivity, specificity, precision and F-1 score and is presented accordingly. CONCLUSION CADx systems built on DL models performed better in TB detection and classification due to their abstraction of low-level features, better generalization and minimal or no human intervention in their operations. Research gaps identified in the literature have been highlighted and discussed for further investigation.
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Affiliation(s)
- Evans Kotei
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Ramkumar Thirunavukarasu
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
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26
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Shi B, Tay A, Au WL, Tan DML, Chia NSY, Yen SC. Detection of Freezing of Gait Using Convolutional Neural Networks and Data From Lower Limb Motion Sensors. IEEE Trans Biomed Eng 2022; 69:2256-2267. [PMID: 34986092 DOI: 10.1109/tbme.2022.3140258] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Parkinson's disease (PD) is a chronic, non-reversible neurodegenerative disorder, and freezing of gait (FOG) is one of the most disabling symptoms in PD as it is often the leading cause of falls and injuries that drastically reduces patients' quality of life. In order to monitor continuously and objectively PD patients who suffer from FOG and enable the possibility of on-demand cueing assistance, a sensor-based FOG detection solution can help clinicians manage the disease and help patients overcome freezing episodes. Many recent studies have leveraged deep learning models to detect FOG using signals extracted from inertial measurement unit (IMU) devices. Usually, the latent features and patterns of FOG are discovered from either the time or frequency domain. In this study, we investigated the use of the time-frequency domain by applying the Continuous Wavelet Transform to signals from IMUs placed on the lower limbs of 63 PD patients who suffered from FOG. We built convolutional neural networks to detect the FOG occurrences, and employed the Bayesian Optimisation approach to obtain the hyper-parameters. The results showed that the proposed subject-independent model was able to achieve a geometric mean of 90.7% and a F1 score of 91.5%.
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Ramasamy Rajammal R, Mirjalili S, Ekambaram G, Palanisamy N. Binary Grey Wolf Optimizer with Mutation and Adaptive K-nearest Neighbour for Feature Selection in Parkinson’s Disease Diagnosis. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108701] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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28
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A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts. SENSORS 2022; 22:s22103859. [PMID: 35632266 PMCID: PMC9143761 DOI: 10.3390/s22103859] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/12/2022] [Accepted: 05/17/2022] [Indexed: 12/17/2022]
Abstract
Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥92%, precision ≥97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (−0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases.
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29
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Musculoskeletal Injury Recovery Assessment using Gait Analysis with Ground Reaction Force Sensor. Med Eng Phys 2022; 103:103788. [DOI: 10.1016/j.medengphy.2022.103788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 02/01/2022] [Accepted: 03/13/2022] [Indexed: 11/22/2022]
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30
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ConfusionVis: Comparative evaluation and selection of multi-class classifiers based on confusion matrices. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108651] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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31
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Exley T, Moudy S, Patterson RM, Kim J, Albert MV. Predicting UPDRS Motor Symptoms in Individuals with Parkinsons Disease from Force Plates Using Machine Learning. IEEE J Biomed Health Inform 2022; 26:3486-3494. [PMID: 35259121 DOI: 10.1109/jbhi.2022.3157518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Parkinsons disease (PD) is a neurodegenerative disease that affects motor abilities with increasing severity as the disease progresses. Traditional methods for diagnosing PD include a section where a trained specialist scores qualitative symptoms using the motor subscale of the Unified Parkinsons Disease Rating Scale (UPDRS-III). The aim of this feasibility study was twofold. First, to evaluate quiet standing as an additional, out-of-clinic, objective feature to predict UPDRS-III subscores related to motor symptom severity; and second, to use quiet standing to detect the presence of motor symptoms. Force plate data were collected from 42 PD patients and 43 healthy controls during quiet standing (a task involving standing still with eyes open and closed) as a feasible task in clinics. Predicting each subscore of the UPDRS-III could aid in identifying progression of PD and provide specialists additional tools to make an informed diagnosis. Random Forest feature importance indicated that features correlated with range of center of pressure (i.e. the medial-lateral and anterior-posterior sway) were most useful in the prediction of the top PD prediction subscores of postural stability (r = 0.599; p = 0.014), hand tremor of the left hand (r = 0.650; p = 0.015), and tremor at rest of the left upper extremity (r = 0.703; p = 0.016). Quiet standing can detect body bradykinesia (AUC-ROC = 0.924) and postural stability (AUC-ROC = 0.967) with high predictability. Although there are limited data, these results should be used as a feasibility study that evaluates the predictability of individual UPDRS-III subscores using quiet standing data.
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32
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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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O’Day J, Lee M, Seagers K, Hoffman S, Jih-Schiff A, Kidziński Ł, Delp S, Bronte-Stewart H. Assessing inertial measurement unit locations for freezing of gait detection and patient preference. J Neuroeng Rehabil 2022; 19:20. [PMID: 35152881 PMCID: PMC8842967 DOI: 10.1186/s12984-022-00992-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 01/13/2022] [Indexed: 12/28/2022] Open
Abstract
Background Freezing of gait, a common symptom of Parkinson’s disease, presents as sporadic episodes in which an individual’s feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference. Methods Sixteen people with Parkinson’s disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson’s disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events. Results The best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86) for the best technical set and minimal IMU set, respectively. Conclusions Several IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the development and adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait. Supplementary Information The online version contains supplementary material available at 10.1186/s12984-022-00992-x.
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Wulz AR, Law R, Wang J, Wolkin AF. Leveraging data science to enhance suicide prevention research: a literature review. Inj Prev 2022; 28:74-80. [PMID: 34413072 PMCID: PMC9161307 DOI: 10.1136/injuryprev-2021-044322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 07/31/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE The purpose of this research is to identify how data science is applied in suicide prevention literature, describe the current landscape of this literature and highlight areas where data science may be useful for future injury prevention research. DESIGN We conducted a literature review of injury prevention and data science in April 2020 and January 2021 in three databases. METHODS For the included 99 articles, we extracted the following: (1) author(s) and year; (2) title; (3) study approach (4) reason for applying data science method; (5) data science method type; (6) study description; (7) data source and (8) focus on a disproportionately affected population. RESULTS Results showed the literature on data science and suicide more than doubled from 2019 to 2020, with articles with individual-level approaches more prevalent than population-level approaches. Most population-level articles applied data science methods to describe (n=10) outcomes, while most individual-level articles identified risk factors (n=27). Machine learning was the most common data science method applied in the studies (n=48). A wide array of data sources was used for suicide research, with most articles (n=45) using social media and web-based behaviour data. Eleven studies demonstrated the value of applying data science to suicide prevention literature for disproportionately affected groups. CONCLUSION Data science techniques proved to be effective tools in describing suicidal thoughts or behaviour, identifying individual risk factors and predicting outcomes. Future research should focus on identifying how data science can be applied in other injury-related topics.
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Affiliation(s)
- Avital Rachelle Wulz
- Oak Ridge Associated Universities (ORAU), Division of Injury Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Royal Law
- Division of Injury Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Jing Wang
- Division of Injury Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Amy Funk Wolkin
- Division of Injury Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
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35
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Alissa M, Lones MA, Cosgrove J, Alty JE, Jamieson S, Smith SL, Vallejo M. Parkinson’s disease diagnosis using convolutional neural networks and figure-copying tasks. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06469-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractParkinson’s disease (PD) is a progressive neurodegenerative disorder that causes abnormal movements and an array of other symptoms. An accurate PD diagnosis can be a challenging task as the signs and symptoms, particularly at an early stage, can be similar to other medical conditions or the physiological changes of normal ageing. This work aims to contribute to the PD diagnosis process by using a convolutional neural network, a type of deep neural network architecture, to differentiate between healthy controls and PD patients. Our approach focuses on discovering deviations in patient’s movements with the use of drawing tasks. In addition, this work explores which of two drawing tasks, wire cube or spiral pentagon, are more effective in the discrimination process. With $$93.5\%$$
93.5
%
accuracy, our convolutional classifier, trained with images of the pentagon drawing task and augmentation techniques, can be used as an objective method to discriminate PD from healthy controls. Our compact model has the potential to be developed into an offline real-time automated single-task diagnostic tool, which can be easily deployed within a clinical setting.
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36
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Shalin G, Pardoel S, Lemaire ED, Nantel J, Kofman J. Prediction and detection of freezing of gait in Parkinson's disease from plantar pressure data using long short-term memory neural-networks. J Neuroeng Rehabil 2021; 18:167. [PMID: 34838066 PMCID: PMC8626900 DOI: 10.1186/s12984-021-00958-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 11/10/2021] [Indexed: 11/10/2022] Open
Abstract
Background Freezing of gait (FOG) is a walking disturbance in advanced stage Parkinson’s disease (PD) that has been associated with increased fall risk and decreased quality of life. Freezing episodes can be mitigated or prevented with external intervention such as visual or auditory cues, activated by FOG prediction and detection systems. While most research on FOG detection and prediction has been based on inertial measurement unit (IMU) and accelerometer data, plantar-pressure data may capture subtle weight shifts unique to FOG episodes. Different machine learning algorithms have been used for FOG detection and prediction; however, long short-term memory (LSTM) deep learning methods hold an advantage when dealing with time-series data, such as sensor data. This research aimed to determine if LSTM can be used to detect and predict FOG from plantar pressure data alone, specifically for use in a real-time wearable system. Methods Plantar pressure data were collected from pressure-sensing insole sensors worn by 11 participants with PD as they walked a predefined freeze-provoking path. FOG instances were labelled, 16 features were extracted, and the dataset was balanced and normalized (z-score). The resulting datasets were classified using long short-term memory neural-network models. Separate models were trained for detection and prediction. For prediction models, data before FOG were included in the target class. Leave-one-freezer-out cross validation was used for model evaluation. In addition, the models were tested on all non-freezer data to determine model specificity. Results The best FOG detection model had 82.1% (SD 6.2%) mean sensitivity and 89.5% (SD 3.6%) mean specificity for one-freezer-held-out cross validation. Specificity improved to 93.3% (SD 4.0%) when ignoring inactive state data (standing) and analyzing the model only on active states (turning and walking). The model correctly detected 95% of freeze episodes. The best FOG prediction method achieved 72.5% (SD 13.6%) mean sensitivity and 81.2% (SD 6.8%) mean specificity for one-freezer-held-out cross validation. Conclusions Based on FOG data collected in a laboratory, the results suggest that plantar pressure data can be used for FOG detection and prediction. However, further research is required to improve FOG prediction performance, including training with a larger sample of people who experience FOG.
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Affiliation(s)
- Gaurav Shalin
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Scott Pardoel
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Edward D Lemaire
- Faculty of Medicine, University of Ottawa and Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Julie Nantel
- School of Human Kinetics, University of Ottawa, Ottawa, ON, Canada
| | - Jonathan Kofman
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada.
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37
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Godkin FE, Turner E, Demnati Y, Vert A, Roberts A, Swartz RH, McLaughlin PM, Weber KS, Thai V, Beyer KB, Cornish B, Abrahao A, Black SE, Masellis M, Zinman L, Beaton D, Binns MA, Chau V, Kwan D, Lim A, Munoz DP, Strother SC, Sunderland KM, Tan B, McIlroy WE, Van Ooteghem K. Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease. J Neurol 2021; 269:2673-2686. [PMID: 34705114 PMCID: PMC8548705 DOI: 10.1007/s00415-021-10831-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND Remote health monitoring with wearable sensor technology may positively impact patient self-management and clinical care. In individuals with complex health conditions, multi-sensor wear may yield meaningful information about health-related behaviors. Despite available technology, feasibility of device-wearing in daily life has received little attention in persons with physical or cognitive limitations. This mixed methods study assessed the feasibility of continuous, multi-sensor wear in persons with cerebrovascular (CVD) or neurodegenerative disease (NDD). METHODS Thirty-nine participants with CVD, Alzheimer's disease/amnestic mild cognitive impairment, frontotemporal dementia, Parkinson's disease, or amyotrophic lateral sclerosis (median age 68 (45-83) years, 36% female) wore five devices (bilateral ankles and wrists, chest) continuously for a 7-day period. Adherence to device wearing was quantified by examining volume and pattern of device removal (non-wear). A thematic analysis of semi-structured de-brief interviews with participants and study partners was used to examine user acceptance. RESULTS Adherence to multi-sensor wear, defined as a minimum of three devices worn concurrently, was high (median 98.2% of the study period). Non-wear rates were low across all sensor locations (median 17-22 min/day), with significant differences between some locations (p = 0.006). Multi-sensor non-wear was higher for daytime versus nighttime wear (p < 0.001) and there was a small but significant increase in non-wear over the collection period (p = 0.04). Feedback from de-brief interviews suggested that multi-sensor wear was generally well accepted by both participants and study partners. CONCLUSION A continuous, multi-sensor remote health monitoring approach is feasible in a cohort of persons with CVD or NDD.
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Affiliation(s)
- F Elizabeth Godkin
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Erin Turner
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Youness Demnati
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Adam Vert
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Angela Roberts
- School of Communication Sciences and Disorders, Elborn College, Western University, London, ON, Canada.,Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA
| | - Richard H Swartz
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | | | - Kyle S Weber
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Vanessa Thai
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Kit B Beyer
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Benjamin Cornish
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Agessandro Abrahao
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Sandra E Black
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Mario Masellis
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Lorne Zinman
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Malcolm A Binns
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Vivian Chau
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Donna Kwan
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Andrew Lim
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Douglas P Munoz
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Kelly M Sunderland
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Brian Tan
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - William E McIlroy
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Karen Van Ooteghem
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
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38
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Pardoel S, Shalin G, Lemaire ED, Kofman J, Nantel J. Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson's disease. PLoS One 2021; 16:e0258544. [PMID: 34637473 PMCID: PMC8509886 DOI: 10.1371/journal.pone.0258544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 09/29/2021] [Indexed: 11/24/2022] Open
Abstract
Freezing of gait (FOG) is an intermittent walking disturbance experienced by people with Parkinson's disease (PD). Wearable FOG identification systems can improve gait and reduce the risk of falling due to FOG by detecting FOG in real-time and providing a cue to reduce freeze duration. However, FOG prediction and prevention is desirable. Datasets used to train machine learning models often generate ground truth FOG labels based on visual observation of specific lower limb movements (event-based definition) or an overall inability to walk effectively (period of gait disruption based definition). FOG definition ambiguity may affect model performance, especially with respect to multiple FOG in rapid succession. This research examined whether merging multiple freezes that occurred in rapid succession could improve FOG detection and prediction model performance. Plantar pressure and lower limb acceleration data were used to extract a feature set and train decision tree ensembles. FOG was labeled using an event-based definition. Additional datasets were then produced by merging FOG that occurred in rapid succession. A merging threshold was introduced where FOG that were separated by less than the merging threshold were merged into one episode. FOG detection and prediction models were trained for merging thresholds of 0, 1, 2, and 3 s. Merging slightly improved FOG detection model performance; however, for the prediction model, merging resulted in slightly later FOG identification and lower precision. FOG prediction models may benefit from using event-based FOG definitions and avoiding merging multiple FOG in rapid succession.
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Affiliation(s)
- Scott Pardoel
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Gaurav Shalin
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Edward D. Lemaire
- Faculty of Medicine, University of Ottawa and Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Jonathan Kofman
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Julie Nantel
- School of Human Kinetics, University of Ottawa, Ottawa, ON, Canada
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39
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Ashfaque Mostafa T, Soltaninejad S, McIsaac TL, Cheng I. A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction. SENSORS (BASEL, SWITZERLAND) 2021; 21:6446. [PMID: 34640763 PMCID: PMC8512068 DOI: 10.3390/s21196446] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 09/13/2021] [Accepted: 09/24/2021] [Indexed: 11/30/2022]
Abstract
Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson's Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, its detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on a publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University.
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Affiliation(s)
- Tahjid Ashfaque Mostafa
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada;
| | - Sara Soltaninejad
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada;
| | - Tara L. McIsaac
- Arizona School of Health Sciences, A.T. Still University, 5850 E. Still Circle, Mesa, AZ 85206, USA;
- School of Pharmacy and Health Professions, Creighton University Health Sciences, 3100 N. Central Ave., Phoenix, AZ 85013, USA
| | - Irene Cheng
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada;
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40
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Naghavi N, Wade E. Towards Real-time Prediction of Freezing of Gait in Patients with Parkinsons Disease: A Novel Deep One-class Classifier. IEEE J Biomed Health Inform 2021; 26:1726-1736. [PMID: 34375292 DOI: 10.1109/jbhi.2021.3103071] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinsons disease. FoG impairs walking and is associated with increased fall risk. On-demand external cueing systems can detect FoG and provide stimuli to help individuals overcome freezing. Predicting FoG before onset enables preemptive cueing and may prevent FoG. However, detection and prediction remain challenging. If FoG data are not available for an individual, patient-independent models have been used to detect FoG, which have shown great sensitivity and poor specificity, or vice versa. In this study, we introduce a Deep Gait Anomaly Detector (DGAD) using a transfer learning-based approach to improve FoG detection accuracy. We also evaluate the effect of data augmentation and additional pre-FoG segments on prediction rate. Seven individuals with PD performed a series of daily walking tasks wearing inertial measurement units on their ankles. The DGAD algorithm demonstrated average sensitivity and specificity of 63.0% and 98.6% (3.2% improvement compared with the highest specificity in the literature). The target models identified 87.4% of FoG onsets, with 21.9% predicted. This study demonstrates our algorithm's potential for accurate identification of FoG and delivery of cues for patients for whom no FoG data is available for model training.
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41
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Cai X. A Novel Disease Diagnosis Method Using Combining Knowledge Graph and Deep Learning. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Disease diagnosis methods based on deep learning have some shortcomings in the auxiliary diagnosis process, such as relying heavily on labeled data and lack of doctor or expert experience knowledge. Based on the above background, this study proposes a disease diagnosis method combining
medical knowledge atlas and deep learning (CKGDL). The core of the method is a knowledge-driven convolutional neural network (CNN) model. The structured disease knowledge in the medical knowledge map is obtained through entity link disambiguation and knowledge map embedding and extraction.
The disease feature word vector and the corresponding knowledge entity vector in the disease description text are used as the multi-channel input of CNN, and different types of diseases are expressed from the semantic and knowledge levels in the convolution process. Through training and testing
on multiple types of disease description text data sets, the experimental results show that the diagnostic performance of this method is better than that of a single CNN model and other disease diagnosis methods. And further verified that this method of joint training of knowledge and data
is more suitable for the initial diagnosis of the disease.
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Affiliation(s)
- Xi Cai
- ChongQing Technology and Business Institute, ChongQing, 400052, China
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42
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Alzubaidi MS, Shah U, Dhia Zubaydi H, Dolaat K, Abd-Alrazaq AA, Ahmed A, Househ M. The Role of Neural Network for the Detection of Parkinson's Disease: A Scoping Review. Healthcare (Basel) 2021; 9:healthcare9060740. [PMID: 34208654 PMCID: PMC8235532 DOI: 10.3390/healthcare9060740] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 05/26/2021] [Accepted: 05/26/2021] [Indexed: 11/16/2022] Open
Abstract
Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available to diagnose PD conclusively. Therefore, computer-aided diagnosis (CAD) systems offered a better solution to make the necessary data-driven decisions and assist the physician. Numerous studies were conducted to propose CAD to diagnose PD in the early stages. No comprehensive reviews have been conducted to summarize the role of AI tools to combat PD. Objective: The study aimed to explore and summarize the applications of neural networks to diagnose PD. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed to conduct this scoping review. To identify the relevant studies, both medical databases (e.g., PubMed) and technical databases (IEEE) were searched. Three reviewers carried out the study selection and extracted the data from the included studies independently. Then, the narrative approach was adopted to synthesis the extracted data. Results: Out of 1061 studies, 91 studies satisfied the eligibility criteria in this review. About half of the included studies have implemented artificial neural networks to diagnose PD. Numerous studies included focused on the freezing of gait (FoG). Biomedical voice and signal datasets were the most commonly used data types to develop and validate these models. However, MRI- and CT-scan images were also utilized in the included studies. Conclusion: Neural networks play an integral and substantial role in combating PD. Many possible applications of neural networks were identified in this review, however, most of them are limited up to research purposes.
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Affiliation(s)
- Mahmood Saleh Alzubaidi
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 53, Qatar; (U.S.); (K.D.); (A.A.A.-A.); (A.A.)
- Correspondence: (M.S.A.); (M.H.)
| | - Uzair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 53, Qatar; (U.S.); (K.D.); (A.A.A.-A.); (A.A.)
| | - Haider Dhia Zubaydi
- National Advanced IPv6 Centre, Universiti Sains Malaysia, Gelugor 11800, Malaysia;
| | - Khalid Dolaat
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 53, Qatar; (U.S.); (K.D.); (A.A.A.-A.); (A.A.)
| | - Alaa A. Abd-Alrazaq
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 53, Qatar; (U.S.); (K.D.); (A.A.A.-A.); (A.A.)
| | - Arfan Ahmed
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 53, Qatar; (U.S.); (K.D.); (A.A.A.-A.); (A.A.)
| | - Mowafa Househ
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 53, Qatar; (U.S.); (K.D.); (A.A.A.-A.); (A.A.)
- Correspondence: (M.S.A.); (M.H.)
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43
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Raykov YP, Evers LJW, Badawy R, Bloem BR, Heskes TM, Meinders MJ, Claes K, Little MA. Probabilistic Modelling of Gait for Robust Passive Monitoring in Daily Life. IEEE J Biomed Health Inform 2021; 25:2293-2304. [PMID: 33180738 DOI: 10.1109/jbhi.2020.3037857] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Passive monitoring in daily life may provide valuable insights into a person's health throughout the day. Wearable sensor devices play a key role in enabling such monitoring in a non-obtrusive fashion. However, sensor data collected in daily life reflect multiple health and behavior-related factors together. This creates the need for a structured principled analysis to produce reliable and interpretable predictions that can be used to support clinical diagnosis and treatment. In this work we develop a principled modelling approach for free-living gait (walking) analysis. Gait is a promising target for non-obtrusive monitoring because it is common and indicative of many different movement disorders such as Parkinson's disease (PD), yet its analysis has largely been limited to experimentally controlled lab settings. To locate and characterize stationary gait segments in free-living using accelerometers, we present an unsupervised probabilistic framework designed to segment signals into differing gait and non-gait patterns. We evaluate the approach using a new video-referenced dataset including 25 PD patients with motor fluctuations and 25 age-matched controls, performing unscripted daily living activities in and around their own houses. Using this dataset, we demonstrate the framework's ability to detect gait and predict medication induced fluctuations in PD patients based on free-living gait. We show that our approach is robust to varying sensor locations, including the wrist, ankle, trouser pocket and lower back.
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44
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Gravity Control-Based Data Augmentation Technique for Improving VR User Activity Recognition. Symmetry (Basel) 2021. [DOI: 10.3390/sym13050845] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The neural-network-based human activity recognition (HAR) technique is being increasingly used for activity recognition in virtual reality (VR) users. The major issue of a such technique is the collection large-scale training datasets which are key for deriving a robust recognition model. However, collecting large-scale data is a costly and time-consuming process. Furthermore, increasing the number of activities to be classified will require a much larger number of training datasets. Since training the model with a sparse dataset can only provide limited features to recognition models, it can cause problems such as overfitting and suboptimal results. In this paper, we present a data augmentation technique named gravity control-based augmentation (GCDA) to alleviate the sparse data problem by generating new training data based on the existing data. The benefits of the symmetrical structure of the data are that it increased the number of data while preserving the properties of the data. The core concept of GCDA is two-fold: (1) decomposing the acceleration data obtained from the inertial measurement unit (IMU) into zero-gravity acceleration and gravitational acceleration, and augmenting them separately, and (2) exploiting gravity as a directional feature and controlling it to augment training datasets. Through the comparative evaluations, we validated that the application of GCDA to training datasets showed a larger improvement in classification accuracy (96.39%) compared to the typical data augmentation methods (92.29%) applied and those that did not apply the augmentation method (85.21%).
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45
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Bikias T, Iakovakis D, Hadjidimitriou S, Charisis V, Hadjileontiadis LJ. DeepFoG: An IMU-Based Detection of Freezing of Gait Episodes in Parkinson's Disease Patients via Deep Learning. Front Robot AI 2021; 8:537384. [PMID: 34113654 PMCID: PMC8185568 DOI: 10.3389/frobt.2021.537384] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Accepted: 04/12/2021] [Indexed: 11/24/2022] Open
Abstract
Freezing of Gait (FoG) is a movement disorder that mostly appears in the late stages of Parkinson’s Disease (PD). It causes incapability of walking, despite the PD patient’s intention, resulting in loss of coordination that increases the risk of falls and injuries and severely affects the PD patient’s quality of life. Stress, emotional stimulus, and multitasking have been encountered to be associated with the appearance of FoG episodes, while the patient’s functionality and self-confidence are constantly deteriorating. This study suggests a non-invasive method for detecting FoG episodes, by analyzing inertial measurement unit (IMU) data. Specifically, accelerometer and gyroscope data from 11 PD subjects, as captured from a single wrist-worn IMU sensor during continuous walking, are processed via Deep Learning for window-based detection of the FoG events. The proposed approach, namely DeepFoG, was evaluated in a Leave-One-Subject-Out (LOSO) cross-validation (CV) and 10-fold CV fashion schemes against its ability to correctly estimate the existence or not of a FoG episode at each data window. Experimental results have shown that DeepFoG performs satisfactorily, as it achieves 83%/88% and 86%/90% sensitivity/specificity, for LOSO CV and 10-fold CV schemes, respectively. The promising performance of the proposed DeepFoG reveals the potentiality of single-arm IMU-based real-time FoG detection that could guide effective interventions via stimuli, such as rhythmic auditory stimulation (RAS) and hand vibration. In this way, DeepFoG may scaffold the elimination of risk of falls in PD patients, sustaining their quality of life in everyday living activities.
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Affiliation(s)
- Thomas Bikias
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios Iakovakis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Stelios Hadjidimitriou
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vasileios Charisis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leontios J Hadjileontiadis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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46
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Mirelman A, Ben Or Frank M, Melamed M, Granovsky L, Nieuwboer A, Rochester L, Del Din S, Avanzino L, Pelosin E, Bloem BR, Della Croce U, Cereatti A, Bonato P, Camicioli R, Ellis T, Hamilton JL, Hass CJ, Almeida QJ, Inbal M, Thaler A, Shirvan J, Cedarbaum JM, Giladi N, Hausdorff JM. Detecting Sensitive Mobility Features for Parkinson's Disease Stages Via Machine Learning. Mov Disord 2021; 36:2144-2155. [PMID: 33955603 DOI: 10.1002/mds.28631] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 04/10/2021] [Accepted: 04/12/2021] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND It is not clear how specific gait measures reflect disease severity across the disease spectrum in Parkinson's disease (PD). OBJECTIVE To identify the gait and mobility measures that are most sensitive and reflective of PD motor stages and determine the optimal sensor location in each disease stage. METHODS Cross-sectional wearable-sensor records were collected in 332 patients with PD (Hoehn and Yahr scale I-III) and 100 age-matched healthy controls. Sensors were adhered to the participant's lower back, bilateral ankles, and wrists. Study participants walked in a ~15-meter corridor for 1 minute under two walking conditions: (1) preferred, usual walking speed and (2) walking while engaging in a cognitive task (dual-task). A subgroup (n = 303, 67% PD) also performed the Timed Up and Go test. Multiple machine-learning feature selection and classification algorithms were applied to discriminate between controls and PD and between the different PD severity stages. RESULTS High discriminatory values were found between motor disease stages with mean sensitivity in the range 72%-83%, specificity 69%-80%, and area under the curve (AUC) 0.76-0.90. Measures from upper-limb sensors best discriminated controls from early PD, turning measures obtained from the trunk sensor were prominent in mid-stage PD, and stride timing and regularity were discriminative in more advanced stages. CONCLUSIONS Applying machine-learning to multiple, wearable-derived features reveals that different measures of gait and mobility are associated with and discriminate distinct stages of PD. These disparate feature sets can augment the objective monitoring of disease progression and may be useful for cohort selection and power analyses in clinical trials of PD. © 2021 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Anat Mirelman
- Laboratory for Early Markers Of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Mor Ben Or Frank
- Laboratory for Early Markers Of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | | | | | - Alice Nieuwboer
- Department of Rehabilitation Science, KU Leuven, Neuromotor Rehabilitation Research Group, Leuven, Belgium
| | - Lynn Rochester
- Institute of Neuroscience, Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, UK
| | - Silvia Del Din
- Institute of Neuroscience, Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, UK
| | - Laura Avanzino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy.,IRCCS Policlinico San Martino Teaching Hospital, Genoa, Italy
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy.,IRCCS Policlinico San Martino Teaching Hospital, Genoa, Italy
| | - Bastiaan R Bloem
- Department of Neurology, Radboud University Medical Center; Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands
| | - Ugo Della Croce
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Andrea Cereatti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy.,Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Paolo Bonato
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA
| | - Richard Camicioli
- Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Theresa Ellis
- Department of Physical Therapy & Athletic Training, Boston University, Boston, Massachusetts, USA
| | - Jamie L Hamilton
- Michael J. Fox Foundation for Parkinson's Research, New York, New York, USA
| | - Chris J Hass
- College of Health & Human Performance, Applied Physiology and Kinesiology, University of Florida, Gainesville, Florida, USA
| | - Quincy J Almeida
- Movement Disorders Research & Rehabilitation Centre, Wilfrid Laurier University, Waterloo, Canada
| | - Maidan Inbal
- Laboratory for Early Markers Of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Avner Thaler
- Laboratory for Early Markers Of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | | | - Jesse M Cedarbaum
- Coeruleus Clinical Sciences, Woodbridge, Connecticut, USA.,Yale University School of Medicine, New Haven, Connecticut, USA
| | - Nir Giladi
- Laboratory for Early Markers Of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jeffrey M Hausdorff
- Laboratory for Early Markers Of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Department of Physical Therapy, Tel Aviv University, Tel Aviv, Israel.,Department of Orthopedic Surgery, Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
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47
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Shahtalebi S, Atashzar SF, Patel RV, Jog MS, Mohammadi A. A deep explainable artificial intelligent framework for neurological disorders discrimination. Sci Rep 2021; 11:9630. [PMID: 33953261 PMCID: PMC8099874 DOI: 10.1038/s41598-021-88919-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 04/13/2021] [Indexed: 02/03/2023] Open
Abstract
Pathological hand tremor (PHT) is a common symptom of Parkinson's disease (PD) and essential tremor (ET), which affects manual targeting, motor coordination, and movement kinetics. Effective treatment and management of the symptoms relies on the correct and in-time diagnosis of the affected individuals, where the characteristics of PHT serve as an imperative metric for this purpose. Due to the overlapping features of the corresponding symptoms, however, a high level of expertise and specialized diagnostic methodologies are required to correctly distinguish PD from ET. In this work, we propose the data-driven [Formula: see text] model, which processes the kinematics of the hand in the affected individuals and classifies the patients into PD or ET. [Formula: see text] is trained over 90 hours of hand motion signals consisting of 250 tremor assessments from 81 patients, recorded at the London Movement Disorders Centre, ON, Canada. The [Formula: see text] outperforms its state-of-the-art counterparts achieving exceptional differential diagnosis accuracy of [Formula: see text]. In addition, using the explainability and interpretability measures for machine learning models, clinically viable and statistically significant insights on how the data-driven model discriminates between the two groups of patients are achieved.
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Affiliation(s)
- Soroosh Shahtalebi
- grid.410319.e0000 0004 1936 8630Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC H3G 1M8 Canada
| | - S. Farokh Atashzar
- grid.137628.90000 0004 1936 8753Departments of Electrical and Computer Engineering, and Mechanical and Aerospace Engineering, New York University (NYU), New York, NY 10003 USA ,grid.137628.90000 0004 1936 8753NYU WIRELESS and NYU Center for Urban Science and Progress (CUSP), New York University (NYU), New York, NY 10003 USA
| | - Rajni V. Patel
- grid.39381.300000 0004 1936 8884Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9 Canada ,grid.39381.300000 0004 1936 8884Department of Clinical Neurological Sciences, Western University, London, ON N6A 3K7 Canada
| | - Mandar S. Jog
- grid.39381.300000 0004 1936 8884Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9 Canada ,grid.39381.300000 0004 1936 8884Department of Clinical Neurological Sciences, Western University, London, ON N6A 3K7 Canada
| | - Arash Mohammadi
- grid.410319.e0000 0004 1936 8630Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC H3G 1M8 Canada
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48
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Tăuţan AM, Ionescu B, Santarnecchi E. Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques. Artif Intell Med 2021; 117:102081. [PMID: 34127244 DOI: 10.1016/j.artmed.2021.102081] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/21/2021] [Accepted: 04/26/2021] [Indexed: 10/21/2022]
Abstract
Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.
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Affiliation(s)
- Alexandra-Maria Tăuţan
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Bogdan Ionescu
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Harvard Medical School, 330 Brookline Avenue, Boston, United States.
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49
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Termine A, Fabrizio C, Strafella C, Caputo V, Petrosini L, Caltagirone C, Giardina E, Cascella R. Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence. J Pers Med 2021; 11:280. [PMID: 33917161 PMCID: PMC8067806 DOI: 10.3390/jpm11040280] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/05/2021] [Accepted: 04/06/2021] [Indexed: 12/13/2022] Open
Abstract
In the big data era, artificial intelligence techniques have been applied to tackle traditional issues in the study of neurodegenerative diseases. Despite the progress made in understanding the complex (epi)genetics signatures underlying neurodegenerative disorders, performing early diagnosis and developing drug repurposing strategies remain serious challenges for such conditions. In this context, the integration of multi-omics, neuroimaging, and electronic health records data can be exploited using deep learning methods to provide the most accurate representation of patients possible. Deep learning allows researchers to find multi-modal biomarkers to develop more effective and personalized treatments, early diagnosis tools, as well as useful information for drug discovering and repurposing in neurodegenerative pathologies. In this review, we will describe how relevant studies have been able to demonstrate the potential of deep learning to enhance the knowledge of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases through the integration of all sources of biomedical data.
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Affiliation(s)
- Andrea Termine
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
| | - Carlo Fabrizio
- IRCCS Santa Lucia Foundation, Laboratory of Experimental and Behavioral Neurophysiology, 00143 Rome, Italy; (C.F.); (L.P.)
| | - Claudia Strafella
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- Department of Biomedicine and Prevention, Tor Vergata University of Rome, 00133 Rome, Italy
| | - Valerio Caputo
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- Department of Biomedicine and Prevention, Tor Vergata University of Rome, 00133 Rome, Italy
| | - Laura Petrosini
- IRCCS Santa Lucia Foundation, Laboratory of Experimental and Behavioral Neurophysiology, 00143 Rome, Italy; (C.F.); (L.P.)
| | - Carlo Caltagirone
- IRCCS Santa Lucia Foundation, Department of Clinical and Behavioral Neurology, 00179 Rome, Italy;
| | - Emiliano Giardina
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- UILDM Lazio ONLUS Foundation, Department of Biomedicine and Prevention, Tor Vergata University, 00133 Rome, Italy
| | - Raffaella Cascella
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- Department of Biomedical Sciences, Catholic University Our Lady of Good Counsel, 1000 Tirana, Albania
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50
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Wang C, Qi H. Visualising the knowledge structure and evolution of wearable device research. J Med Eng Technol 2021; 45:207-222. [PMID: 33769166 DOI: 10.1080/03091902.2021.1891314] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
In recent years, the literature associated with wearable devices has grown rapidly, but few studies have used bibliometrics and a visualisation approach to conduct deep mining and reveal a panorama of the wearable devices field. To explore the foundational knowledge and research hotspots of the wearable devices field, this study conducted a series of bibliometric analyses on the related literature, including papers' production trends in the field and the distribution of countries, a keyword co-occurrence analysis, theme evolution analysis and research hotspots and trends for the future. By conducting a literature content analysis and structure analysis, we found the following: (a) The subject evolution path includes sensor research, sensitivity research and multi-functional device research. (b) Wearable device research focuses on information collection, sensor materials, manufacturing technology and application, artificial intelligence technology application, energy supply and medical applications. The future development trend will be further studied in combination with big data analysis, telemedicine and personalised precision medical application.
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Affiliation(s)
- Chen Wang
- Department of Health informatics and Management, School of Health Humanities, Peking University, Beijing, China
| | - Huiying Qi
- Department of Health informatics and Management, School of Health Humanities, Peking University, Beijing, China
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