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Peppes N, Tsakanikas P, Daskalakis E, Alexakis T, Adamopoulou E, Demestichas K. FoGGAN: Generating Realistic Parkinson's Disease Freezing of Gait Data Using GANs. Sensors (Basel) 2023; 23:8158. [PMID: 37836988 PMCID: PMC10574838 DOI: 10.3390/s23198158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 09/23/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023]
Abstract
Data scarcity in the healthcare domain is a major drawback for most state-of-the-art technologies engaging artificial intelligence. The unavailability of quality data due to both the difficulty to gather and label them as well as due to their sensitive nature create a breeding ground for data augmentation solutions. Parkinson's Disease (PD) which can have a wide range of symptoms including motor impairments consists of a very challenging case for quality data acquisition. Generative Adversarial Networks (GANs) can help alleviate such data availability issues. In this light, this study focuses on a data augmentation solution engaging Generative Adversarial Networks (GANs) using a freezing of gait (FoG) symptom dataset as input. The data generated by the so-called FoGGAN architecture presented in this study are almost identical to the original as concluded by a variety of similarity metrics. This highlights the significance of such solutions as they can provide credible synthetically generated data which can be utilized as training dataset inputs to AI applications. Additionally, a DNN classifier's performance is evaluated using three different evaluation datasets and the accuracy results were quite encouraging, highlighting that the FOGGAN solution could lead to the alleviation of the data shortage matter.
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Affiliation(s)
- Nikolaos Peppes
- Institute of Communication and Computer Systems, National Technical University of Athens, 15773 Athens, Greece; (P.T.); (E.D.); (T.A.); (E.A.)
| | - Panagiotis Tsakanikas
- Institute of Communication and Computer Systems, National Technical University of Athens, 15773 Athens, Greece; (P.T.); (E.D.); (T.A.); (E.A.)
| | - Emmanouil Daskalakis
- Institute of Communication and Computer Systems, National Technical University of Athens, 15773 Athens, Greece; (P.T.); (E.D.); (T.A.); (E.A.)
| | - Theodoros Alexakis
- Institute of Communication and Computer Systems, National Technical University of Athens, 15773 Athens, Greece; (P.T.); (E.D.); (T.A.); (E.A.)
| | - Evgenia Adamopoulou
- Institute of Communication and Computer Systems, National Technical University of Athens, 15773 Athens, Greece; (P.T.); (E.D.); (T.A.); (E.A.)
| | - Konstantinos Demestichas
- Department of Agricultural Economics and Rural Development, Agricultural University of Athens, 11855 Athens, Greece;
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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) 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Marquez JS, Hasan SMS, Siddiquee MR, Luca CC, Mishra VR, Mari Z, Bai O. Neural Correlates of Freezing of Gait in Parkinson's Disease: An Electrophysiology Mini-Review. Front Neurol 2020; 11:571086. [PMID: 33240199 PMCID: PMC7683766 DOI: 10.3389/fneur.2020.571086] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/23/2020] [Indexed: 12/13/2022] Open
Abstract
Freezing of gait (FoG) is a disabling symptom characterized as a brief inability to step or by short steps, which occurs when initiating gait or while turning, affecting over half the population with advanced Parkinson's disease (PD). Several non-competing hypotheses have been proposed to explain the pathophysiology and mechanism behind FoG. Yet, due to the complexity of FoG and the lack of a complete understanding of its mechanism, no clear consensus has been reached on the best treatment options. Moreover, most studies that aim to explore neural biomarkers of FoG have been limited to semi-static or imagined paradigms. One of the biggest unmet needs in the field is the identification of reliable biomarkers that can be construed from real walking scenarios to guide better treatments and validate medical and therapeutic interventions. Advances in neural electrophysiology exploration, including EEG and DBS, will allow for pathophysiology research on more real-to-life scenarios for better FoG biomarker identification and validation. The major aim of this review is to highlight the most up-to-date studies that explain the mechanisms underlying FoG through electrophysiology explorations. The latest methodological approaches used in the neurophysiological study of FoG are summarized, and potential future research directions are discussed.
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Affiliation(s)
- J. Sebastian Marquez
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, United States
| | - S. M. Shafiul Hasan
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, United States
| | - Masudur R. Siddiquee
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, United States
| | - Corneliu C. Luca
- Department of Neurology, University of Miami Hospital, Miami, FL, United States
| | - Virendra R. Mishra
- Lou Ruvo Center for Brain Health, Cleveland Clinic, Las Vegas, NV, United States
| | - Zoltan Mari
- Lou Ruvo Center for Brain Health, Cleveland Clinic, Las Vegas, NV, United States
| | - Ou Bai
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, United States
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Weiss D, Schoellmann A, Fox MD, Bohnen NI, Factor SA, Nieuwboer A, Hallett M, Lewis SJG. Freezing of gait: understanding the complexity of an enigmatic phenomenon. Brain 2020; 143:14-30. [PMID: 31647540 DOI: 10.1093/brain/awz314] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 08/07/2019] [Accepted: 08/16/2019] [Indexed: 12/15/2022] Open
Abstract
Diverse but complementary methodologies are required to uncover the complex determinants and pathophysiology of freezing of gait. To develop future therapeutic avenues, we need a deeper understanding of the disseminated functional-anatomic network and its temporally associated dynamic processes. In this targeted review, we will summarize the latest advances across multiple methodological domains including clinical phenomenology, neurogenetics, multimodal neuroimaging, neurophysiology, and neuromodulation. We found that (i) locomotor network vulnerability is established by structural damage, e.g. from neurodegeneration possibly as result from genetic variability, or to variable degree from brain lesions. This leads to an enhanced network susceptibility, where (ii) modulators can both increase or decrease the threshold to express freezing of gait. Consequent to a threshold decrease, (iii) neuronal integration failure of a multilevel brain network will occur and affect one or numerous nodes and projections of the multilevel network. Finally, (iv) an ultimate pathway might encounter failure of effective motor output and give rise to freezing of gait as clinical endpoint. In conclusion, we derive key questions from this review that challenge this pathophysiological view. We suggest that future research on these questions should lead to improved pathophysiological insight and enhanced therapeutic strategies.
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Affiliation(s)
- Daniel Weiss
- Centre for Neurology, Department for Neurodegenerative Diseases, and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Anna Schoellmann
- Centre for Neurology, Department for Neurodegenerative Diseases, and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Michael D Fox
- Berenson-Allen Center, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical Center, Boston, MA, USA.,Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Nicolaas I Bohnen
- Departments of Radiology and Neurology, University of Michigan, Ann Arbor, MI, USA; Veterans Administration Ann Arbor Healthcare System, Ann Arbor, MI, USA; Morris K. Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, MI, USA
| | - Stewart A Factor
- Department of Neurology, Emory School of Medicine, Atlanta, GA, USA
| | - Alice Nieuwboer
- Neuromotor Rehabilitation Research Group, Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Mark Hallett
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Simon J G Lewis
- Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Australia
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