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Yang PK, Filtjens B, Ginis P, Goris M, Nieuwboer A, Gilat M, Slaets P, Vanrumste B. Freezing of gait assessment with inertial measurement units and deep learning: effect of tasks, medication states, and stops. J Neuroeng Rehabil 2024; 21:24. [PMID: 38350964 PMCID: PMC10865632 DOI: 10.1186/s12984-024-01320-1] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 01/30/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson's Disease (PD). Traditionally, FOG assessment relies on time-consuming visual inspection of camera footage. Therefore, previous studies have proposed portable and automated solutions to annotate FOG. However, automated FOG assessment is challenging due to gait variability caused by medication effects and varying FOG-provoking tasks. Moreover, whether automated approaches can differentiate FOG from typical everyday movements, such as volitional stops, remains to be determined. To address these questions, we evaluated an automated FOG assessment model with deep learning (DL) based on inertial measurement units (IMUs). We assessed its performance trained on all standardized FOG-provoking tasks and medication states, as well as on specific tasks and medication states. Furthermore, we examined the effect of adding stopping periods on FOG detection performance. METHODS Twelve PD patients with self-reported FOG (mean age 69.33 ± 6.02 years) completed a FOG-provoking protocol, including timed-up-and-go and 360-degree turning-in-place tasks in On/Off dopaminergic medication states with/without volitional stopping. IMUs were attached to the pelvis and both sides of the tibia and talus. A temporal convolutional network (TCN) was used to detect FOG episodes. FOG severity was quantified by the percentage of time frozen (%TF) and the number of freezing episodes (#FOG). The agreement between the model-generated outcomes and the gold standard experts' video annotation was assessed by the intra-class correlation coefficient (ICC). RESULTS For FOG assessment in trials without stopping, the agreement of our model was strong (ICC (%TF) = 0.92 [0.68, 0.98]; ICC(#FOG) = 0.95 [0.72, 0.99]). Models trained on a specific FOG-provoking task could not generalize to unseen tasks, while models trained on a specific medication state could generalize to unseen states. For assessment in trials with stopping, the agreement of our model was moderately strong (ICC (%TF) = 0.95 [0.73, 0.99]; ICC (#FOG) = 0.79 [0.46, 0.94]), but only when stopping was included in the training data. CONCLUSION A TCN trained on IMU signals allows valid FOG assessment in trials with/without stops containing different medication states and FOG-provoking tasks. These results are encouraging and enable future work investigating automated FOG assessment during everyday life.
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Affiliation(s)
- Po-Kai Yang
- eMedia Research Lab/STADIUS, Department of Electrical Engineering (ESAT), KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium.
- Intelligent Mobile Platforms Research Group, Department of Mechanical Engineering, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium.
| | - Benjamin Filtjens
- eMedia Research Lab/STADIUS, Department of Electrical Engineering (ESAT), KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
- Intelligent Mobile Platforms Research Group, Department of Mechanical Engineering, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
| | - Pieter Ginis
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Maaike Goris
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Alice Nieuwboer
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Moran Gilat
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Peter Slaets
- Intelligent Mobile Platforms Research Group, Department of Mechanical Engineering, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
| | - Bart Vanrumste
- eMedia Research Lab/STADIUS, Department of Electrical Engineering (ESAT), KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Klaver EC, Heijink IB, Silvestri G, van Vugt JPP, Janssen S, Nonnekes J, van Wezel RJA, Tjepkema-Cloostermans MC. Comparison of state-of-the-art deep learning architectures for detection of freezing of gait in Parkinson's disease. Front Neurol 2023; 14:1306129. [PMID: 38178885 PMCID: PMC10764416 DOI: 10.3389/fneur.2023.1306129] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 11/21/2023] [Indexed: 01/06/2024] Open
Abstract
Introduction Freezing of gait (FOG) is one of the most debilitating motor symptoms experienced by patients with Parkinson's disease (PD). FOG detection is possible using acceleration data from wearable sensors, and a convolutional neural network (CNN) is often used to determine the presence of FOG epochs. We compared the performance of a standard CNN for the detection of FOG with two more complex networks, which are well suited for time series data, the MiniRocket and the InceptionTime. Methods We combined acceleration data of people with PD across four studies. The final data set was split into a training (80%) and hold-out test (20%) set. A fifth study was included as an unseen test set. The data were windowed (2 s) and five-fold cross-validation was applied. The CNN, MiniRocket, and InceptionTime models were evaluated using a receiver operating characteristic (ROC) curve and its area under the curve (AUC). Multiple sensor configurations were evaluated for the best model. The geometric mean was subsequently calculated to select the optimal threshold. The selected model and threshold were evaluated on the hold-out and unseen test set. Results A total of 70 participants (23.7 h, 9% FOG) were included in this study for training and testing, and in addition, 10 participants provided an unseen test set (2.4 h, 11% FOG). The CNN performed best (AUC = 0.86) in comparison to the InceptionTime (AUC = 0.82) and MiniRocket (AUC = 0.76) models. For the CNN, we found a similar performance for a seven-sensor configuration (lumbar, upper and lower legs and feet; AUC = 0.86), six-sensor configuration (upper and lower legs and feet; AUC = 0.87), and two-sensor configuration (lower legs; AUC = 0.86). The optimal threshold of 0.45 resulted in a sensitivity of 77% and a specificity of 58% for the hold-out set (AUC = 0.72), and a sensitivity of 85% and a specificity of 68% for the unseen test set (AUC = 0.90). Conclusion We confirmed that deep learning can be used to detect FOG in a large, heterogeneous dataset. The CNN model outperformed more complex networks. This model could be employed in future personalized interventions, with the ultimate goal of using automated FOG detection to trigger real-time cues to alleviate FOG in daily life.
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Affiliation(s)
- Emilie Charlotte Klaver
- Department of Neurology and Clinical Neurophysiology, Medical Spectrum Twente, Enschede, Netherlands
- Department of Neurobiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Irene B. Heijink
- Department of Neurology and Clinical Neurophysiology, Medical Spectrum Twente, Enschede, Netherlands
| | - Gianluigi Silvestri
- Department of Neurobiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
- OnePlanet Research Center imec-the Netherlands, Wageningen, Netherlands
| | - Jeroen P. P. van Vugt
- Department of Neurology and Clinical Neurophysiology, Medical Spectrum Twente, Enschede, Netherlands
| | - Sabine Janssen
- Department of Rehabilitation, Centre of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
- Department of Biomedical Signals and Systems, MedTech Centre, University of Twente, Enschede, Netherlands
- Department of Neurology, Anna Hospital, Geldrop, Netherlands
| | - Jorik Nonnekes
- Department of Rehabilitation, Centre of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
- Department of Rehabilitation, Sint Maartenskliniek, Nijmegen, Netherlands
| | - Richard J. A. van Wezel
- Department of Neurobiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
- Department of Biomedical Signals and Systems, MedTech Centre, University of Twente, Enschede, Netherlands
| | - Marleen C. Tjepkema-Cloostermans
- Department of Neurology and Clinical Neurophysiology, Medical Spectrum Twente, Enschede, Netherlands
- Department of Clinical Neurophysiology, MedTech Centre, University of Twente, Enschede, Netherlands
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Alfalahi H, Shehhi AA, Lamprou C, Ziogas I, Ganiti-Roumeliotou E, Khandoker AH, Hadjileontiadis LJ. Parkinsonian Tremor Detection with Compact Convolutional Transformer from Bispectrum Representation of tri-Axial Accelerometer Signals. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-5. [PMID: 38083408 DOI: 10.1109/embc40787.2023.10340646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
After the breakthroughs of Transformer networks in Natural Language Processing (NLP) tasks, they have led to exciting progress in visual tasks as well. Nonetheless, there has been a parallel growth in the number of parameters and the amount of training data, which led to the conclusion that Transformers are not suited for small datasets. This paper is the first to convey the feasibility of Compact Convolutional Transformers (CCT) for the prediction of Parkinsonian postural tremor based on the Bispectrum (BS) representation of IMU accelerometer time series. The dataset includes tri-axial accelerometer signals collected unobtrusively in-the-wild while subjects are on a phone call, and labelled by neurologists and signal processing experts. The BS is a noise-immune, higher-order representation that reflects a signal's deviation from Gaussianity and measures quadratic phase coupling. We performed comparative classification experiments using the CCT, pre-trained CNNs such as VGG-16 and ResNet-50, and the conventional Vision Transformer (ViT). Our model achieves competitive prediction accuracy and F1 score of 96% with only 1.016 M trainable parameters, compared to the ViT with 21.659 M trainable parameters, in a five-fold cross-validation scheme. Our model also outperforms pre-trained CNNs such as VGG-16 and ResNet-50. Furthermore, we show that the performance gains are maintained when training on a larger dataset of BS images. Our effort here is motivated by the hypothesis that data-efficient transformers outperform transfer learning using pre-trained CNNs, paving the way for promising deep learning architecture for small-scale, novel and noisy medical imaging datasets.Clinical relevance- Novel deep learning model for unobtrusive prediction of Parkinsonian Postural Tremor from Bispectrum image representation of tri-axial accelerometer signals collected in-the-wild.
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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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Young F, Mason R, Morris RE, Stuart S, Godfrey A. IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring. Sensors (Basel) 2023; 23:4100. [PMID: 37112441 PMCID: PMC10144082 DOI: 10.3390/s23084100] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 06/19/2023]
Abstract
Walking/gait quality is a useful clinical tool to assess general health and is now broadly described as the sixth vital sign. This has been mediated by advances in sensing technology, including instrumented walkways and three-dimensional motion capture. However, it is wearable technology innovation that has spawned the highest growth in instrumented gait assessment due to the capabilities for monitoring within and beyond the laboratory. Specifically, instrumented gait assessment with wearable inertial measurement units (IMUs) has provided more readily deployable devices for use in any environment. Contemporary IMU-based gait assessment research has shown evidence of the robust quantifying of important clinical gait outcomes in, e.g., neurological disorders to gather more insightful habitual data in the home and community, given the relatively low cost and portability of IMUs. The aim of this narrative review is to describe the ongoing research regarding the need to move gait assessment out of bespoke settings into habitual environments and to consider the shortcomings and inefficiencies that are common within the field. Accordingly, we broadly explore how the Internet of Things (IoT) could better enable routine gait assessment beyond bespoke settings. As IMU-based wearables and algorithms mature in their corroboration with alternate technologies, such as computer vision, edge computing, and pose estimation, the role of IoT communication will enable new opportunities for remote gait assessment.
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Affiliation(s)
- Fraser Young
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rachel Mason
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rosie E. Morris
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Samuel Stuart
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
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Alfalahi H, Dias SB, Khandoker AH, Chaudhuri KR, Hadjileontiadis LJ. A scoping review of neurodegenerative manifestations in explainable digital phenotyping. NPJ Parkinsons Dis 2023; 9:49. [PMID: 36997573 PMCID: PMC10063633 DOI: 10.1038/s41531-023-00494-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 03/16/2023] [Indexed: 04/03/2023] Open
Abstract
Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.
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Affiliation(s)
- Hessa Alfalahi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- CIPER, Faculdade de Motricidade Humana, University of Lisbon, Lisbon, Portugal
| | - Ahsan H Khandoker
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Kallol Ray Chaudhuri
- Parkinson Foundation, International Center of Excellence, King's College London, Denmark Hills, London, UK
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Gourrame K, Griškevičius J, Haritopoulos M, Lukšys D, Jatužis D, Kaladytė-Lokominienė R, Bunevičiūtė R, Mickutė G. Parkinson's disease classification with CWNN: Using wavelet transformations and IMU data fusion for improved accuracy. Technol Health Care 2023; 31:2447-2455. [PMID: 37955069 DOI: 10.3233/thc-235010] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2023]
Abstract
BACKGROUND Parkinson's disease (PD) is a chronic neurodegenerative disorder characterized by motor impairments and various other symptoms. Early and accurate classification of PD patients is crucial for timely intervention and personalized treatment. Inertial measurement units (IMUs) have emerged as a promising tool for gathering movement data and aiding in PD classification. OBJECTIVE This paper proposes a Convolutional Wavelet Neural Network (CWNN) approach for PD classification using IMU data. CWNNs have emerged as effective models for sensor data classification. The objective is to determine the optimal combination of wavelet transform and IMU data type that yields the highest classification accuracy for PD. METHODS The proposed CWNN architecture integrates convolutional neural networks and wavelet neural networks to capture spatial and temporal dependencies in IMU data. Different wavelet functions, such as Morlet, Mexican Hat, and Gaussian, are employed in the continuous wavelet transform (CWT) step. The CWNN is trained and evaluated using various combinations of accelerometer data, gyroscope data, and fusion data. RESULTS Extensive experiments are conducted using a comprehensive dataset of IMU data collected from individuals with and without PD. The performance of the proposed CWNN is evaluated in terms of classification accuracy, precision, recall, and F1-score. The results demonstrate the impact of different wavelet functions and IMU data types on PD classification performance, revealing that the combination of Morlet wavelet function and IMU data fusion achieves the highest accuracy. CONCLUSION The findings highlight the significance of combining CWT with IMU data fusion for PD classification using CWNNs. The integration of CWT-based feature extraction and the fusion of IMU data from multiple sensors enhance the representation of PD-related patterns, leading to improved classification accuracy. This research provides valuable insights into the potential of CWT and IMU data fusion for advancing PD classification models, enabling more accurate and reliable diagnosis.
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Affiliation(s)
| | - Julius Griškevičius
- Department of Biomechanical Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania
| | | | - Donatas Lukšys
- Department of Biomechanical Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania
| | - Dalius Jatužis
- Clinics of Neurology and Neurosurgery, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Santaros Klinikos Hospital, Vilnius University, Vilnius, Lithuania
| | - Rūta Kaladytė-Lokominienė
- Clinics of Neurology and Neurosurgery, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Santaros Klinikos Hospital, Vilnius University, Vilnius, Lithuania
| | - Ramunė Bunevičiūtė
- Clinics of Neurology and Neurosurgery, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Santaros Klinikos Hospital, Vilnius University, Vilnius, Lithuania
| | - Gabrielė Mickutė
- Centre of Rehabilitation, Physical and Sports Medicine, Santaros Klinikos Hospital, Vilnius University, Vilnius, Lithuania
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Di Libero T, Langiano E, Carissimo C, Ferrara M, Diotaiuti P, Rodio A. Technological support for people with Parkinson’s disease: a narrative review. JGG 2022. [DOI: 10.36150/2499-6564-n523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Rana A, Dumka A, Singh R, Panda MK, Priyadarshi N. A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives. Diagnostics (Basel) 2022; 12:2708. [PMID: 36359550 PMCID: PMC9689408 DOI: 10.3390/diagnostics12112708] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 10/12/2022] [Revised: 10/30/2022] [Accepted: 11/02/2022] [Indexed: 08/03/2023] Open
Abstract
According to the World Health Organization (WHO), Parkinson's disease (PD) is a neurodegenerative disease of the brain that causes motor symptoms including slower movement, rigidity, tremor, and imbalance in addition to other problems like Alzheimer's disease (AD), psychiatric problems, insomnia, anxiety, and sensory abnormalities. Techniques including artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been established for the classification of PD and normal controls (NC) with similar therapeutic appearances in order to address these problems and improve the diagnostic procedure for PD. In this article, we examine a literature survey of research articles published up to September 2022 in order to present an in-depth analysis of the use of datasets, various modalities, experimental setups, and architectures that have been applied in the diagnosis of subjective disease. This analysis includes a total of 217 research publications with a list of the various datasets, methodologies, and features. These findings suggest that ML/DL methods and novel biomarkers hold promising results for application in medical decision-making, leading to a more methodical and thorough detection of PD. Finally, we highlight the challenges and provide appropriate recommendations on selecting approaches that might be used for subgrouping and connection analysis with structural magnetic resonance imaging (sMRI), DaTSCAN, and single-photon emission computerized tomography (SPECT) data for future Parkinson's research.
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Affiliation(s)
- Arti Rana
- Computer Science & Engineering, Veer Madho Singh Bhandari Uttarakhand Technical University, Dehradun 248007, Uttarakhand, India
| | - Ankur Dumka
- Department of Computer Science and Engineering, Women Institute of Technology, Dehradun 248007, Uttarakhand, India
- Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun 248001, Uttarakhand, India
| | - Rajesh Singh
- Division of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, Uttarakhand, India
- Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
| | - Manoj Kumar Panda
- Department of Electrical Engineering, G.B. Pant Institute of Engineering and Technology, Pauri 246194, Uttarakhand, India
| | - Neeraj Priyadarshi
- Department of Electrical Engineering, JIS College of Engineering, Kolkata 741235, West Bengal, India
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13
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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] [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: 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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14
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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: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [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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15
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Khanna NN, Maindarkar M, Saxena A, Ahluwalia P, Paul S, Srivastava SK, Cuadrado-Godia E, Sharma A, Omerzu T, Saba L, Mavrogeni S, Turk M, Laird JR, Kitas GD, Fatemi M, Barqawi AB, Miner M, Singh IM, Johri A, Kalra MM, Agarwal V, Paraskevas KI, Teji JS, Fouda MM, Pareek G, Suri JS. Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction-A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review. Diagnostics (Basel) 2022; 12:1249. [PMID: 35626404 PMCID: PMC9141739 DOI: 10.3390/diagnostics12051249] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/14/2022] [Accepted: 05/15/2022] [Indexed: 12/12/2022] Open
Abstract
PURPOSE The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. METHODS Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. SUMMARY We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.
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Affiliation(s)
- Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - Mahesh Maindarkar
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India; (M.M.); (S.P.)
- Stroke Monitoring and Diagnostic Division, AtheroPoint, Roseville, CA 95661, USA;
| | - Ajit Saxena
- Department of Urology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - Puneet Ahluwalia
- Max Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, India;
| | - Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India; (M.M.); (S.P.)
| | - Saurabh K. Srivastava
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad 244001, India;
| | - Elisa Cuadrado-Godia
- Department of Neurology, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22908, USA;
| | - Tomaz Omerzu
- Department of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia; (T.O.); (M.T.)
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09124 Cagliari, Italy;
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Centre, 176 74 Athens, Greece;
| | - Monika Turk
- Department of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia; (T.O.); (M.T.)
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, NY 55905, USA;
| | - Al Baha Barqawi
- Division of Urology, Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA;
| | - Martin Miner
- Men’s Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA;
| | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint, Roseville, CA 95661, USA;
| | - Amer Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | | | - Vikas Agarwal
- Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India;
| | - Kosmas I. Paraskevas
- Department of Vascular Surgery, Central Clinic of Athens, 106 80 Athens, Greece;
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA;
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint, Roseville, CA 95661, USA;
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Tanveer M, Rashid A, Kumar R, Balasubramanian R. Parkinson’s disease diagnosis using neural networks: Survey and comprehensive evaluation. Inf Process Manag 2022; 59:102909. [DOI: 10.1016/j.ipm.2022.102909] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Silverio AA, Silverio LAA. Developments in Deep Brain Stimulators for Successful Aging Towards Smart Devices—An Overview. Front Aging 2022; 3:848219. [PMID: 35821845 PMCID: PMC9261350 DOI: 10.3389/fragi.2022.848219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 03/15/2022] [Indexed: 12/02/2022]
Abstract
This work provides an overview of the present state-of-the-art in the development of deep brain Deep Brain Stimulation (DBS) and how such devices alleviate motor and cognitive disorders for a successful aging. This work reviews chronic diseases that are addressable via DBS, reporting also the treatment efficacies. The underlying mechanism for DBS is also reported. A discussion on hardware developments focusing on DBS control paradigms is included specifically the open- and closed-loop “smart” control implementations. Furthermore, developments towards a “smart” DBS, while considering the design challenges, current state of the art, and constraints, are also presented. This work also showcased different methods, using ambient energy scavenging, that offer alternative solutions to prolong the battery life of the DBS device. These are geared towards a low maintenance, semi-autonomous, and less disruptive device to be used by the elderly patient suffering from motor and cognitive disorders.
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Affiliation(s)
- Angelito A. Silverio
- Department of Electronics Engineering, University of Santo Tomas, Manila, Philippines
- Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines
- *Correspondence: Angelito A. Silverio,
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El Marhraoui Y, Amroun H, Boukallel M, Anastassova M, Lamy S, Bouilland S, Ammi M. Foot-to-Ground Phases Detection: A Comparison of Data Representation Formatting Methods with Respect to Adaption of Deep Learning Architectures. Computers 2022; 11:58. [DOI: 10.3390/computers11050058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Identifying the foot stance and foot swing phases, also known as foot-to-ground (FTG) detection, is a branch of Human Activity Recognition (HAR). Our study aims to detect two main phases of the gait (i.e., foot-off and foot-contact) corresponding to the moments when each foot is in contact with the ground or not. This will allow the medical professionals to characterize and identify the different phases of the human gait and their respective patterns. This detection process is paramount for extracting gait features (e.g., step width, stride width, gait speed, cadence, etc.) used by medical experts to highlight gait anomalies, stance issues, or any other walking irregularities. It will be used to assist health practitioners with patient monitoring, in addition to developing a full pipeline for FTG detection that would help compute gait indicators. In this paper, a comparison of different training configurations, including model architectures, data formatting, and pre-processing, was conducted to select the parameters leading to the highest detection accuracy. This binary classification provides a label for each timestamp informing whether the foot is in contact with the ground or not. Models such as CNN, LSTM, and ConvLSTM were the best fits for this study. Yet, we did not exclude DNNs and Machine Learning models, such as Random Forest and XGBoost from our work in order to have a wide range of possible comparisons. As a result of our experiments, which included 27 senior participants who had a stroke in the past wearing IMU sensors on their ankles, the ConvLSTM model achieved a high accuracy of 97.01% for raw windowed data with a size of 3 frames per window, and each window was formatted to have two superimposed channels (accelerometer and gyroscope channels). The model was trained to have the best detection without any knowledge of the participants’ personal information including age, gender, health condition, the type of activity, or the used foot. In other words, the model’s input data only originated from IMU sensors. Overall, in terms of FTG detection, the combination of the ConvLSTM model and the data representation had an important impact in outperforming other start-of-the-art configurations; in addition, the compromise between the model’s complexity and its accuracy is a major asset for deploying this model and developing real-time solutions.
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Suri JS, Paul S, Maindarkar MA, Puvvula A, Saxena S, Saba L, Turk M, Laird JR, Khanna NN, Viskovic K, Singh IM, Kalra M, Krishnan PR, Johri A, Paraskevas KI. Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review. Metabolites 2022; 12:metabo12040312. [PMID: 35448500 PMCID: PMC9033076 DOI: 10.3390/metabo12040312] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 12/20/2022] Open
Abstract
Parkinson’s disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, due to a lack of sample size, comorbidity, insufficient validation, clinical examination, and a lack of big data configuration, there have been no well-explained bias-free AI investigations to establish the CVD/Stroke risk stratification in the PD framework. The study has two objectives: (i) to establish a solid link between PD and CVD/stroke; and (ii) to use the AI paradigm to examine a well-defined CVD/stroke risk stratification in the PD framework. The PRISMA search strategy selected 223 studies for CVD/stroke risk, of which 54 and 44 studies were related to the link between PD-CVD, and PD-stroke, respectively, 59 studies for joint PD-CVD-Stroke framework, and 66 studies were only for the early PD diagnosis without CVD/stroke link. Sequential biological links were used for establishing the hypothesis. For AI design, PD risk factors as covariates along with CVD/stroke as the gold standard were used for predicting the CVD/stroke risk. The most fundamental cause of CVD/stroke damage due to PD is cardiac autonomic dysfunction due to neurodegeneration that leads to heart failure and its edema, and this validated our hypothesis. Finally, we present the novel AI solutions for CVD/stroke risk prediction in the PD framework. The study also recommends strategies for removing the bias in AI for CVD/stroke risk prediction using the PD framework.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA; (A.P.); (I.M.S.)
- Correspondence: ; Tel.: +1-(916)-749-5628
| | - Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India; (S.P.); (M.A.M.)
| | - Maheshrao A. Maindarkar
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India; (S.P.); (M.A.M.)
| | - Anudeep Puvvula
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA; (A.P.); (I.M.S.)
- Annu’s Hospitals for Skin & Diabetes, Gudur 524101, India
| | - Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhuneshwar 751003, India;
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09121 Cagliari, Italy;
| | - Monika Turk
- Deparment of Neurology, University Medical Centre Maribor, 1262 Maribor, Slovenia;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India;
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia;
| | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA; (A.P.); (I.M.S.)
| | - Mannudeep Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA;
| | | | - Amer Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Kosmas I. Paraskevas
- Department of Vascular Surgery, Central Clinic of Athens, 106 80 Athens, Greece;
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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: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Abstract
Freezing of gait (FOG) is a common and challenging clinical symptom in Parkinson’s disease. In this review, we summarise the recent insights into freezing of gait and highlight the strategies that should be considered to improve future treatment. There is a need to develop individualised and on-demand therapies, through improved detection and wearable technologies. Whilst there already exist a number of pharmacological (e.g., dopaminergic and beyond dopamine), non-pharmacological (physiotherapy and cueing, cognitive training, and non-invasive brain stimulation) and surgical approaches to freezing (i.e., dual-site deep brain stimulation, closed-loop programming), an integrated collaborative approach to future research in this complex area will be necessary to systematically investigate new therapeutic avenues. A review of the literature suggests standardising how gait freezing is measured, enriching patient cohorts for preventative studies, and harnessing the power of existing data, could help lead to more effective treatments for freezing of gait and offer relief to many patients.
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Affiliation(s)
- Cathy K Cui
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences, The University of Sydney, Camperdown, NSW, Australia
| | - Simon J G Lewis
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences, The University of Sydney, Camperdown, NSW, Australia
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