1
|
Lee RE, Chan PY. Explainable artificial intelligence for searching frequency characteristics in Parkinson's disease tremor. Sci Rep 2023; 13:18622. [PMID: 37903843 PMCID: PMC10616175 DOI: 10.1038/s41598-023-45802-z] [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: 04/11/2023] [Accepted: 10/24/2023] [Indexed: 11/01/2023] Open
Abstract
The distinction between Parkinson's disease (PD) and essential tremor (ET) tremors is subtle, posing challenges in differentiation. To accurately classify the PD and ET, BiLSTM-based recurrent neural networks are employed to classify between normal patients (N), PD patients, and ET patients using accelerometry data on their lower arm (L), hand (H), and upper arm (U) as inputs. The trained recurrent neural network (RNN) has reached 80% accuracy. The neural network is analyzed using layer-wise relevance propagation (LRP) to understand the internal workings of the neural network. A novel explainable AI method, called LRP-based approximate linear weights (ALW), is introduced to identify the similarities in relevance when assigning the class scores in the neural network. The ALW functions as a 2D kernel that linearly transforms the input data directly into the class scores, which significantly reduces the complexity of analyzing the neural network. This new classification method reconstructs the neural network's original function, achieving a 73% PD and ET tremor classification accuracy. By analyzing the ALWs, the correlation between each input and the class can also be determined. Then, the differentiating features can be subsequently identified. Since the input is preprocessed using short-time Fourier transform (STFT), the differences between the magnitude of tremor frequencies ranging from 3 to 30 Hz in the mean N, PD, and ET subjects are successfully identified. Aside from matching the current medical knowledge on frequency content in the tremors, the differentiating features also provide insights about frequency contents in the tremors in other frequency bands and body parts.
Collapse
Affiliation(s)
- Rui En Lee
- School of Engineering, Monash University Malaysia, Bandar Sunway, Subang Jaya, Selangor, Malaysia
| | - Ping Yi Chan
- School of Engineering, Monash University Malaysia, Bandar Sunway, Subang Jaya, Selangor, Malaysia.
| |
Collapse
|
2
|
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] [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.
Collapse
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
| |
Collapse
|
3
|
Farhani G, Zhou Y, Jenkins ME, Naish MD, Trejos AL. Using Deep Learning for Task and Tremor Type Classification in People with Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2022; 22:7322. [PMID: 36236422 PMCID: PMC9570986 DOI: 10.3390/s22197322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
Hand tremor is one of the dominating symptoms of Parkinson's disease (PD), which significantly limits activities of daily living. Along with medications, wearable devices have been proposed to suppress tremor. However, suppressing tremor without interfering with voluntary motion remains challenging and improvements are needed. The main goal of this work was to design algorithms for the automatic identification of the tremor type and voluntary motions, using only surface electromyography (sEMG) data. Towards this goal, a bidirectional long short-term memory (BiLSTM) algorithm was implemented that uses sEMG data to identify the motion and tremor type of people living with PD when performing a task. Moreover, in order to automate the training process, hyperparamter selection was performed using a regularized evolutionary algorithm. The results show that the accuracy of task classification among 15 people living with PD was 84±8%, and the accuracy of tremor classification was 88±5%. Both models performed significantly above chance levels (20% and 33% for task and tremor classification, respectively). Thus, it was concluded that the trained models, based on using purely sEMG signals, could successfully identify the task and tremor types.
Collapse
Affiliation(s)
- Ghazal Farhani
- Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada
| | - Yue Zhou
- School of Biomedical Engineering, Western University, London, ON N6A 5B9, Canada
| | - Mary E. Jenkins
- Movement Disorders Program, Clinical Neurological Sciences, Western University, London, ON N6A 3K7, Canada
| | - Michael D. Naish
- Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada
- School of Biomedical Engineering, Western University, London, ON N6A 5B9, Canada
- Department of Mechanical and Materials Engineering, Western University, London, ON N6A 5B9, Canada
| | - Ana Luisa Trejos
- Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada
- School of Biomedical Engineering, Western University, London, ON N6A 5B9, Canada
| |
Collapse
|
4
|
Parkinson’s disease diagnosis using neural networks: Survey and comprehensive evaluation. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102909] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
|
5
|
Ibrahim A, Zhou Y, Jenkins ME, Trejos AL, Naish MD. Real-Time Voluntary Motion Prediction and Parkinson's Tremor Reduction Using Deep Neural Networks. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1413-1423. [PMID: 34255631 DOI: 10.1109/tnsre.2021.3097007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Wearable tremor suppression devices (WTSD) have been considered as a viable solution to manage parkinsonian tremor. WTSDs showed their ability to improve the quality of life of individuals suffering from parkinsonian tremor, by helping them to perform activities of daily living (ADL). Since parkinsonian tremor has been shown to be nonstationary, nonlinear, and stochastic in nature, the performance of the tremor models used by WTSDs is affected by their inability to adapt to the nonlinear behaviour of tremor. Another drawback that the models have is their limitation to estimate or predict one step ahead, which introduces delay when used in real time with WTSDs, which compromises performance. To address these issues, this work proposes a deep neural network model that learns the correlations and nonlinearities of tremor and voluntary motion, and is capable of multi-step prediction with minimal delay. A generalized model that is task and user-independent is presented. The model achieved an average estimation percentage accuracy of 99.2%. The average future voluntary motion prediction percentage accuracy with 10, 20, 50, and 100 steps ahead was 97.0%, 94.0%, 91.6%, and 89.9%, respectively, with prediction time as low as 1.5 ms for 100 steps ahead. The proposed model also achieved an average of 93.8% ± 1.5% in tremor reduction when it was tested in an experimental setup in real time. The tremor reduction showed an improvement of 25% over the Weighted Fourier Linear Combiner (WFLC), an estimator commonly used with WTSDs.
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Okagbue HI, Oguntunde PE, Obasi ECM, Adamu PI, Opanuga AA. Diagnosing malaria from some symptoms: a machine learning approach and public health implications. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-020-00488-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
8
|
Atashzar SF, Huang HY, Duca FD, Burdet E, Farina D. Energetic Passivity Decoding of Human Hip Joint for Physical Human-Robot Interaction. IEEE Robot Autom Lett 2020; 5:5953-5960. [DOI: 10.1109/lra.2020.3010459] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
|