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Cho CH, Huang PJ, Chen MC, Lin CW. Closed-Loop Deep Brain Stimulation With Reinforcement Learning and Neural Simulation. IEEE Trans Neural Syst Rehabil Eng 2024; 32:3615-3624. [PMID: 39302783 DOI: 10.1109/tnsre.2024.3465243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2024]
Abstract
Deep Brain Stimulation (DBS) is effective for movement disorders, particularly Parkinson's disease (PD). However, a closed-loop DBS system using reinforcement learning (RL) for automatic parameter tuning, offering enhanced energy efficiency and the effect of thalamus restoration, is yet to be developed for clinical and commercial applications. In this research, we instantiate a basal ganglia-thalamic (BGT) model and design it as an interactive environment suitable for RL models. Four finely tuned RL agents based on different frameworks, namely Soft Actor-Critic (SAC), Twin Delayed Deep Deterministic Policy Gradient (TD3), Proximal Policy Optimization (PPO), and Advantage Actor-Critic (A2C), are established for further comparison. Within the implemented RL architectures, the optimized TD3 demonstrates a significant 67% reduction in average power dissipation when compared to the open-loop system while preserving the normal response of the simulated BGT circuitry. As a result, our method mitigates thalamic error responses under pathological conditions and prevents overstimulation. In summary, this study introduces a novel approach to implementing an adaptive parameter-tuning closed-loop DBS system. Leveraging the advantages of TD3, our proposed approach holds significant promise for advancing the integration of RL applications into DBS systems, ultimately optimizing therapeutic effects in future clinical trials.
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Jaramillo-Jimenez A, Tovar-Rios DA, Mantilla-Ramos YJ, Ochoa-Gomez JF, Bonanni L, Brønnick K. ComBat models for harmonization of resting-state EEG features in multisite studies. Clin Neurophysiol 2024; 167:241-253. [PMID: 39369552 DOI: 10.1016/j.clinph.2024.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 07/23/2024] [Accepted: 09/09/2024] [Indexed: 10/08/2024]
Abstract
OBJECTIVE Pooling multisite resting-state electroencephalography (rsEEG) datasets may introduce bias due to batch effects (i.e., cross-site differences in the rsEEG related to scanner/sample characteristics). The Combining Batches (ComBat) models, introduced for microarray expression and adapted for neuroimaging, can control for batch effects while preserving the variability of biological covariates. We aim to evaluate four ComBat harmonization methods in a pooled sample from five independent rsEEG datasets of young and old adults. METHODS RsEEG signals (n = 374) were automatically preprocessed. Oscillatory and aperiodic rsEEG features were extracted in sensor space. Features were harmonized using neuroCombat (standard ComBat used in neuroimaging), neuroHarmonize (variant with nonlinear adjustment of covariates), OPNested-GMM (variant based on Gaussian Mixture Models to fit bimodal feature distributions), and HarmonizR (variant based on resampling to handle missing feature values). Relationships between rsEEG features and age were explored before and after harmonizing batch effects. RESULTS Batch effects were identified in rsEEG features. All ComBat methods reduced batch effects and features' dispersion; HarmonizR and OPNested-GMM ComBat achieved the greatest performance. Harmonized Beta power, individual Alpha peak frequency, Aperiodic exponent, and offset in posterior electrodes showed significant relations with age. All ComBat models maintained the direction of observed relationships while increasing the effect size. CONCLUSIONS ComBat models, particularly HarmonizeR and OPNested-GMM ComBat, effectively control for batch effects in rsEEG spectral features. SIGNIFICANCE This workflow can be used in multisite studies to harmonize batch effects in sensor-space rsEEG spectral features while preserving biological associations.
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Affiliation(s)
- Alberto Jaramillo-Jimenez
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital, Stavanger, Norway; Faculty of Health Sciences, University of Stavanger, Stavanger, Norway; Grupo de Neurociencias de Antioquia, Universidad de Antioquia, Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, Medellín, Colombia.
| | - Diego A Tovar-Rios
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital, Stavanger, Norway; Doctoral School Biomedical Sciences, KU Leuven, Leuven, Belgium; Grupo de Investigación en Estadística Aplicada - INFERIR, Universidad del Valle, Cali, Colombia; Prevención y Control de la Enfermedad Crónica - PRECEC, Universidad del Valle, Colombia.
| | - Yorguin-Jose Mantilla-Ramos
- Grupo Neuropsicología y Conducta, Universidad de Antioquia. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, Medellín, Colombia; Cognitive and Computational Neuroscience Laboratory (CoCo Lab), University of Montreal, Montreal, Canada.
| | - John-Fredy Ochoa-Gomez
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia. Medellín, Colombia.
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, G. d'Annunzio University, Chieti, Italy.
| | - Kolbjørn Brønnick
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital, Stavanger, Norway; Faculty of Social Sciences, University of Stavanger, Stavanger, Norway.
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Cai T, Zhao G, Zang J, Zong C, Zhang Z, Xue C. Quantifying instability in neurological disorders EEG based on phase space DTM function. Comput Biol Med 2024; 180:108951. [PMID: 39094326 DOI: 10.1016/j.compbiomed.2024.108951] [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: 03/31/2024] [Revised: 07/25/2024] [Accepted: 07/26/2024] [Indexed: 08/04/2024]
Abstract
Classifying individuals with neurological disorders and healthy subjects using EEG is a crucial area of research. The current feature extraction approach focuses on the frequency domain features in each of the EEG frequency bands and functional brain networks. In recent years, researchers have discovered and extensively studied stability differences in the electroencephalograms (EEG) of patients with neurological disorders. Based on this, this paper proposes a feature descriptor to characterize EEG instability. The proposed method starts by forming a signal point cloud through Phase Space Reconstruction (PSR). Subsequently, a pseudo-metric space is constructed, and pseudo-distances are calculated based on the consistent measure of the point cloud. Finally, Distance to Measure (DTM) Function are generated to replace the distance function in the original metric space. We calculated the relative distances in the point cloud by measuring signal similarity and, based on this, summarized the point cloud structures formed by EEG with different stabilities after PSR. This process demonstrated that Multivariate Kernel Density Estimation (MKDE) based on a Gaussian kernel can effectively separate the mappings of different stable components within the signal in the phase space. The two average DTM values are then proposed as feature descriptors for EEG instability.In the validation phase, the proposed feature descriptor is tested on three typical neurological disorders: epilepsy, Alzheimer's disease, and Parkinson's disease, using the Bonn dataset, CHB-MIT, the Florida State University dataset, and the Iowa State University dataset. DTM values are used as feature inputs for four different machine learning classifiers, and The results show that the best classification accuracy of the proposed method reaches 98.00 %, 96.25 %, 96.71 % and 95.34 % respectively, outperforming commonly used nonlinear descriptors. Finally, the proposed method is tested and analyzed using noisy signals, demonstrating its robustness compared to other methods.
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Affiliation(s)
- Tianming Cai
- Shanxi College of Technology, No.11 Changning Street, Development Zone, Shuozhou, Shanxi, 036000, China; North University of China, School of Instrument and Electronics, No.3 College Road, Jiancaoping District, Taiyuan, Shanxi, 030051, China
| | - Guoying Zhao
- Shanxi College of Technology, No.11 Changning Street, Development Zone, Shuozhou, Shanxi, 036000, China; North University of China, School of Instrument and Electronics, No.3 College Road, Jiancaoping District, Taiyuan, Shanxi, 030051, China
| | - Junbin Zang
- Shanxi College of Technology, No.11 Changning Street, Development Zone, Shuozhou, Shanxi, 036000, China; North University of China, School of Instrument and Electronics, No.3 College Road, Jiancaoping District, Taiyuan, Shanxi, 030051, China.
| | - Chen Zong
- The Second Hospital of Shanxi Medical University, No.382 Wuyi Road, Taiyuan, Shanxi, 030001, China
| | - Zhidong Zhang
- North University of China, School of Instrument and Electronics, No.3 College Road, Jiancaoping District, Taiyuan, Shanxi, 030051, China
| | - Chenyang Xue
- North University of China, School of Instrument and Electronics, No.3 College Road, Jiancaoping District, Taiyuan, Shanxi, 030051, China
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Niu Y, Xiang J, Gao K, Wu J, Sun J, Wang B, Ding R, Dou M, Wen X, Cui X, Zhou M. Multi-Frequency Entropy for Quantifying Complex Dynamics and Its Application on EEG Data. ENTROPY (BASEL, SWITZERLAND) 2024; 26:728. [PMID: 39330063 PMCID: PMC11431093 DOI: 10.3390/e26090728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/24/2024] [Accepted: 07/26/2024] [Indexed: 09/28/2024]
Abstract
Multivariate entropy algorithms have proven effective in the complexity dynamic analysis of electroencephalography (EEG) signals, with researchers commonly configuring the variables as multi-channel time series. However, the complex quantification of brain dynamics from a multi-frequency perspective has not been extensively explored, despite existing evidence suggesting interactions among brain rhythms at different frequencies. In this study, we proposed a novel algorithm, termed multi-frequency entropy (mFreEn), enhancing the capabilities of existing multivariate entropy algorithms and facilitating the complexity study of interactions among brain rhythms of different frequency bands. Firstly, utilizing simulated data, we evaluated the mFreEn's sensitivity to various noise signals, frequencies, and amplitudes, investigated the effects of parameters such as the embedding dimension and data length, and analyzed its anti-noise performance. The results indicated that mFreEn demonstrated enhanced sensitivity and reduced parameter dependence compared to traditional multivariate entropy algorithms. Subsequently, the mFreEn algorithm was applied to the analysis of real EEG data. We found that mFreEn exhibited a good diagnostic performance in analyzing resting-state EEG data from various brain disorders. Furthermore, mFreEn showed a good classification performance for EEG activity induced by diverse task stimuli. Consequently, mFreEn provides another important perspective to quantify complex dynamics.
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Affiliation(s)
- Yan Niu
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China; (Y.N.); (J.X.); (K.G.); (J.S.); (B.W.); (R.D.); (M.D.); (X.C.)
| | - Jie Xiang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China; (Y.N.); (J.X.); (K.G.); (J.S.); (B.W.); (R.D.); (M.D.); (X.C.)
| | - Kai Gao
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China; (Y.N.); (J.X.); (K.G.); (J.S.); (B.W.); (R.D.); (M.D.); (X.C.)
| | - Jinglong Wu
- Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
| | - Jie Sun
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China; (Y.N.); (J.X.); (K.G.); (J.S.); (B.W.); (R.D.); (M.D.); (X.C.)
| | - Bin Wang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China; (Y.N.); (J.X.); (K.G.); (J.S.); (B.W.); (R.D.); (M.D.); (X.C.)
| | - Runan Ding
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China; (Y.N.); (J.X.); (K.G.); (J.S.); (B.W.); (R.D.); (M.D.); (X.C.)
| | - Mingliang Dou
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China; (Y.N.); (J.X.); (K.G.); (J.S.); (B.W.); (R.D.); (M.D.); (X.C.)
| | - Xin Wen
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China;
| | - Xiaohong Cui
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China; (Y.N.); (J.X.); (K.G.); (J.S.); (B.W.); (R.D.); (M.D.); (X.C.)
| | - Mengni Zhou
- Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
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Wu H, Qi J, Purwanto E, Zhu X, Yang P, Chen J. Multi-Scale Feature and Multi-Channel Selection toward Parkinson's Disease Diagnosis with EEG. SENSORS (BASEL, SWITZERLAND) 2024; 24:4634. [PMID: 39066031 PMCID: PMC11280892 DOI: 10.3390/s24144634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/10/2024] [Accepted: 07/13/2024] [Indexed: 07/28/2024]
Abstract
OBJECTIVE Motivated by Health Care 4.0, this study aims to reducing the dimensionality of traditional EEG features based on manual extracted features, including statistical features in the time and frequency domains. METHODS A total of 22 multi-scale features were extracted from the UNM and Iowa datasets using a 4th order Butterworth filter and wavelet packet transform. Based on single-channel validation, 29 channels with the highest R2 scores were selected from a pool of 59 common channels. The proposed channel selection scheme was validated on the UNM dataset and tested on the Iowa dataset to compare its generalizability against models trained without channel selection. RESULTS The experimental results demonstrate that the proposed model achieves an optimal classification accuracy of 100%. Additionally, the generalization capability of the channel selection method is validated through out-of-sample testing based on the Iowa dataset Conclusions: Using single-channel validation, we proposed a channel selection scheme based on traditional statistical features, resulting in a selection of 29 channels. This scheme significantly reduced the dimensionality of EEG feature vectors related to Parkinson's disease by 50%. Remarkably, this approach demonstrated considerable classification performance on both the UNM and Iowa datasets. For the closed-eye state, the highest classification accuracy achieved was 100%, while for the open-eye state, the highest accuracy reached 93.75%.
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Affiliation(s)
- Haoyu Wu
- Department of Computing, Xi’an Jiaotong-Liverpool Univeristy, Suzhou 215000, China; (H.W.); (E.P.); (X.Z.)
| | - Jun Qi
- Department of Computing, Xi’an Jiaotong-Liverpool Univeristy, Suzhou 215000, China; (H.W.); (E.P.); (X.Z.)
| | - Erick Purwanto
- Department of Computing, Xi’an Jiaotong-Liverpool Univeristy, Suzhou 215000, China; (H.W.); (E.P.); (X.Z.)
| | - Xiaohui Zhu
- Department of Computing, Xi’an Jiaotong-Liverpool Univeristy, Suzhou 215000, China; (H.W.); (E.P.); (X.Z.)
| | - Po Yang
- Department of Computer Science, The University of Sheffield, Sheffield S10 2TN, UK;
| | - Jianjun Chen
- Department of Computing, Xi’an Jiaotong-Liverpool Univeristy, Suzhou 215000, China; (H.W.); (E.P.); (X.Z.)
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Siuly S, Khare SK, Kabir E, Sadiq MT, Wang H. An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network. Comput Biol Med 2024; 174:108462. [PMID: 38599069 DOI: 10.1016/j.compbiomed.2024.108462] [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: 10/13/2023] [Revised: 04/07/2024] [Accepted: 04/07/2024] [Indexed: 04/12/2024]
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) signals are commonly used for early PD diagnosis due to their potential in monitoring disease progression. But traditional EEG-based methods lack exploration of brain regions that provide essential information about PD, and their performance falls short for real-time applications. To address these limitations, this study proposes a novel approach using a Time-Frequency Representation (TFR) based AlexNet Convolutional Neural Network (CNN) model to explore EEG channel-based analysis and identify critical brain regions efficiently diagnosing PD from EEG data. The Wavelet Scattering Transform (WST) is employed to capture distinct temporal and spectral characteristics, while AlexNet CNN is utilized to detect complex spatial patterns at different scales, accurately identifying intricate EEG patterns associated with PD. The experiment results on two real-time EEG PD datasets: San Diego dataset and the Iowa dataset demonstrate that frontal and central brain regions, including AF4 and AFz electrodes, contribute significantly to providing more representative features compared to other regions for PD detection. The proposed architecture achieves an impressive accuracy of 99.84% for the San Diego dataset and 95.79% for the Iowa dataset, outperforming existing EEG-based PD detection methods. The findings of this research will assist to create an essential technology for efficient PD diagnosis, enhancing patient care and quality of life.
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Affiliation(s)
- Siuly Siuly
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Australia; Centre for Health Research, University of Southern Queensland, Toowoomba, Australia.
| | - Smith K Khare
- Mærsk Mc-Kinney Møller Institute, Faculty of Engineering, University of Southern Denmark, Denmark
| | - Enamul Kabir
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia
| | - Muhammad Tariq Sadiq
- School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, United Kingdom
| | - Hua Wang
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Australia
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7
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Oh J, Nam KW, Kim WJ, Kang BH, Park SH. Flexible Dry Electrode Based on a Wrinkled Surface That Uses Carbon Nanotube/Polymer Composites for Recording Electroencephalograms. MATERIALS (BASEL, SWITZERLAND) 2024; 17:668. [PMID: 38591516 PMCID: PMC10856397 DOI: 10.3390/ma17030668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/09/2023] [Accepted: 10/18/2023] [Indexed: 04/10/2024]
Abstract
Electroencephalography (EEG) captures minute electrical signals emanating from the brain. These signals are vulnerable to interference from external noise and dynamic artifacts; hence, accurately recording such signals is challenging. Although dry electrodes are convenient, their signals are of limited quality; consequently, wet electrodes are predominantly used in EEG. Therefore, developing dry electrodes for accurately and stably recording EEG signals is crucial. In this study, we developed flexible dry electrodes using polydimethylsiloxane (PDMS)/carbon-nanotube (CNT) composites with isotropically wrinkled surfaces that effectively combine the advantages of wet and dry electrodes. Adjusting the PDMS crosslinker ratio led to good adhesion, resulting in a highly adhesive CNT/PDMS composite with a low Young's modulus that exhibited excellent electrical and mechanical properties owing to its ability to conformally contact skin. The isotropically wrinkled surface also effectively controls dynamic artifacts during EEG signal detection and ensures accurate signal analysis. The results of this study demonstrate that dry electrodes based on flexible CNT/PDMS composites and corrugated structures can outperform wet electrodes. The introduction of such electrodes is expected to enable the accurate analysis and monitoring of EEG signals in various scenarios, including clinical trials.
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Affiliation(s)
| | | | | | | | - Sung-Hoon Park
- Department of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea; (J.O.); (K.-W.N.); (W.-J.K.); (B.-H.K.)
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8
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Anjum MF, Espinoza AI, Cole RC, Singh A, May P, Uc EY, Dasgupta S, Narayanan NS. Resting-state EEG measures cognitive impairment in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:6. [PMID: 38172519 PMCID: PMC10764756 DOI: 10.1038/s41531-023-00602-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 11/14/2023] [Indexed: 01/05/2024] Open
Abstract
Cognitive dysfunction is common in Parkinson's disease (PD). We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from National Institutes of Health (NIH) Toolbox using cross-validations, regression models, and randomization tests. Finally, we externally validated our approach on 32 PD participants. We observed cognition-related changes in EEG over multiple spectral rhythms. Utilizing only 8 best-performing electrodes, our proposed index strongly correlated with cognition (MoCA: rho = 0.68, p value < 0.001; NIH-Toolbox cognitive tests: rho ≥ 0.56, p value < 0.001) outperforming traditional spectral markers (rho = -0.30-0.37). The index showed a strong fit in regression models (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Notably, our approach was equally effective (rho = 0.68, p value < 0.001; MoCA) in out-of-sample testing. In summary, we introduced a computationally efficient data-driven approach for cross-domain cognition indexing using fewer than 10 EEG electrodes, potentially compatible with dynamic therapies like closed-loop neurostimulation. These results will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.
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Affiliation(s)
- Md Fahim Anjum
- Department of Neurology, University of California San Francisco, San Francisco, CA, 94143, USA.
| | - Arturo I Espinoza
- Department of Neurology, The University of Iowa, Iowa city, IA, 52240, USA
| | - Rachel C Cole
- Department of Neurology, The University of Iowa, Iowa city, IA, 52240, USA
| | - Arun Singh
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, South Dakota, SD, 57069, USA
| | - Patrick May
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa city, IA, 52240, USA
| | - Ergun Y Uc
- Department of Neurology, The University of Iowa, Iowa city, IA, 52240, USA
- Neurology Service, Iowa City VA Medical Center, Iowa city, IA, 52240, USA
| | - Soura Dasgupta
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa city, IA, 52240, USA
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Hireš M, Drotár P, Pah ND, Ngo QC, Kumar DK. On the inter-dataset generalization of machine learning approaches to Parkinson's disease detection from voice. Int J Med Inform 2023; 179:105237. [PMID: 37801807 DOI: 10.1016/j.ijmedinf.2023.105237] [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: 03/06/2023] [Revised: 09/20/2023] [Accepted: 09/24/2023] [Indexed: 10/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Parkinson's disease is the second-most-common neurodegenerative disorder that affects motor skills, cognitive processes, mood, and everyday tasks such as speaking and walking. The voices of people with Parkinson's disease may become weak, breathy, or hoarse and may sound emotionless, with slurred words and mumbling. Algorithms for computerized voice analysis have been proposed and have shown highly accurate results. However, these algorithms were developed on single, limited datasets, with participants possessing similar demographics. Such models are prone to overfitting and are unsuitable for generalization, which is essential in real-world applications. METHODS We evaluated the computerized Parkinson's disease diagnosis performance of various machine learning models and showed that these models degraded rapidly when used on different datasets. We evaluated two mainstream state-of-the-art approaches, one based on deep convolutional neural networks and another based on voice feature extraction followed by a shallow classifier (i.e., extreme gradient boosting (XGBoost)). RESULTS An investigation with four datasets (CzechPD, PC-GITA, ITA, and RMIT-PD) proved that even if the algorithms yielded excellent performance on a single dataset, the results obtained on new data or even a mix of datasets were very unsatisfactory. CONCLUSIONS More work needs to be done to make computerized voice analysis methods for Parkinson's disease diagnosis suitable for real-world applications.
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Affiliation(s)
- Máté Hireš
- Intelligent Information Systems Lab, Technical University of Kosice, Letna 9, 42001 Kosice, Slovakia
| | - Peter Drotár
- Intelligent Information Systems Lab, Technical University of Kosice, Letna 9, 42001 Kosice, Slovakia.
| | - Nemuel Daniel Pah
- Biosignals Lab, RMIT University, Melbourne, Australia; Universitas Surabaya, Surabaya, Indonesia
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Dhanalakshmi S, Maanasaa RS, Maalikaa RS, Senthil R. A review of emergent intelligent systems for the detection of Parkinson's disease. Biomed Eng Lett 2023; 13:591-612. [PMID: 37872986 PMCID: PMC10590348 DOI: 10.1007/s13534-023-00319-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/11/2023] [Accepted: 09/07/2023] [Indexed: 10/25/2023] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder affecting people worldwide. The PD symptoms are divided into motor and non-motor symptoms. Detection of PD is very crucial and essential. Such challenges can be overcome by applying artificial intelligence to diagnose PD. Many studies have also proposed the implementation of computer-aided diagnosis for the detection of PD. This systematic review comprehensively analyzed all appropriate algorithms for detecting and assessing PD based on the literature from 2012 to 2023 which are conducted as per PRISMA model. This review focused on motor symptoms, namely handwriting dynamics, voice impairments and gait, multimodal features, and brain observation using single photon emission computed tomography, magnetic resonance and electroencephalogram signals. The significant challenges are critically analyzed, and appropriate recommendations are provided. The critical discussion of this review article can be helpful in today's PD community in such a way that it allows clinicians to provide proper treatment and timely medication.
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Affiliation(s)
- Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203 India
| | - Ramesh Sai Maanasaa
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203 India
| | - Ramesh Sai Maalikaa
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203 India
| | - Ramalingam Senthil
- Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203 India
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Shkury E, Danziger-Schragenheim S, Katzir Z, Ezra Y, Giladi N, Mirelman A, Maidan I. Differences in EEG Event-Related Potentials during Dual Task in Parkinson's Disease Carriers and Non-Carriers of the G2019S-LRRK2 Mutation. SENSORS (BASEL, SWITZERLAND) 2023; 23:8266. [PMID: 37837096 PMCID: PMC10575245 DOI: 10.3390/s23198266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/28/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND The G2019S-LRRK2 gene mutation is a common cause of hereditary Parkinson's disease (PD), associated with a higher frequency of the postural instability gait difficulty (PIGD) motor phenotype yet with preserved cognition. This study investigated neurophysiological changes during motor and cognitive tasks in PD patients with and without the G2019S-LRRK2 mutation. METHODS 33 iPD patients and 22 LRRK2-PD patients performed the visual Go/NoGo task (VGNG) during sitting (single-task) and walking (dual-task) while wearing a 64-channel EEG cap. Event-related potentials (ERP) from Fz and Pz, specifically N200 and P300, were extracted and analyzed to quantify brain activity patterns. RESULTS The LRRK2-PD group performed better in the VGNG than the iPD group (group*task; p = 0.05). During Go, the iPD group showed reduced N2 amplitude and prolonged N2 latency during walking, whereas the LRRK2-PD group showed only shorter latency (group*task p = 0.027). During NoGo, opposite patterns emerged; the iPD group showed reduced N2 and increased P3 amplitudes during walking while the LRRK2-PD group demonstrated increased N2 and reduced P3 (N2: group*task, p = 0.010, P3: group*task, p = 0.012). CONCLUSIONS The LRRK2-PD group showed efficient early cognitive processes, reflected by N2, resulting in greater neural synchronization and prominent ERPs. These processes are possibly the underlying mechanisms for the observed better cognitive performance as compared to the iPD group. As such, future applications of intelligent medical sensing should be capable of capturing these electrophysiological patterns in order to enhance motor-cognitive functions.
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Affiliation(s)
- Eden Shkury
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; (E.S.); (S.D.-S.); (Z.K.); (Y.E.); (N.G.); (A.M.)
- School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Shani Danziger-Schragenheim
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; (E.S.); (S.D.-S.); (Z.K.); (Y.E.); (N.G.); (A.M.)
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Zoya Katzir
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; (E.S.); (S.D.-S.); (Z.K.); (Y.E.); (N.G.); (A.M.)
- School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Yael Ezra
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; (E.S.); (S.D.-S.); (Z.K.); (Y.E.); (N.G.); (A.M.)
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Nir Giladi
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; (E.S.); (S.D.-S.); (Z.K.); (Y.E.); (N.G.); (A.M.)
- School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Anat Mirelman
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; (E.S.); (S.D.-S.); (Z.K.); (Y.E.); (N.G.); (A.M.)
- School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Inbal Maidan
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; (E.S.); (S.D.-S.); (Z.K.); (Y.E.); (N.G.); (A.M.)
- School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
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12
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Eyvazpour R, Navi FFT, Shakeri E, Nikzad B, Heysieattalab S. Machine learning-based classifying of risk-takers and risk-aversive individuals using resting-state EEG data: A pilot feasibility study. Brain Behav 2023; 13:e3139. [PMID: 37366037 PMCID: PMC10498077 DOI: 10.1002/brb3.3139] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 05/29/2023] [Accepted: 06/15/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Decision-making is vital in interpersonal interactions and a country's economic and political conditions. People, especially managers, have to make decisions in different risky situations. There has been a growing interest in identifying managers' personality traits (i.e., risk-taking or risk-averse) in recent years. Although there are findings of signal decision-making and brain activity, the implementation of an intelligent brain-based technique to predict risk-averse and risk-taking managers is still in doubt. METHODS This study proposes an electroencephalogram (EEG)-based intelligent system to distinguish risk-taking managers from risk-averse ones by recording the EEG signals from 30 managers. In particular, wavelet transform, a time-frequency domain analysis method, was used on resting-state EEG data to extract statistical features. Then, a two-step statistical wrapper algorithm was used to select the appropriate features. The support vector machine classifier, a supervised learning method, was used to classify two groups of managers using chosen features. RESULTS Intersubject predictive performance could classify two groups of managers with 74.42% accuracy, 76.16% sensitivity, 72.32% specificity, and 75% F1-measure, indicating that machine learning (ML) models can distinguish between risk-taking and risk-averse managers using the features extracted from the alpha frequency band in 10 s analysis window size. CONCLUSIONS The findings of this study demonstrate the potential of using intelligent (ML-based) systems in distinguish between risk-taking and risk-averse managers using biological signals.
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Affiliation(s)
- Reza Eyvazpour
- Department of Biomedical Engineering, School of Electrical EngineeringIran University of Science and Technology (IUST)TehranIran
| | | | - Elmira Shakeri
- Department of Business Management, Faculty of Management and AccountingAllameh Tabataba'i UniversityTehranIran
| | - Behzad Nikzad
- Department of Cognitive NeuroscienceUniversity of TabrizTabrizIran
- Neurobioscince DivisionResearch Center of Bioscience and Biotechnology, University of TabrizTabrizIran
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Göker H. Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model. Phys Eng Sci Med 2023; 46:1163-1174. [PMID: 37245195 DOI: 10.1007/s13246-023-01284-x] [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: 07/26/2022] [Accepted: 05/18/2023] [Indexed: 05/29/2023]
Abstract
Parkinson's disease (PD) is characterized by slowed movements, speech disorders, an inability to control muscle movements, and tremors in the hands and feet. In the early stages of PD, the changes in these motor signs are very vague, so an objective and accurate diagnosis is difficult. The disease is complex, progressive, and very common. There are more than 10 million people worldwide suffering from PD. In this study, an EEG-based deep learning model was proposed for the automatic detection of PD to support experts. The EEG dataset comprises signals recorded by the University of Iowa from 14 PD patients and 14 healthy controls. First of all, the power spectral density values (PSDs) of the frequencies between 1 and 49 Hz of the EEG signals were calculated separately using periodogram, welch, and multitaper spectral analysis methods. 49 feature vectors were extracted for each of the three different experiments. Then, the performances of support vector machine, random forest, k-nearest neighbor, and bidirectional long-short-term memory (BiLSTM) algorithms were compared using the PSDs feature vectors. After the comparison, the model integrating welch spectral analysis and the BiLSTM algorithm showed the highest performance as a result of the experiments. The deep learning model achieved satisfactory performance with 0.965 specificity, 0.994 sensitivity, 0.964 precision, 0.978 f1-score, 0.958 Matthews correlation coefficient, and 97.92% accuracy. The study is a promising attempt to detect PD from EEG signals and it also provides evidence that deep learning algorithms are more effective than machine learning algorithms for EEG signal analysis.
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Affiliation(s)
- Hanife Göker
- Health Services Vocational College, Gazi University, 06830, Ankara, Turkey.
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14
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Suuronen I, Airola A, Pahikkala T, Murtojarvi M, Kaasinen V, Railo H. Budget-Based Classification of Parkinson's Disease From Resting State EEG. IEEE J Biomed Health Inform 2023; 27:3740-3747. [PMID: 37018586 DOI: 10.1109/jbhi.2023.3235040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Early detection is vital for future neuroprotective treatments of Parkinson's disease (PD). Resting state electroencephalographic (EEG) recording has shown potential as a cost-effective means to aid in detection of neurological disorders such as PD. In this study, we investigated how the number and placement of electrodes affects classifying PD patients and healthy controls using machine learning based on EEG sample entropy. We used a custom budget-based search algorithm for selecting optimized sets of channels for classification, and iterated over variable channel budgets to investigate changes in classification performance. Our data consisted of 60-channel EEG collected at three different recording sites, each of which included observations collected both eyes open (total N = 178) and eyes closed (total N = 131). Our results with the data recorded eyes open demonstrated reasonable classification performance (ACC = .76; AUC = .76) with only 5 channels placed far away from each other, the selected regions including right-frontal, left-temporal and midline-occipital sites. Comparison to randomly selected subsets of channels indicated improved classifier performance only with relatively small channel-budgets. The results with the data recorded eyes closed demonstrated consistently worse classification performance (when compared to eyes open data), and classifier performance improved more steadily as a function of number of channels. In summary, our results suggest that a small subset of electrodes of an EEG recording can suffice for detecting PD with a classification performance on par with a full set of electrodes. Furthermore our results demonstrate that separately collected EEG data sets can be used for pooled machine learning based PD detection with reasonable classification performance.
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15
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Jaramillo-Jimenez A, Tovar-Rios DA, Ospina JA, Mantilla-Ramos YJ, Loaiza-López D, Henao Isaza V, Zapata Saldarriaga LM, Cadavid Castro V, Suarez-Revelo JX, Bocanegra Y, Lopera F, Pineda-Salazar DA, Tobón Quintero CA, Ochoa-Gomez JF, Borda MG, Aarsland D, Bonanni L, Brønnick K. Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis. Clin Neurophysiol 2023; 151:28-40. [PMID: 37146531 DOI: 10.1016/j.clinph.2023.03.363] [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: 09/25/2022] [Revised: 02/18/2023] [Accepted: 03/27/2023] [Indexed: 05/07/2023]
Abstract
OBJECTIVE This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach. METHODS We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature. RESULTS For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs. CONCLUSIONS Increased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD. SIGNIFICANCE Rs-EEG theta and pre-alpha findings are generalisable in PD. FDA constitutes a reliable and powerful tool to analyse epoch-to-epoch the rs-EEG.
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Affiliation(s)
- Alberto Jaramillo-Jimenez
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital. Stavanger, Norway; Faculty of Health Sciences, University of Stavanger. Stavanger, Norway; Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación SINAPSIS, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia.
| | - Diego A Tovar-Rios
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital. Stavanger, Norway; Faculty of Health Sciences, University of Stavanger. Stavanger, Norway; Universidad del Valle, Grupo de Investigación en Estadística Aplicada - INFERIR, Faculty of Engineering, Santiago de Cali, Colombia; Universidad del Valle, Prevención y Control de la Enfermedad Crónica - PRECEC, Faculty of Health, Santiago de Cali, Colombia
| | - Johann Alexis Ospina
- Facultad de Ciencias Básicas, Universidad Autónoma de Occidente, Santiago de Cali, Colombia
| | - Yorguin-Jose Mantilla-Ramos
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia
| | - Daniel Loaiza-López
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia
| | - Verónica Henao Isaza
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia
| | - Luisa María Zapata Saldarriaga
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia
| | - Valeria Cadavid Castro
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia
| | - Jazmin Ximena Suarez-Revelo
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Yamile Bocanegra
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - David Antonio Pineda-Salazar
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Carlos Andrés Tobón Quintero
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Área Investigación e Innovación, Hospital Alma Mater de Antioquia. Medellín, Colombia
| | - John Fredy Ochoa-Gomez
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Miguel Germán Borda
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital. Stavanger, Norway; Faculty of Health Sciences, University of Stavanger. Stavanger, Norway; Semillero de Neurociencias y Envejecimiento, Pontificia Universidad Javeriana, Ageing Institute, Medical School. Bogotá, Colombia
| | - Dag Aarsland
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital. Stavanger, Norway; Faculty of Health Sciences, University of Stavanger. Stavanger, Norway; Department of Old Age Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College London. London, UK
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, G. d'Annunzio University. Chieti, Italy
| | - Kolbjørn Brønnick
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital. Stavanger, Norway; Faculty of Health Sciences, University of Stavanger. Stavanger, Norway
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Kurbatskaya A, Jaramillo-Jimenez A, Ochoa-Gomez JF, Bronnick K, Fernandez-Quilez A. Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083565 DOI: 10.1109/embc40787.2023.10340700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands (δ and θ) and high-frequency bands (α and β) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD). However, rs-EEG feature extraction and the interpretation thereof can be time-intensive and prone to examiner variability. Machine learning (ML) has the potential to automatize the analysis of rs-EEG recordings and provides a supportive tool for clinicians to ease their workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD subjects pooled from four datasets obtained at different centers. We propose an end-to-end pipeline consisting of preprocessing, extraction of PSD features from clinically-validated frequency bands, and feature selection. Following, we assess the classification ability of the features via ML algorithms to stratify between PD and non-PD subjects. Further, we evaluate the effect of feature harmonization, given the multi-center nature of the datasets. Our validation results show, on average, an improvement in PD detection ability (69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features and performing univariate feature selection (k = 202 features). Our final results show an average global accuracy of 72.2% with balanced accuracy results for all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%, respectively.Clinical relevance- We present an end-to-end pipeline to extract clinically relevant features from rs-EEG recordings that can facilitate the analysis and detection of PD. Further, we provide an ML system that shows a good performance in detecting PD, even in the presence of centers with different acquisition protocols. Our results show the relevance of harmonizing features and provide a good starting point for future studies focusing on rs-EEG analysis and multi-center data.
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Karakaş MF, Latifoğlu F. Distinguishing Parkinson's Disease with GLCM Features from the Hankelization of EEG Signals. Diagnostics (Basel) 2023; 13:1769. [PMID: 37238253 PMCID: PMC10216898 DOI: 10.3390/diagnostics13101769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 04/30/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
This study proposes a novel method that uses electroencephalography (EEG) signals to classify Parkinson's Disease (PD) and demographically matched healthy control groups. The method utilizes the reduced beta activity and amplitude decrease in EEG signals that are associated with PD. The study involved 61 PD patients and 61 demographically matched controls groups, and EEG signals were recorded in various conditions (eyes closed, eyes open, eyes both open and closed, on-drug, off-drug) from three publicly available EEG data sources (New Mexico, Iowa, and Turku). The preprocessed EEG signals were classified using features obtained from gray-level co-occurrence matrix (GLCM) features through the Hankelization of EEG signals. The performance of classifiers with these novel features was evaluated using extensive cross-validations (CV) and leave-one-out cross-validation (LOOCV) schemes. This method under 10 × 10 fold CV, the method was able to differentiate PD groups from healthy control groups using a support vector machine (SVM) with an accuracy of 92.4 ± 0.01, 85.7 ± 0.02, and 77.1 ± 0.06 for New Mexico, Iowa, and Turku datasets, respectively. After a head-to-head comparison with state-of-the-art methods, this study showed an increase in the classification of PD and controls.
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Affiliation(s)
- Mehmet Fatih Karakaş
- Faculty of Engineering and Architecture, Department of Biomedical Engineering, Erzincan Binali Yildirim University, Erzincan 24002, Turkey
| | - Fatma Latifoğlu
- Faculty of Engineering, Department of Biomedical Engineering, Erciyes University, Kayseri 38280, Turkey
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18
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Nour M, Senturk U, Polat K. Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN. Comput Biol Med 2023; 161:107031. [PMID: 37211002 DOI: 10.1016/j.compbiomed.2023.107031] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/05/2023] [Accepted: 05/10/2023] [Indexed: 05/23/2023]
Abstract
In this paper, we proposed a novel approach to diagnose and classify Parkinson's Disease (PD) using ensemble learning and 1D-PDCovNN, a novel deep learning technique. PD is a neurodegenerative disorder; early detection and correct classification are essential for better disease management. The primary aim of this study is to develop a robust approach to diagnosing and classifying PD using EEG signals. As the dataset, we have used the San Diego Resting State EEG dataset to evaluate our proposed method. The proposed method mainly consists of three stages. In the first stage, the Independent Component Analysis (ICA) method has been used as the pre-processing method to filter out the blink noises from the EEG signals. Also, the effect of the band showing motor cortex activity in the 7-30 Hz frequency band of EEG signals in diagnosing and classifying Parkinson's disease from EEG signals has been investigated. In the second stage, the Common Spatial Pattern (CSP) method has been used as the feature extraction to extract useful information from EEG signals. Finally, an ensemble learning approach, Dynamic Classifier Selection (DCS) in Modified Local Accuracy (MLA), has been employed in the third stage, consisting of seven different classifiers. As the classifier method, DCS in MLA, XGBoost, and 1D-PDCovNN classifier has been used to classify the EEG signals as the PD and healthy control (HC). We first used dynamic classifier selection to diagnose and classify Parkinson's disease (PD) from EEG signals, and promising results have been obtained. The performance of the proposed approach has been evaluated using the classification accuracy, F-1 score, kappa score, Jaccard score, ROC curve, recall, and precision values in the classification of PD with the proposed models. In the classification of PD, the combination of DCS in MLA achieved an accuracy of 99,31%. The results of this study demonstrate that the proposed approach can be used as a reliable tool for early diagnosis and classification of PD.
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Affiliation(s)
- Majid Nour
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Umit Senturk
- Department of Computer Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey.
| | - Kemal Polat
- Department of Electrical and Electronics Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey.
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Anjum MF, Espinoza A, Cole R, Singh A, May P, Uc E, Dasgupta S, Narayanan N. Resting-state EEG measures cognitive impairment in Parkinson's disease. RESEARCH SQUARE 2023:rs.3.rs-2666578. [PMID: 36993450 PMCID: PMC10055637 DOI: 10.21203/rs.3.rs-2666578/v1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Background Cognitive dysfunction is common in Parkinson's disease (PD) and is diagnosed by complex, time-consuming psychometric tests which are affected by language and education, subject to learning effects, and not suitable for continuous monitoring of cognition. Objectives We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. Methods We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from the National Institutes of Health (NIH) Toolbox using cross-validation schemes, regression models, and randomization tests. Results We observed cognition-related changes in EEG activities over multiple spectral rhythms. Utilizing only 8 best-performing EEG electrodes, our proposed index strongly correlated with cognition (rho = 0.68, p value < 0.001 with MoCA; rho ≥ 0.56, p value < 0.001 with cognitive tests from the NIH Toolbox) outperforming traditional spectral markers (rho = -0.30 - 0.37). The index showed a strong fit in regression models (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Conclusions Our approach is computationally efficient for real-time indexing of cognition across domains, implementable even in hardware with limited computing capabilities, making it potentially compatible with dynamic therapies such as closed-loop neurostimulation, and will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.
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Bush A, Zou J, Lipski WJ, Kokkinos V, Richardson RM. Broadband aperiodic components of local field potentials reflect inherent differences between cortical and subcortical activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.08.527719. [PMID: 36798268 PMCID: PMC9934688 DOI: 10.1101/2023.02.08.527719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Information flow in brain networks is reflected in intracerebral local field potential (LFP) measurements that have both periodic and aperiodic components. The 1/fχ broadband aperiodic component of the power spectra has been shown to track arousal level and to correlate with other physiological and pathophysiological states, with consistent patterns across cortical regions. Previous studies have focused almost exclusively on cortical neurophysiology. Here we explored the aperiodic activity of subcortical nuclei from the human thalamus and basal ganglia, in relation to simultaneously recorded cortical activity. We elaborated on the FOOOF (fitting of one over f) method by creating a new parameterization of the aperiodic component with independent and more easily interpretable parameters, which allows seamlessly fitting spectra with and without an aperiodic knee, a component of the signal that reflects the dominant timescale of aperiodic fluctuations. First, we found that the aperiodic exponent from sensorimotor cortex in Parkinson's disease (PD) patients correlated with disease severity. Second, although the aperiodic knee frequency changed across cortical regions as previously reported, no aperiodic knee was detected from subcortical regions across movement disorders patients, including the ventral thalamus (VIM), globus pallidus internus (GPi) and subthalamic nucleus (STN). All subcortical region studied exhibited a relatively low aperiodic exponent (χSTN=1.3±0.2, χVIM=1.4±0.1, χGPi =1.4±0.1) that differed markedly from cortical values (χCortex=3.2±0.4, fkCortex=17±5 Hz). These differences were replicated in a second dataset from epilepsy patients undergoing intracranial monitoring that included thalamic recordings. The consistently lower aperiodic exponent and lack of an aperiodic knee from all subcortical recordings may reflect cytoarchitectonic and/or functional differences between subcortical nuclei and the cortex.
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Affiliation(s)
- Alan Bush
- Brain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jasmine Zou
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology,Cambridge, MA, USA
| | - Witold J. Lipski
- Department of Neurological Surgery, University of Pittsburgh, School of Medicine, Pittsburgh, PA, USA
| | - Vasileios Kokkinos
- Brain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - R. Mark Richardson
- Brain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology,Cambridge, MA, USA
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Chawla P, Rana SB, Kaur H, Singh K, Yuvaraj R, Murugappan M. A decision support system for automated diagnosis of Parkinson’s disease from EEG using FAWT and entropy features. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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22
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Detection of Parkinson's disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques. Sci Rep 2022; 12:22547. [PMID: 36581646 PMCID: PMC9800369 DOI: 10.1038/s41598-022-26644-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 12/19/2022] [Indexed: 12/30/2022] Open
Abstract
Early detection of Parkinson's disease (PD) is very important in clinical diagnosis for preventing disease development. In this study, we present efficient discrete wavelet transform (DWT)-based methods for detecting PD from health control (HC) in two cases, namely, off-and on-medication. First, the EEG signals are preprocessed to remove major artifacts before being decomposed into several EEG sub-bands (approximate and details) using DWT. The features are then extracted from the wavelet packet-derived reconstructed signals using different entropy measures, namely, log energy entropy, Shannon entropy, threshold entropy, sure entropy, and norm entropy. Several machine learning techniques are investigated to classify the resulting PD/HC features. The effects of DWT coefficients and brain regions on classification accuracy are being investigated as well. Two public datasets are used to verify the proposed methods: the SanDiego dataset (31 subjects, 93 min) and the UNM dataset (54 subjects, 54 min). The results are promising and show that four entropy measures: log energy entropy, threshold entropy, sure entropy, and modified-Shannon entropy (TShEn) lead to high classification accuracy, indicating they are good biomarkers for PD detection. With the SanDiego dataset, the classification results of off-medication PD versus HC are 99.89, 99.87, and 99.91 for accuracy, sensitivity, and specificity, respectively, using the combination of DWT + TShEn and KNN classifier. Using the same combination, the results of on-medication PD versus HC are 94.21, 93.33, and 95%. With the UNM dataset, the obtained classification accuracy is around 99.5% in both cases of off-and on-medication PD using DWT + TShEn + SVM and DWT + ThEn + KNN, respectively. The results also demonstrate the importance of all DWT coefficients and that selecting a suitable small number of EEG channels from several brain regions could improve the classification accuracy.
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Mishra S, Kumar Satapathy S, Mohanty SN, Pattnaik CR. A DM-ELM based classifier for EEG brain signal classification for epileptic seizure detection. Commun Integr Biol 2022; 16:2153648. [PMID: 36531748 PMCID: PMC9757406 DOI: 10.1080/19420889.2022.2153648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Epilepsy is one of the dreaded conditions that had taken billions of people under its cloud worldwide. Detecting the seizure at the correct time in an individual is something that medical practitioners focus in order to help people save their lives. Analysis of the Electroencephalogram (EEG) signal from the scalp area of the human brain can help in detecting the seizure beforehand. This paper presents a novel classification technique to classify EEG brain signals for epilepsy identification based on Discrete Wavelet Transform and Moth Flame Optimization-based Extreme Learning Machine (DM-ELM). ELM is a very popular machine learning method based on Neural Networks (NN) where the model is trained rigorously to get the minimized error rate and maximized accuracy. Here we have used several experimental evaluations to compare the performance of basic ELM and DM-ELM and it has been experimentally proved that DM-ELM outperforms basic ELM but with few time constraints.
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Affiliation(s)
- Shruti Mishra
- Department of Computer Science & Engineering, Vellore Institute of Technology, Chennai, india
| | - Sandeep Kumar Satapathy
- Department of Computer Science & Engineering, Vellore Institute of Technology, Chennai, india
| | - Sachi Nandan Mohanty
- School of Computer Science &Engineering, VIT-AP University, Amaravati, India,CONTACT Sachi Nandan Mohanty School of Computer Science &Engineering, VIT-AP University, Amaravati, India
| | - Chinmaya Ranjan Pattnaik
- Department of Computer Science & Engineering, Ajay Binaya Institute of Technology, Cuttack, India
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Tawhid MNA, Siuly S, Wang K, Wang H. Textural feature based intelligent approach for neurological abnormality detection from brain signal data. PLoS One 2022; 17:e0277555. [PMID: 36374850 PMCID: PMC9662730 DOI: 10.1371/journal.pone.0277555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/30/2022] [Indexed: 11/16/2022] Open
Abstract
The diagnosis of neurological diseases is one of the biggest challenges in modern medicine, which is a major issue at the moment. Electroencephalography (EEG) recordings is usually used to identify various neurological diseases. EEG produces a large volume of multi-channel time-series data that neurologists visually analyze to identify and understand abnormalities within the brain and how they propagate. This is a time-consuming, error-prone, subjective, and exhausting process. Moreover, recent advances in EEG classification have mostly focused on classifying patients of a specific disease from healthy subjects using EEG data, which is not cost effective as it requires multiple systems for checking a subject's EEG data for different neurological disorders. This forces researchers to advance their work and create a single, unified classification framework for identifying various neurological diseases from EEG signal data. Hence, this study aims to meet this requirement by developing a machine learning (ML) based data mining technique for categorizing multiple abnormalities from EEG data. Textural feature extractors and ML-based classifiers are used on time-frequency spectrogram images to develop the classification system. Initially, noises and artifacts are removed from the signal using filtering techniques and then normalized to reduce computational complexity. Afterwards, normalized signals are segmented into small time segments and spectrogram images are generated from those segments using short-time Fourier transform. Then two histogram based textural feature extractors are used to calculate features separately and principal component analysis is used to select significant features from the extracted features. Finally, four different ML based classifiers are used to categorize those selected features into different disease classes. The developed method is tested on four real-time EEG datasets. The obtained result has shown potential in classifying various abnormality types, indicating that it can be utilized to identify various neurological abnormalities from brain signal data.
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Affiliation(s)
- Md. Nurul Ahad Tawhid
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia
| | - Siuly Siuly
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia
| | - Kate Wang
- School of Health and Biomedical Sciences, RMIT University, Melbourne, Victoria, Australia
| | - Hua Wang
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia
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Espinoza AI, May P, Anjum MF, Singh A, Cole RC, Trapp N, Dasgupta S, Narayanan NS. A pilot study of machine learning of resting-state EEG and depression in Parkinson's disease. Clin Park Relat Disord 2022; 7:100166. [PMID: 36203748 PMCID: PMC9529981 DOI: 10.1016/j.prdoa.2022.100166] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/02/2022] [Accepted: 09/21/2022] [Indexed: 01/11/2023] Open
Abstract
Introduction Depression is a non-motor symptom of Parkinson's disease (PD). PD-related depression is difficult to diagnose, and the neurophysiological basis is poorly understood. Depression can markedly affect cortical function, which suggests that scalp electroencephalography (EEG) may be able to distinguish depression in PD. We conducted a pilot study of depression and resting-state EEG in PD. Methods We recruited 18 PD patients without depression, 18 PD patients with depression, and 12 demographically similar non-PD patients with clinical depression. All patients were on their usual medications. We collected resting-state EEG in all patients and compared cortical brain signal features between patients with and without depression. We used a machine learning algorithm that harnesses the entire power spectrum (linear predictive coding of EEG Algorithm for PD: LEAPD) to distinguish between groups. Results We found differences between PD patients with and without depression in the alpha band (8-13 Hz) globally and in the beta (13-30 Hz) and gamma (30-50 Hz) bands in the central electrodes. From two minutes of resting-state EEG, we found that LEAPD-based machine learning could robustly distinguish between PD patients with and without depression with 97 % accuracy and between PD patients with depression and non-PD patients with depression with 100 % accuracy. We verified the robustness of our finding by confirming that the classification accuracy gracefully declines as data are randomly truncated. Conclusions Our results suggest that resting-state EEG power spectral analysis has the potential to distinguish depression in PD accurately. We demonstrated the efficacy of the LEAPD algorithm in identifying PD patients with depression from PD patients without depression and controls with depression. Our data provide insight into cortical mechanisms of depression and could lead to novel neurophysiological markers for non-motor symptoms of PD.
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Affiliation(s)
| | - Patrick May
- Department of Electrical and Computer Engineering, University of Iowa, United States
| | - Md Fahim Anjum
- Department of Electrical and Computer Engineering, University of Iowa, United States
| | - Arun Singh
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, United States
| | - Rachel C. Cole
- Department of Neurology, University of Iowa, United States
| | - Nicholas Trapp
- Department of Psychiatry, University of Iowa, United States
| | - Soura Dasgupta
- Department of Electrical and Computer Engineering, University of Iowa, United States
| | - Nandakumar S. Narayanan
- Department of Neurology, University of Iowa, United States,Corresponding author at: 169 Newton Road, Pappajohn Biomedical Discovery Building—5336, University of Iowa, Iowa City 52242, United States.
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Qiu L, Li J, Pan J. Parkinson’s disease detection based on multi-pattern analysis and multi-scale convolutional neural networks. Front Neurosci 2022; 16:957181. [PMID: 35968382 PMCID: PMC9363757 DOI: 10.3389/fnins.2022.957181] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
Parkinson’s disease (PD) is a complex neurodegenerative disease. At present, the early diagnosis of PD is still extremely challenging, and there is still a lack of consensus on the brain characterization of PD, and a more efficient and robust PD detection method is urgently needed. In order to further explore the features of PD based on brain activity and achieve effective detection of PD patients (including OFF and ON medications), in this study, a multi-pattern analysis based on brain activation and brain functional connectivity was performed on the brain functional activity of PD patients, and a novel PD detection model based on multi-scale convolutional neural network (MCNN) was proposed. Based on the analysis of power spectral density (PSD) and phase-locked value (PLV) features of multiple frequency bands of two independent resting-state electroencephalography (EEG) datasets, we found that there were significant differences in PSD and PLV between HCs and PD patients (including OFF and ON medications), especially in the β and γ bands, which were very effective for PD detection. Moreover, the combined use of brain activation represented by PSD and functional connectivity patterns represented by PLV can effectively improve the performance of PD detection. Furthermore, our proposed MCNN model shows great potential for automatic PD detection, with cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve all above 99%. Our study may help to further understand the characteristics of PD and provide new ideas for future PD diagnosis based on spontaneous EEG activity.
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Avvaru S, Parhi KK. Betweenness Centrality in Resting-State Functional Networks Distinguishes Parkinson's Disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4785-4788. [PMID: 36086073 DOI: 10.1109/embc48229.2022.9870988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The goal of this paper is to use graph theory network measures derived from non-invasive electroencephalography (EEG) to develop neural decoders that can differentiate Parkinson's disease (PD) patients from healthy controls (HC). EEG signals from 27 patients and 27 demographically matched controls from New Mexico were analyzed by estimating their functional networks. Data recorded from the patients during ON and OFF levodopa sessions were included in the analysis for comparison. We used betweenness centrality of estimated functional networks to classify the HC and PD groups. The classifiers were evaluated using leave-one-out cross-validation. We observed that the PD patients (on and off medication) could be distinguished from healthy controls with 89% accuracy - approximately 4% higher than the state-of-the-art on the same dataset. This work shows that brain network analysis using extracranial resting-state EEG can discover patterns of interactions indicative of PD. This approach can also be extended to other neurological disorders.
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Ezazi Y, Ghaderyan P. Textural feature of EEG signals as a new biomarker of reward processing in Parkinson’s disease detection. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Parkinson’s Disease Detection from Resting-State EEG Signals Using Common Spatial Pattern, Entropy, and Machine Learning Techniques. Diagnostics (Basel) 2022; 12:diagnostics12051033. [PMID: 35626189 PMCID: PMC9139946 DOI: 10.3390/diagnostics12051033] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/08/2022] [Accepted: 04/18/2022] [Indexed: 02/04/2023] Open
Abstract
Parkinson’s disease (PD) is a very common brain abnormality that affects people all over the world. Early detection of such abnormality is critical in clinical diagnosis in order to prevent disease progression. Electroencephalography (EEG) is one of the most important PD diagnostic tools since this disease is linked to the brain. In this study, novel efficient common spatial pattern-based approaches for detecting Parkinson’s disease in two cases, off–medication and on–medication, are proposed. First, the EEG signals are preprocessed to remove major artifacts before spatial filtering using a common spatial pattern. Several features are extracted from spatially filtered signals using different metrics, namely, variance, band power, energy, and several types of entropy. Machine learning techniques, namely, random forest, linear/quadratic discriminant analysis, support vector machine, and k-nearest neighbor, are investigated to classify the extracted features. The impacts of frequency bands, segment length, and reduction number on the results are also investigated in this work. The proposed methods are tested using two EEG datasets: the SanDiego dataset (31 participants, 93 min) and the UNM dataset (54 participants, 54 min). The results show that the proposed methods, particularly the combination of common spatial patterns and log energy entropy, provide competitive results when compared to methods in the literature. The achieved results in terms of classification accuracy, sensitivity, and specificity in the case of off-medication PD detection are around 99%. In the case of on-medication PD, the results range from 95% to 98%. The results also reveal that features extracted from the alpha and beta bands have the highest classification accuracy.
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Halhouli O, Zhang Q, Aldridge GM. Caring for patients with cognitive dysfunction, fluctuations and dementia caused by Parkinson's disease. PROGRESS IN BRAIN RESEARCH 2022; 269:407-434. [PMID: 35248204 DOI: 10.1016/bs.pbr.2022.01.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Cognitive dysfunction is one of the most prevalent non-motor symptoms in patients with Parkinson's disease (PD). While it tends to worsen in the later stages of disease, it can occur at any time, with 15-20% of patients exhibiting cognitive deficits at diagnosis (Aarsland et al., 2010; Goldman and Sieg, 2020). The characteristic features of cognitive dysfunction include impairment in executive function, visuospatial abilities, and attention, which vary in severity from subtle impairment to overt dementia (Martinez-Horta and Kulisevsky, 2019). To complicate matters, cognitive dysfunction is prone to fluctuate in PD patients, impacting diagnosis and the ability to assess progression and decision-making capacity. The diagnosis of cognitive impairment or dementia has a huge impact on patient independence, quality of life, life expectancy and caregiver burden (Corallo et al., 2017; Lawson et al., 2016; Leroi et al., 2012). It is therefore essential that physicians caring for patients with PD provide education, screening and treatment for this aspect of the disease. In this chapter, we provide a practical guide for the assessment and management of various degrees of cognitive dysfunction in patients with PD by approaching the disease at different stages. We address risk factors for cognitive dysfunction, prevention strategies prior to making the diagnosis, available tools for screening. Lastly, we review aspects of care, management and considerations, including decision-making capacity, that occur after the patient has been diagnosed with cognitive dysfunction or dementia.
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Affiliation(s)
- Oday Halhouli
- University of Iowa, Department of Neurology, Iowa City, IA, United States
| | - Qiang Zhang
- University of Iowa, Department of Neurology, Iowa City, IA, United States
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Lee SB, Kim YJ, Hwang S, Son H, Lee SK, Park KI, Kim YG. Predicting Parkinson's disease using gradient boosting decision tree models with electroencephalography signals. Parkinsonism Relat Disord 2022; 95:77-85. [PMID: 35051896 DOI: 10.1016/j.parkreldis.2022.01.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 01/11/2022] [Accepted: 01/11/2022] [Indexed: 12/22/2022]
Abstract
INTRODUCTION Parkinson's disease (PD) is a neurodegenerative disorder with only symptomatic treatments currently available. Although correct, early diagnoses of PD are important, the existing diagnostic method based on pathologic examinations only has an accuracy of approximately 80.6%. Although electroencephalography (EEG)-based assistive technology has been introduced, it has been difficult to implement in practice due to the high computational complexity and low accuracy of the analysis methods. This study proposed a fast, accurate PD prediction method using the Hjorth parameter and the gradient boosting decision tree (GBDT) algorithm. METHOD We used an open EEG dataset with 41 PD patients and 41 healthy controls (HCs); EEG signals were recorded from participants at the University of New Mexico (PD: 27 vs. HC: 27) and University of Iowa (PD: 14 vs. HC: 14). We explored the analytic time segment and frequency range in which the Hjorth parameter best represents the EEG characteristics of PD patients. RESULTS Our best model (CatBoost-based) distinguished PD patients from controls with an accuracy of 89.3%, an area under the receiver operating characteristics curve (AUC) of 0.912, an F-score of 0.903, and an odds ratio of 115.5. These results showed that our models outperformed those of all other previous works and were even superior to previously known pathologic examination-based diagnoses with long-term follow-up (accuracy = 83.9%). CONCLUSION The proposed methods are expected to be utilized as an effective method for improving the diagnosis of PD.
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Affiliation(s)
- Seung-Bo Lee
- Office of Hospital Information, Seoul National University Hospital, Seoul, Republic of Korea.
| | - Yong-Jeong Kim
- Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea.
| | - Sungeun Hwang
- Department of Neurology, Ewha Womans University Mokdong Hospital, Seoul, Republic of Korea.
| | - Hyoshin Son
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea.
| | - Sang Kun Lee
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea.
| | - Kyung-Il Park
- Department of Neurology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea; Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Young-Gon Kim
- Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea; AI Institute, Seoul National University, Seoul, Republic of Korea.
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Hassin-Baer S, Cohen OS, Israeli-Korn S, Yahalom G, Benizri S, Sand D, Issachar G, Geva AB, Shani-Hershkovich R, Peremen Z. Identification of an early-stage Parkinson's disease neuromarker using event-related potentials, brain network analytics and machine-learning. PLoS One 2022; 17:e0261947. [PMID: 34995285 PMCID: PMC8741046 DOI: 10.1371/journal.pone.0261947] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 11/24/2021] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson's disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms. BACKGROUND Currently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD. METHODS Nineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation. RESULTS The ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task). CONCLUSIONS This pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.
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Affiliation(s)
- Sharon Hassin-Baer
- Movement Disorders Institute and Department of Neurology, Chaim Sheba Medical Center, Tel Hashomer, Ramat-Gan, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Oren S. Cohen
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Neurology, Assaf Harofeh Medical Center, Zerifin, Israel
| | - Simon Israeli-Korn
- Movement Disorders Institute and Department of Neurology, Chaim Sheba Medical Center, Tel Hashomer, Ramat-Gan, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Gilad Yahalom
- Department of Neurology and Movement Disorders Clinic, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Sandra Benizri
- Movement Disorders Unit, Functional Neurosurgery Center, Assuta Ramat Ha Hayal Hospital, Tel Aviv, Israel
| | - Daniel Sand
- elminda Ltd., Herzliya, Israel
- Faculty of Medicine, Department of Medical Neurobiology, The Hebrew University of Jerusalem, Ein Kerem, Jerusalem, Israel
| | | | - Amir B. Geva
- elminda Ltd., Herzliya, Israel
- Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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Novel automated PD detection system using aspirin pattern with EEG signals. Comput Biol Med 2021; 137:104841. [PMID: 34509880 DOI: 10.1016/j.compbiomed.2021.104841] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 11/20/2022]
Abstract
BACKGROUND AND OBJECTIVE Parkinson's disease (PD) is one of the most common diseases worldwide which reduces quality of life of patients and their family members. The electroencephalogram (EEG) signals coupled with various advanced machine-learning algorithms have been widely used to detect PD automatically. In this paper, we propose a novel aspirin pattern to detect PD accurately using EEG signals. METHOD In this research, the feature generation ability of a chemical graph is investigated. Therefore, this work presents a new graph-based aspirin model for automated PD detection using EEG signals. The proposed method consists of (i) multilevel feature generation phase involving new aspirin pattern, statistical moments, and maximum absolute pooling (MAP), (ii) selection of most discriminative features using neighborhood component analysis (NCA), and (iii) classification using k nearest neighbor (kNN) for automated detection of PD and (iv) iterative majority voting. RESULTS A public dataset has been used to develop the proposed model. Two cases are created, and these cases consisted of two classes. Leave one subject out (LOSO) validation have been used to calculate robust results. Our proposal achieved 93.57% and 95.48% classification accuracies for Case 1 and Case 2 respectively. CONCLUSION Our developed automated PD model is accurate and equipped to be tested with more diverse EEG datasets.
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Iravani B, Arshamian A, Schaefer M, Svenningsson P, Lundström JN. A non-invasive olfactory bulb measure dissociates Parkinson's patients from healthy controls and discloses disease duration. NPJ Parkinsons Dis 2021; 7:75. [PMID: 34408159 PMCID: PMC8373926 DOI: 10.1038/s41531-021-00220-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/22/2021] [Indexed: 11/22/2022] Open
Abstract
Olfactory dysfunction is a prevalent non-motor symptom of Parkinson's disease (PD). This dysfunction is a result of neurodegeneration within the olfactory bulb (OB), the first processing area of the central olfactory system, and commonly precedes the characteristic motor symptoms in PD by several years. Functional measurements of the OB could therefore potentially be used as an early biomarker for PD. Here, we used a non-invasive method, so-called electrobulbogram (EBG), to measure OB function in PD and age-matched healthy controls to assess whether EBG measures can dissociate PDs from controls. We estimated the spectrogram of the EBG signal during exposure to odor in PD (n = 20) and age-matched controls (n = 18) as well as identified differentiating patterns of odor-related synchronization in the gamma, beta, and theta frequency bands. Moreover, we assessed if these PD-EBG components could dissociate PD from control as well as their relationship with PD characteristics. We identified six EBG components during the initial and later stages of odor processing which dissociated PD from controls with 90% sensitivity and 100% specificity with links to PD characteristics. These PD-EBG components were related to medication, disease duration, and severity, as well as clinical odor identification performance. These findings support using EBG as a tool to experimentally assess PD interventions, potentially aid diagnosis, and the potential development of EBG into an early biomarker for PD.
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Affiliation(s)
- Behzad Iravani
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
| | - Artin Arshamian
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Psychology, Stockholm University, Stockholm, Sweden
| | - Martin Schaefer
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Per Svenningsson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Johan N Lundström
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
- Monell Chemical Senses Center, Philadelphia, PA, USA.
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA.
- Stockholm University Brain Imaging Centre, Stockholm University, Stockholm, Sweden.
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GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals. ELECTRONICS 2021. [DOI: 10.3390/electronics10141740] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Parkinson’s disease (PD) is globally the most common neurodegenerative movement disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the brain. However, current methods to diagnose PD on the basis of clinical features of Parkinsonism may lead to misdiagnoses. Hence, noninvasive methods such as electroencephalographic (EEG) recordings of PD patients can be an alternative biomarker. In this study, a deep-learning model is proposed for automated PD diagnosis. EEG recordings of 16 healthy controls and 15 PD patients were used for analysis. Using Gabor transform, EEG recordings were converted into spectrograms, which were used to train the proposed two-dimensional convolutional neural network (2D-CNN) model. As a result, the proposed model achieved high classification accuracy of 99.46% (±0.73) for 3-class classification (healthy controls, and PD patients with and without medication) using tenfold cross-validation. This indicates the potential of proposed model to simultaneously automatically detect PD patients and their medication status. The proposed model is ready to be validated with a larger database before implementation as a computer-aided diagnostic (CAD) tool for clinical-decision support.
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Khare SK, Bajaj V, Acharya UR. Detection of Parkinson’s disease using automated tunable Q wavelet transform technique with EEG signals. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.04.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Zhang Q, Aldridge GM, Narayanan NS, Anderson SW, Uc EY. Approach to Cognitive Impairment in Parkinson's Disease. Neurotherapeutics 2020; 17:1495-1510. [PMID: 33205381 PMCID: PMC7851260 DOI: 10.1007/s13311-020-00963-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/30/2020] [Indexed: 01/03/2023] Open
Abstract
Cognitive dysfunction is common in Parkinson's disease (PD) and predicts poor clinical outcomes. It is associated primarily with pathologic involvement of basal forebrain cholinergic and prefrontal dopaminergic systems. Impairments in executive functions, attention, and visuospatial abilities are its hallmark features with eventual involvement of memory and other domains. Subtle symptoms in the premotor and early phases of PD progress to mild cognitive impairment (MCI) which may be present at the time of diagnosis. Eventually, a large majority of PD patients develop dementia with advancing age and longer disease duration, which is usually accompanied by immobility, hallucinations/psychosis, and dysautonomia. Dopaminergic medications and deep brain stimulation help motor dysfunction, but may have potential cognitive side effects. Central acetylcholinesterase inhibitors, and possibly memantine, provide modest and temporary symptomatic relief for dementia, although there is no evidence-based treatment for MCI. There is no proven disease-modifying treatment for cognitive impairment in PD. The symptomatic and disease-modifying role of physical exercise, cognitive training, and neuromodulation on cognitive impairment in PD is under investigation. Multidisciplinary approaches to cognitive impairment with effective treatment of comorbidities, proper rehabilitation, and maintenance of good support systems in addition to pharmaceutical treatment may improve the quality of life of the patients and caregivers.
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Affiliation(s)
- Qiang Zhang
- Department of Neurology, Carver College of Medicine, University of Iowa, 200 Hawkins Drive-2RCP, Iowa City, Iowa 52242 USA
- Neurology Service, Veterans Affairs Medical Center, Iowa City, Iowa USA
| | - Georgina M. Aldridge
- Department of Neurology, Carver College of Medicine, University of Iowa, 200 Hawkins Drive-2RCP, Iowa City, Iowa 52242 USA
| | - Nandakumar S. Narayanan
- Department of Neurology, Carver College of Medicine, University of Iowa, 200 Hawkins Drive-2RCP, Iowa City, Iowa 52242 USA
| | - Steven W. Anderson
- Department of Neurology, Carver College of Medicine, University of Iowa, 200 Hawkins Drive-2RCP, Iowa City, Iowa 52242 USA
| | - Ergun Y. Uc
- Department of Neurology, Carver College of Medicine, University of Iowa, 200 Hawkins Drive-2RCP, Iowa City, Iowa 52242 USA
- Neurology Service, Veterans Affairs Medical Center, Iowa City, Iowa USA
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