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Kachare P, Puri D, Sangle SB, Al-Shourbaji I, Jabbari A, Kirner R, Alameen A, Migdady H, Abualigah L. LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection. Phys Eng Sci Med 2024; 47:1037-1050. [PMID: 38862778 DOI: 10.1007/s13246-024-01425-w] [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: 11/26/2023] [Accepted: 04/10/2024] [Indexed: 06/13/2024]
Abstract
Alzheimer's disease (AD) is a progressive and incurable neurologi-cal disorder with a rising mortality rate, worsened by error-prone, time-intensive, and expensive clinical diagnosis methods. Automatic AD detection methods using hand-crafted Electroencephalogram (EEG) signal features lack accuracy and reliability. A lightweight convolution neural network for AD detection (LCADNet) is investigated to extract disease-specific features while reducing the detection time. The LCADNet uses two convolutional layers for extracting complex EEG features, two fully connected layers for selecting disease-specific features, and a softmax layer for predicting AD detection probability. A max-pooling layer interlaced between convolutional layers decreases the time-domain redundancy in the EEG signal. The efficiency of the LCADNet and four pre-trained models using transfer learning is compared using a publicly available AD detection dataset. The LCADNet shows the lowest computation complexity in terms of both the number of floating point operations and inference time and the highest classification performance across six measures. The generalization of the LCADNet is assessed by cross-testing it with two other publicly available AD detection datasets. It outperforms existing EEG-based AD detection methods with an accuracy of 98.50%. The LCADNet may be a valuable aid for neurologists and its Python implemen- tation can be found at github.com/SandeepSangle12/LCADNet.git.
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Affiliation(s)
- Pramod Kachare
- Department of Electronics and Telecommunication, Ramrao Adik Institute of Technology, D. Y. Patil Campus, Navi-Mumbai, Maharashtra, 400706, India
| | - Digambar Puri
- Department of Electronics and Telecommunication, Ramrao Adik Institute of Technology, D. Y. Patil Campus, Navi-Mumbai, Maharashtra, 400706, India
| | - Sandeep B Sangle
- Department of Electronics and Telecommunication, Ramrao Adik Institute of Technology, D. Y. Patil Campus, Navi-Mumbai, Maharashtra, 400706, India
| | - Ibrahim Al-Shourbaji
- Department of Electrical and Electronics Engineering, Jazan University, Jazan, 45142, Saudi Arabia
- Department of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Abdoh Jabbari
- Department of Electrical and Electronics Engineering, Jazan University, Jazan, 45142, Saudi Arabia
| | - Raimund Kirner
- Department of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Abdalla Alameen
- Department of Computer Engineering and Information, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Wadi Alddawasir, 11991, Saudi Arabia
| | - Hazem Migdady
- CSMIS Department, Oman College of Management and Technology, 320, Barka, Oman
| | - Laith Abualigah
- Jadara Research Center, Jadara University, Irbid, 21110, Jordan.
- Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan.
- MEU Research Unit, Middle East University, Amman, 11831, Jordan.
- Applied science research center, Applied science private university, Amman, 11931, Jordan.
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Gaeta AM, Quijada-López M, Barbé F, Vaca R, Pujol M, Minguez O, Sánchez-de-la-Torre M, Muñoz-Barrutia A, Piñol-Ripoll G. Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach. Front Aging Neurosci 2024; 16:1369545. [PMID: 38988328 PMCID: PMC11233742 DOI: 10.3389/fnagi.2024.1369545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 06/04/2024] [Indexed: 07/12/2024] Open
Abstract
Introduction Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Current core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a lumbar puncture to be performed, making them impractical as screening tools. Considering the role of sleep disturbances in AD, recent research suggests quantitative sleep electroencephalography features as potential non-invasive biomarkers of AD pathology. However, quantitative analysis of comprehensive polysomnography (PSG) signals remains relatively understudied. PSG is a non-invasive test enabling qualitative and quantitative analysis of a wide range of parameters, offering additional insights alongside other biomarkers. Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers. Methods Mild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers. Results On 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aβ42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aβ42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors. Conclusions Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions.
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Affiliation(s)
- Anna Michela Gaeta
- Servicio de Neumología, Hospital Universitario Severo Ochoa, Leganés, Spain
| | - María Quijada-López
- Departamento de Bioingeniería, Universidad Carlos III de Madrid, Leganés, Spain
| | - Ferran Barbé
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, Institut de Recerca Biomedica de Lleida (IRBLleida), Lleida, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain
| | - Rafaela Vaca
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, Institut de Recerca Biomedica de Lleida (IRBLleida), Lleida, Spain
| | - Montse Pujol
- Unitat Trastorns Cognitius, Clinical Neuroscience Research, Institut de Recerca Biomedica de Lleida (IRBLleida), Hospital Universitari Santa Maria, Lleida, Spain
| | - Olga Minguez
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, Institut de Recerca Biomedica de Lleida (IRBLleida), Lleida, Spain
| | - Manuel Sánchez-de-la-Torre
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain
- Group of Precision Medicine in Chronic Diseases, Hospital Nacional de Parapléjicos, IDISCAM, Department of Nursing, Physiotherapy and Occupational Therapy, Faculty of Physiotherapy and Nursing, University of Castilla-La Mancha, Toledo, Spain
| | - Arrate Muñoz-Barrutia
- Departamento de Bioingeniería, Universidad Carlos III de Madrid, Leganés, Spain
- Departamento de Bioingegneria, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Gerard Piñol-Ripoll
- Unitat Trastorns Cognitius, Clinical Neuroscience Research, Institut de Recerca Biomedica de Lleida (IRBLleida), Hospital Universitari Santa Maria, Lleida, Spain
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Zhou H, Yin L, Su R, Zhang Y, Yuan Y, Xie P, Li X. STCGRU: A hybrid model based on CNN and BiGRU for mild cognitive impairment diagnosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 248:108123. [PMID: 38471292 DOI: 10.1016/j.cmpb.2024.108123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/28/2023] [Accepted: 03/07/2024] [Indexed: 03/14/2024]
Abstract
BACKGROUND AND OBJECTIVE Early diagnosis of mild cognitive impairment (MCI) is one of the essential measures to prevent its further development into Alzheimer's disease (AD). In this paper, we propose a hybrid deep learning model for early diagnosis of MCI, called spatio-temporal convolutional gated recurrent unit network (STCGRU). METHODS The STCGRU comprises three bespoke convolutional neural network (CNN) modules and a bi-directional gated recurrent unit (BiGRU) module, which can effectively extract the spatial and temporal features of EEG and obtain excellent diagnostic results. We use a publicly available EEG dataset that has not undergone pre-processing to verify the robustness and accuracy of the model. Ablation experiments on STCGRU are conducted to showcase the individual performance improvement of each module. RESULTS Compared with other state-of-the-art approaches using the same publicly available EEG dataset, the results show that STCGRU is more suitable for early diagnosis of MCI. After 10-fold cross-validation, the average classification accuracy of the hybrid model reached 99.95 %, while the average kappa value reached 0.9989. CONCLUSIONS The experimental results show that the hybrid model proposed in this paper can directly extract compelling spatio-temporal features from the raw EEG data for classification. The STCGRU allows for accurate diagnosis of patients with MCI and has a high practical value.
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Affiliation(s)
- Hao Zhou
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Liyong Yin
- The First Hospital of Qinhuangdao, Qinhuangdao, PR China
| | - Rui Su
- Hebei Medical University, Shijiazhuang, PR China
| | - Ying Zhang
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Yi Yuan
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Ping Xie
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Xin Li
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China.
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Said A, Göker H. Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals. Cogn Neurodyn 2024; 18:597-614. [PMID: 38699612 PMCID: PMC11061085 DOI: 10.1007/s11571-023-10010-y] [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: 05/17/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 05/05/2024] Open
Abstract
Mild cognitive impairment (MCI) is a neuropsychological syndrome that is characterized by cognitive impairments. It typically affects adults 60 years of age and older. It is a noticeable decline in the cognitive function of the patient, and if left untreated it gets converted to Alzheimer's disease (AD). For that reason, early diagnosis of MCI is important as it slows down the conversion of the disease to AD. Early and accurate diagnosis of MCI requires recognition of the clinical characteristics of the disease, extensive testing, and long-term observations. These observations and tests can be subjective, expensive, incomplete, or inaccurate. Electroencephalography (EEG) is a powerful choice for the diagnosis of diseases with its advantages such as being non-invasive, based on findings, less costly, and getting results in a short time. In this study, a new EEG-based model is developed which can effectively detect MCI patients with higher accuracy. For this purpose, a dataset consisting of EEG signals recorded from a total of 34 subjects, 18 of whom were MCI and 16 control groups was used, and their ages ranged from 40 to 77. To conduct the experiment, the EEG signals were denoised using Multiscale Principal Component Analysis (MSPCA), and to increase the size of the dataset Data Augmentation (DA) method was performed. The tenfold cross-validation method was used to validate the model, moreover, the power spectral density (PSD) of the EEG signals was extracted from the EEG signals using three spectral analysis methods, the periodogram, welch, and multitaper. The PSD graphs of the EEG signals showed signal differences between the subjects of control and the MCI group, indicating that the signal power of MCI patients is lower compared to control groups. To classify the subjects, one of the best classifiers of deep learning algorithms called the Bi-directional long-short-term-memory (Bi-LSTM) was used, and several machine learning algorithms, such as decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN). These algorithms were trained and tested using the extracted feature vectors from the control and the MCI groups. Additionally, the values of the coefficient matrix of those algorithms were compared and evaluated with the performance evaluation matrix to determine which one performed the best overall. According to the experimental results, the proposed deep learning model of multitaper spectral analysis approach with Bi-LSTM deep learning algorithm attained the highest number of correctly classified samples for diagnosing MCI patients and achieved a remarkable accuracy compared to the other proposed models. The achieved classification results of the deep learning model are reported to be 98.97% accuracy, 98.34% sensitivity, 99.67% specificity, 99.70% precision, 99.02% f1 score, and 97.94% Matthews correlation coefficient (MCC).
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Affiliation(s)
- Afrah Said
- Department of Electrical Electronics Engineering, Faculty of Simav Technology, Dumlupınar University, 43500 Kütahya, Turkey
| | - Hanife Göker
- Health Services Vocational College, Gazi University, 06830 Ankara, Turkey
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Nyholm J, Ghazi AN, Ghazi SN, Sanmartin Berglund J. Prediction of dementia based on older adults' sleep disturbances using machine learning. Comput Biol Med 2024; 171:108126. [PMID: 38342045 DOI: 10.1016/j.compbiomed.2024.108126] [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: 11/01/2023] [Revised: 12/14/2023] [Accepted: 02/06/2024] [Indexed: 02/13/2024]
Abstract
BACKGROUND The most common degenerative condition in older adults is dementia, which can be predicted using a number of indicators and whose progression can be slowed down. One of the indicators of an increased risk of dementia is sleep disturbances. This study aims to examine if machine learning can predict dementia and which sleep disturbance factors impact dementia. METHODS This study uses five machine learning algorithms (gradient boosting, logistic regression, gaussian naive Bayes, random forest and support vector machine) and data on the older population (60+) in Sweden from the Swedish National Study on Ageing and Care - Blekinge (n=4175). Each algorithm uses 10-fold stratified cross-validation to obtain the results, which consist of the Brier score for checking accuracy and the feature importance for examining the factors which impact dementia. The algorithms use 16 features which are on personal and sleep disturbance factors. RESULTS Logistic regression found an association between dementia and sleep disturbances. However, it is slight for the features in the study. Gradient boosting was the most accurate algorithm with 92.9% accuracy, 0.926 f1-score, 0.974 ROC AUC and 0.056 Brier score. The significant factors were different in each machine learning algorithm. If the person sleeps more than two hours during the day, their sex, education level, age, waking up during the night and if the person snores are the variables that most consistently have the highest feature importance in all algorithms. CONCLUSION There is an association between sleep disturbances and dementia, which machine learning algorithms can predict. Furthermore, the risk factors for dementia are different across the algorithms, but sleep disturbances can predict dementia.
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Affiliation(s)
- Joel Nyholm
- Department of Computer Science, Blekinge Institute of Technology, Karlskrona, 37179, Blekinge, Sweden
| | - Ahmad Nauman Ghazi
- Department of Software Engineering, Blekinge Institute of Technology, Karlskrona, 37179, Blekinge, Sweden.
| | - Sarah Nauman Ghazi
- Department of Health, Blekinge Institute of Technology, Karlskrona, 37179, Blekinge, Sweden
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Huang L, Li Q, Lu Y, Pan F, Cui L, Wang Y, Miao Y, Chen T, Li Y, Wu J, Chen X, Jia J, Guo Q. Consensus on rapid screening for prodromal Alzheimer's disease in China. Gen Psychiatr 2024; 37:e101310. [PMID: 38313393 PMCID: PMC10836380 DOI: 10.1136/gpsych-2023-101310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/19/2023] [Indexed: 02/06/2024] Open
Abstract
Alzheimer's disease (AD) is a common cause of dementia, characterised by cerebral amyloid-β deposition, pathological tau and neurodegeneration. The prodromal stage of AD (pAD) refers to patients with mild cognitive impairment (MCI) and evidence of AD's pathology. At this stage, disease-modifying interventions should be used to prevent the progression to dementia. Given the inherent heterogeneity of MCI, more specific biomarkers are needed to elucidate the underlying AD's pathology. Although the uses of cerebrospinal fluid and positron emission tomography are widely accepted methods for detecting AD's pathology, their clinical applications are limited by their high costs and invasiveness, particularly in low-income areas in China. Therefore, to improve the early detection of Alzheimer's disease (AD) pathology through cost-effective screening methods, a panel of 45 neurologists, psychiatrists and gerontologists was invited to establish a formal consensus on the screening of pAD in China. The supportive evidence and grades of recommendations are based on a systematic literature review and focus group discussion. National meetings were held to allow participants to review, vote and provide their expert opinions to reach a consensus. A majority (two-thirds) decision was used for questions for which consensus could not be reached. Recommended screening methods are presented in this publication, including neuropsychological assessment, peripheral biomarkers and brain imaging. In addition, a general workflow for screening pAD in China is established, which will help clinicians identify individuals at high risk and determine therapeutic targets.
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Affiliation(s)
- Lin Huang
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qinjie Li
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yao Lu
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fengfeng Pan
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Liang Cui
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Wang
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ya Miao
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianlu Chen
- Center for Translational Medicine and Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yatian Li
- Shanghai BestCovered, Shanghai, China
| | | | - Xiaochun Chen
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jianping Jia
- Department of Neurology, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Wirian YB, Jiang Y, Cerel-Suhl S, Suhl J, Cheng Q. Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment. THE 4TH JOINT INTERNATIONAL CONFERENCE ON DEEP LEARNING, BIG DATA AND BLOCKCHAIN (DBB 2023). JOINT INTERNATIONAL CONFERENCE ON DEEP LEARNING, BIG DATA AND BLOCKCHAIN (4TH : 2023 : MARRAKECH, MOROCCO ; ONLINE) 2023; 768:81-90. [PMID: 38939181 PMCID: PMC11210370 DOI: 10.1007/978-3-031-42317-8_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
Medical data are often multi-modal, which are collected from different sources with different formats, such as text, images, and audio. They have some intrinsic connections in meaning and semantics while manifesting disparate appearances. Polysomnography (PSG) datasets are multi-modal data that include hypnogram, electrocardiogram (ECG), and electroencephalogram (EEG). It is hard to measure the associations between different modalities. Previous studies have used PSG datasets to study the relationship between sleep disorders and quality and sleep architecture. We leveraged a new method of deep learning manifold alignment to explore the relationship between sleep architecture and EEG features. Our analysis results agreed with the results of previous studies that used PSG datasets to diagnose different sleep disorders and monitor sleep quality in different populations. The method could effectively find the associations between sleep architecture and EEG datasets, which are important for understanding the changes in sleep stages and brain activity. On the other hand, the Spearman correlation method, which is a common statistical technique, could not find the correlations between these datasets.
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Affiliation(s)
| | - Yang Jiang
- Behavioral Science Department, University of Kentucky, Lexington, KY 40536, USA
| | - Sylvia Cerel-Suhl
- Sleep Center, Lexington Veterans Affairs Medical Center, Lexington, KY 40511, USA
| | - Jeremiah Suhl
- Sleep Center, Lexington Veterans Affairs Medical Center, Lexington, KY 40511, USA
| | - Qiang Cheng
- Computer Science Department, University of Kentucky, Lexington, KY 40536, USA
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Wu R, Li A, Xue C, Chai J, Qiang Y, Zhao J, Wang L. Screening for Mild Cognitive Impairment with Speech Interaction Based on Virtual Reality and Wearable Devices. Brain Sci 2023; 13:1222. [PMID: 37626578 PMCID: PMC10452416 DOI: 10.3390/brainsci13081222] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/13/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023] Open
Abstract
Significant advances in sensor technology and virtual reality (VR) offer new possibilities for early and effective detection of mild cognitive impairment (MCI), and this wealth of data can improve the early detection and monitoring of patients. In this study, we proposed a non-invasive and effective MCI detection protocol based on electroencephalogram (EEG), speech, and digitized cognitive parameters. The EEG data, speech data, and digitized cognitive parameters of 86 participants (44 MCI patients and 42 healthy individuals) were monitored using a wearable EEG device and a VR device during the resting state and task (the VR-based language task we designed). Regarding the features selected under different modality combinations for all language tasks, we performed leave-one-out cross-validation for them using four different classifiers. We then compared the classification performance under multimodal data fusion using features from a single language task, features from all tasks, and using a weighted voting strategy, respectively. The experimental results showed that the collaborative screening of multimodal data yielded the highest classification performance compared to single-modal features. Among them, the SVM classifier using the RBF kernel obtained the best classification results with an accuracy of 87%. The overall classification performance was further improved using a weighted voting strategy with an accuracy of 89.8%, indicating that our proposed method can tap into the cognitive changes of MCI patients. The MCI detection scheme based on EEG, speech, and digital cognitive parameters proposed in this study provides a new direction and support for effective MCI detection, and suggests that VR and wearable devices will be a promising direction for easy-to-perform and effective MCI detection, offering new possibilities for the exploration of VR technology in the field of language cognition.
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Affiliation(s)
- Ruixuan Wu
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Aoyu Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Chen Xue
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Jiali Chai
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Yan Qiang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Juanjuan Zhao
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
- College of Information, Jinzhong College of Information, Jinzhong 030600, China
| | - Long Wang
- College of Information, Jinzhong College of Information, Jinzhong 030600, China
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