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Abdelmissih S, Hosny SA, Elwi HM, Sayed WM, Eshra MA, Shaker OG, Samir NF. Chronic Caffeine Consumption, Alone or Combined with Agomelatine or Quetiapine, Reduces the Maximum EEG Peak, As Linked to Cortical Neurodegeneration, Ovarian Estrogen Receptor Alpha, and Melatonin Receptor 2. Psychopharmacology (Berl) 2024:10.1007/s00213-024-06619-4. [PMID: 38842700 DOI: 10.1007/s00213-024-06619-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 05/16/2024] [Indexed: 06/07/2024]
Abstract
RATIONALE Evidence of the effects of chronic caffeine (CAFF)-containing beverages, alone or in combination with agomelatine (AGO) or quetiapine (QUET), on electroencephalography (EEG), which is relevant to cognition, epileptogenesis, and ovarian function, remains lacking. Estrogenic, adenosinergic, and melatonergic signaling is possibly linked to the dynamics of these substances. OBJECTIVES The brain and ovarian effects of CAFF were compared with those of AGO + CAFF and QUET + CAFF. The implications of estrogenic, adenosinergic, and melatonergic signaling and the brain-ovarian crosstalk were investigated. METHODS Adult female rats were administered AGO (10 mg/kg), QUET (10 mg/kg), CAFF, AGO + CAFF, or QUET + CAFF, once daily for 8 weeks. EEG, estrous cycle progression, and microstructure of the brain and ovaries were examined. Brain and ovarian 17β-estradiol (E2), antimullerian hormone (AMH), estrogen receptor alpha (E2Rα), adenosine receptor 2A (A2AR), and melatonin receptor 2 (MT2R) were assessed. RESULTS CAFF, alone or combined with AGO or QUET, reduced the maximum EEG peak, which was positively linked to ovarian E2Rα, negatively correlated to cortical neurodegeneration and ovarian MT2R, and associated with cystic ovaries. A large corpus luteum emerged with AGO + CAFF and QUET + CAFF, antagonizing the CAFF-mediated increased ovarian A2AR and reduced cortical E2Rα. AGO + CAFF provoked TTP delay and increased ovarian AMH, while QUET + CAFF slowed source EEG frequency to δ range and increased brain E2. CONCLUSIONS CAFF treatment triggered brain and ovarian derangements partially antagonized with concurrent AGO or QUET administration but with no overt affection of estrus cycle progression. Estrogenic, adenosinergic, and melatonergic signaling and brain-ovarian crosstalk may explain these effects.
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Affiliation(s)
- Sherine Abdelmissih
- Department of Medical Pharmacology, Faculty of Medicine Kasr Al-Ainy, Cairo University, Cairo, Egypt.
| | - Sara Adel Hosny
- Department of Medical Histology, Faculty of Medicine Kasr Al-Ainy, Cairo University, Cairo, Egypt
| | - Heba M Elwi
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine Kasr Al-Ainy, Cairo University, Cairo, Egypt
| | - Walaa Mohamed Sayed
- Department of Anatomy and Embryology, Faculty of Medicine Kasr Al-Ainy, Cairo University, Cairo, Egypt
| | - Mohamed Ali Eshra
- Department of Medical Physiology, Faculty of Medicine Kasr Al-Ainy, Cairo University, Cairo, Egypt
| | - Olfat Gamil Shaker
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine Kasr Al-Ainy, Cairo University, Cairo, Egypt
| | - Nancy F Samir
- Department of Medical Physiology, Faculty of Medicine Kasr Al-Ainy, Cairo University, Cairo, Egypt
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Hata M, Miyazaki Y, Mori K, Yoshiyama K, Akamine S, Kanemoto H, Gotoh S, Omori H, Hirashima A, Satake Y, Suehiro T, Takahashi S, Ikeda M. Utilizing portable electroencephalography to screen for pathology of Alzheimer's disease: a methodological advancement in diagnosis of neurodegenerative diseases. Front Psychiatry 2024; 15:1392158. [PMID: 38855641 PMCID: PMC11157607 DOI: 10.3389/fpsyt.2024.1392158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024] Open
Abstract
Background The current biomarker-supported diagnosis of Alzheimer's disease (AD) is hindered by invasiveness and cost issues. This study aimed to address these challenges by utilizing portable electroencephalography (EEG). We propose a novel, non-invasive, and cost-effective method for identifying AD, using a sample of patients with biomarker-verified AD, to facilitate early and accessible disease screening. Methods This study included 35 patients with biomarker-verified AD, confirmed via cerebrospinal fluid sampling, and 35 age- and sex-balanced healthy volunteers (HVs). All participants underwent portable EEG recordings, focusing on 2-minute resting-state EEG epochs with closed eyes state. EEG recordings were transformed into scalogram images, which were analyzed using "vision Transformer(ViT)," a cutting-edge deep learning model, to differentiate patients from HVs. Results The application of ViT to the scalogram images derived from portable EEG data demonstrated a significant capability to distinguish between patients with biomarker-verified AD and HVs. The method achieved an accuracy of 73%, with an area under the receiver operating characteristic curve of 0.80, indicating robust performance in identifying AD pathology using neurophysiological measures. Conclusions Our findings highlight the potential of portable EEG combined with advanced deep learning techniques as a transformative tool for screening of biomarker-verified AD. This study not only contributes to the neurophysiological understanding of AD but also opens new avenues for the development of accessible and non-invasive diagnostic methods. The proposed approach paves the way for future clinical applications, offering a promising solution to the limitations of advanced diagnostic practices for dementia.
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Affiliation(s)
- Masahiro Hata
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yuki Miyazaki
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kohji Mori
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kenji Yoshiyama
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Shoshin Akamine
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hideki Kanemoto
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Shiho Gotoh
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hisaki Omori
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Psychiatry, Esaka Hospital, Osaka, Japan
| | - Atsuya Hirashima
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Psychiatry, Esaka Hospital, Osaka, Japan
| | - Yuto Satake
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Takashi Suehiro
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Shun Takahashi
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Occupational Therapy, Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka, Japan
- Clinical Research and Education Center, Asakayama General Hospital, Osaka, Japan
- Department of Neuropsychiatry, Wakayama Medical University, Wakayama, Japan
| | - Manabu Ikeda
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
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Chen Y, Wang H, Zhang D, Zhang L, Tao L. Multi-feature fusion learning for Alzheimer's disease prediction using EEG signals in resting state. Front Neurosci 2023; 17:1272834. [PMID: 37822349 PMCID: PMC10563817 DOI: 10.3389/fnins.2023.1272834] [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: 08/04/2023] [Accepted: 09/06/2023] [Indexed: 10/13/2023] Open
Abstract
Introduction Diagnosing Alzheimer's disease (AD) lesions via visual examination of Electroencephalography (EEG) signals poses a considerable challenge. This has prompted the exploration of deep learning techniques, such as Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs), for AD prediction. However, the classification performance of CNN-based methods has often been deemed inadequate. This is primarily attributed to CNNs struggling with extracting meaningful lesion signals from the complex and noisy EEG data. Methods In contrast, ViTs have demonstrated proficiency in capturing global signal patterns. In light of these observations, we propose a novel approach to enhance AD risk assessment. Our proposition involves a hybrid architecture, merging the strengths of CNNs and ViTs to compensate for their respective feature extraction limitations. Our proposed Dual-Branch Feature Fusion Network (DBN) leverages both CNN and ViT components to acquire texture features and global semantic information from EEG signals. These elements are pivotal in capturing dynamic electrical signal changes in the cerebral cortex. Additionally, we introduce Spatial Attention (SA) and Channel Attention (CA) blocks within the network architecture. These attention mechanisms bolster the model's capacity to discern abnormal EEG signal patterns from the amalgamated features. To make well-informed predictions, we employ a two-factor decision-making mechanism. Specifically, we conduct correlation analysis on predicted EEG signals from the same subject to establish consistency. Results This is then combined with results from the Clinical Neuropsychological Scale (MMSE) assessment to comprehensively evaluate the subject's susceptibility to AD. Our experimental validation on the publicly available OpenNeuro database underscores the efficacy of our approach. Notably, our proposed method attains an impressive 80.23% classification accuracy in distinguishing between AD, Frontotemporal dementia (FTD), and Normal Control (NC) subjects. Discussion This outcome outperforms prevailing state-of-the-art methodologies in EEG-based AD prediction. Furthermore, our methodology enables the visualization of salient regions within pathological images, providing invaluable insights for interpreting and analyzing AD predictions.
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Affiliation(s)
- Yonglin Chen
- Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Hefei, China
- School of Computer Science and Technology, Anhui University, Hefei, China
| | - Huabin Wang
- Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Hefei, China
- School of Computer Science and Technology, Anhui University, Hefei, China
| | - Dailei Zhang
- Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Hefei, China
| | - Liping Zhang
- Faculty of Engineering, Malaysia School of Engineering, Monash University, Kuala Lumpur, Malaysia
| | - Liang Tao
- Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Hefei, China
- School of Computer Science and Technology, Anhui University, Hefei, China
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AlSharabi K, Salamah YB, Aljalal M, Abdurraqeeb AM, Alturki FA. EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques. Front Hum Neurosci 2023; 17:1190203. [PMID: 37719771 PMCID: PMC10501399 DOI: 10.3389/fnhum.2023.1190203] [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: 03/20/2023] [Accepted: 08/14/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction Despite the existence of numerous clinical techniques for identifying neurological brain disorders in their early stages, Electroencephalogram (EEG) data shows great promise as a means of detecting Alzheimer's disease (AD) at an early stage. The main goal of this research is to create a reliable and accurate clinical decision support system leveraging EEG signal processing to detect AD in its initial phases. Methods The research utilized a dataset consisting of 35 neurotypical individuals, 31 patients with mild AD, and 22 patients with moderate AD. Data were collected while participants were at rest. To extract features from the EEG signals, a band-pass filter was applied to the dataset and the Empirical Mode Decomposition (EMD) technique was employed to decompose the filtered signals. The EMD technique was then leveraged to generate feature vectors by combining multiple signal features, thereby enhancing diagnostic performance. Various artificial intelligence approaches were also explored and compared to identify features of the extracted EEG signals distinguishing mild AD, moderate AD, and neurotypical cases. The performance of the classifiers was evaluated using k-fold cross-validation and leave-one-subject-out (LOSO) cross-validation methods. Results The results of this study provided valuable insights into potential avenues for the early diagnosis of AD. The performance of the various offered methodologies has been compared and evaluated by computing the overall diagnosis precision, recall, and accuracy. The proposed methodologies achieved a maximum classification accuracy of 99.9 and 94.8% for k-fold and LOSO cross-validation techniques, respectively. Conclusion The study aims to assess and compare different proposed methodologies and determine the most effective combination approach for the early detection of AD. Our research findings strongly suggest that the proposed diagnostic support technique is a highly promising supplementary tool for discovering prospective diagnostic biomarkers that can greatly aid in the early clinical diagnosis of AD.
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Affiliation(s)
- Khalil AlSharabi
- Electrical Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi Arabia
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Parreño Torres A, Roncero-Parra C, Borja AL, Mateo-Sotos J. Inter-Hospital Advanced and Mild Alzheimer's Disease Classification Based on Electroencephalogram Measurements via Classical Machine Learning Algorithms. J Alzheimers Dis 2023; 95:1667-1683. [PMID: 37718814 DOI: 10.3233/jad-230525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
BACKGROUND In pursuit of diagnostic tools capable of targeting distinct stages of Alzheimer's disease (AD), this study explores the potential of electroencephalography (EEG) combined with machine learning (ML) algorithms to identify patients with mild or moderate AD (ADM) and advanced AD (ADA). OBJECTIVE This study aims to assess the classification accuracy of six classical ML algorithms using a dataset of 668 patients from multiple hospitals. METHODS The dataset comprised measurements obtained from 668 patients, distributed among control, ADM, and ADA groups, collected from five distinct hospitals between 2011 and 2022. For classification purposes, six classical ML algorithms were employed: support vector machine, Bayesian linear discriminant analysis, decision tree, Gaussian Naïve Bayes, K-nearest neighbor and random forest. RESULTS The RF algorithm exhibited outstanding performance, achieving a remarkable balanced accuracy of 93.55% for ADA classification and 93.25% for ADM classification. The consistent reliability in distinguishing ADA and ADM patients underscores the potential of the EEG-based approach for AD diagnosis. CONCLUSIONS By leveraging a dataset sourced from multiple hospitals and encompassing a substantial patient cohort, coupled with the straightforwardness of the implemented models, it is feasible to attain notably robust results in AD classification.
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Affiliation(s)
| | | | - Alejandro L Borja
- School of Industrial Engineering, University of Castilla-La Mancha, Albacete, Spain
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Faldu KG, Shah JS. Alzheimer's disease: a scoping review of biomarker research and development for effective disease diagnosis. Expert Rev Mol Diagn 2022; 22:681-703. [PMID: 35855631 DOI: 10.1080/14737159.2022.2104639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Alzheimer's disease (AD) is regarded as the foremost reason for neurodegeneration that prominently affects the geriatric population. Characterized by extracellular accumulation of amyloid-beta (Aβ), intracellular aggregation of hyperphosphorylated tau (p-tau), and neuronal degeneration that causes impairment of memory and cognition. Amyloid/tau/neurodegeneration (ATN) classification is utilized for research purposes and involves amyloid, tau, and neuronal injury staging through MRI, PET scanning, and CSF protein concentration estimations. CSF sampling is invasive, and MRI and PET scanning requires sophisticated radiological facilities which limit its widespread diagnostic use. ATN classification lacks effectiveness in preclinical AD. AREAS COVERED This publication intends to collate and review the existing biomarker profile and the current research and development of a new arsenal of biomarkers for AD pathology from different biological samples, microRNA (miRNA), proteomics, metabolomics, artificial intelligence, and machine learning for AD screening, diagnosis, prognosis, and monitoring of AD treatments. EXPERT OPINION It is an accepted observation that AD-related pathological changes occur over a long period of time before the first symptoms are observed providing ample opportunity for detection of biological alterations in various biological samples that can aid in early diagnosis and modify treatment outcomes.
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Affiliation(s)
- Khushboo Govind Faldu
- Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat, India
| | - Jigna Samir Shah
- Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat, India
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Perez-Valero E, Morillas C, Lopez-Gordo MA, Carrera-Muñoz I, López-Alcalde S, Vílchez-Carrillo RM. An Automated Approach for the Detection of Alzheimer's Disease From Resting State Electroencephalography. Front Neuroinform 2022; 16:924547. [PMID: 35898959 PMCID: PMC9309796 DOI: 10.3389/fninf.2022.924547] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 06/20/2022] [Indexed: 12/05/2022] Open
Abstract
Early detection is crucial to control the progression of Alzheimer's disease and to postpone intellectual decline. Most current detection techniques are costly, inaccessible, or invasive. Furthermore, they require laborious analysis, what delays the start of medical treatment. To overcome this, researchers have recently investigated AD detection based on electroencephalography, a non-invasive neurophysiology technique, and machine learning algorithms. However, these approaches typically rely on manual procedures such as visual inspection, that requires additional personnel for the analysis, or on cumbersome EEG acquisition systems. In this paper, we performed a preliminary evaluation of a fully-automated approach for AD detection based on a commercial EEG acquisition system and an automated classification pipeline. For this purpose, we recorded the resting state brain activity of 26 participants from three groups: mild AD, mild cognitive impairment (MCI-non-AD), and healthy controls. First, we applied automated data-driven algorithms to reject EEG artifacts. Then, we obtained spectral, complexity, and entropy features from the preprocessed EEG segments. Finally, we assessed two binary classification problems: mild AD vs. controls, and MCI-non-AD vs. controls, through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best reported in literature, what suggests that AD detection could be automatically detected through automated processing and commercial EEG systems. This is promising, since it may potentially contribute to reducing costs related to AD screening, and to shortening detection times, what may help to advance medical treatment.
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Affiliation(s)
- Eduardo Perez-Valero
- Department of Computers Architecture and Technology, University of Granada, Granada, Spain
- Brain Computer Interface Laboratory, Research Center for Information and Communications Technologies, University of Granada, Granada, Spain
| | - Christian Morillas
- Department of Computers Architecture and Technology, University of Granada, Granada, Spain
- Brain Computer Interface Laboratory, Research Center for Information and Communications Technologies, University of Granada, Granada, Spain
| | - Miguel A. Lopez-Gordo
- Brain Computer Interface Laboratory, Research Center for Information and Communications Technologies, University of Granada, Granada, Spain
- Department of Signal Theory, Telematics, and Communications, University of Granada, Granada, Spain
- *Correspondence: Miguel A. Lopez-Gordo
| | - Ismael Carrera-Muñoz
- Cognitive Neurology Group, Hospital Universitario Virgen de las Nieves, Granada, Spain
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Shan X, Cao J, Huo S, Chen L, Sarrigiannis PG, Zhao Y. Spatial-temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram. Hum Brain Mapp 2022; 43:5194-5209. [PMID: 35751844 PMCID: PMC9812255 DOI: 10.1002/hbm.25994] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/19/2022] [Accepted: 06/08/2022] [Indexed: 01/15/2023] Open
Abstract
Functional connectivity of the human brain, representing statistical dependence of information flow between cortical regions, significantly contributes to the study of the intrinsic brain network and its functional mechanism. To fully explore its potential in the early diagnosis of Alzheimer's disease (AD) using electroencephalogram (EEG) recordings, this article introduces a novel dynamical spatial-temporal graph convolutional neural network (ST-GCN) for better classification performance. Different from existing studies that are based on either topological brain function characteristics or temporal features of EEG, the proposed ST-GCN considers both the adjacency matrix of functional connectivity from multiple EEG channels and corresponding dynamics of signal EEG channel simultaneously. Different from the traditional graph convolutional neural networks, the proposed ST-GCN makes full use of the constrained spatial topology of functional connectivity and the discriminative dynamic temporal information represented by the 1D convolution. We conducted extensive experiments on the clinical EEG data set of AD patients and Healthy Controls. The results demonstrate that the proposed method achieves better classification performance (92.3%) than the state-of-the-art methods. This approach can not only help diagnose AD but also better understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on resting-state EEG.
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Affiliation(s)
- Xiaocai Shan
- Institute of Geology and GeophysicsChinese Academy of SciencesBeijingChina,School of Aerospace, Transport and ManufacturingCranfield UniversityCranfieldUK
| | - Jun Cao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfieldUK
| | - Shoudong Huo
- Institute of Geology and GeophysicsChinese Academy of SciencesBeijingChina
| | - Liangyu Chen
- Department of NeurosurgeryShengjing Hospital of China Medical UniversityShenyangChina
| | | | - Yifan Zhao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfieldUK
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9
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Perez-Valero E, Lopez-Gordo MÁ, Gutiérrez CM, Carrera-Muñoz I, Vílchez-Carrillo RM. A self-driven approach for multi-class discrimination in Alzheimer's disease based on wearable EEG. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 220:106841. [PMID: 35523023 DOI: 10.1016/j.cmpb.2022.106841] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/25/2022] [Accepted: 04/25/2022] [Indexed: 06/14/2023]
Abstract
Early detection is critical to control Alzheimer's disease (AD) progression and postpone cognitive decline. Traditional medical procedures such as magnetic resonance imaging are costly, involve long waiting lists, and require complex analysis. Alternatively, for the past years, researchers have successfully evaluated AD detection approaches based on machine learning and electroencephalography (EEG). Nonetheless, these approaches frequently rely upon manual processing or involve non-portable EEG hardware. These aspects are suboptimal regarding automated diagnosis, since they require additional personnel and hinder portability. In this work, we report the preliminary evaluation of a self-driven AD multi-class discrimination approach based on a commercial EEG acquisition system using sixteen channels. For this purpose, we recorded the EEG of three groups of participants: mild AD, mild cognitive impairment (MCI) non-AD, and controls, and we implemented a self-driven analysis pipeline to discriminate the three groups. First, we applied automated artifact rejection algorithms to the EEG recordings. Then, we extracted power, entropy, and complexity features from the preprocessed epochs. Finally, we evaluated a multi-class classification problem using a multi-layer perceptron through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best in literature (0.88 F1-score), what suggests that AD can potentially be detected through a self-driven approach based on commercial EEG and machine learning. We believe this work and further research could contribute to opening the door for the detection of AD in a single consultation session, therefore reducing the costs associated to AD screening and potentially advancing medical treatment.
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Affiliation(s)
- Eduardo Perez-Valero
- Department of Computer Architecture and Technology, University of Granada, Spain; Brain-Computer Interfaces Laboratory, Research Centre for Information and Communications Technologies, University of Granada, Spain
| | - Miguel Ángel Lopez-Gordo
- Department of Signal Theory, Telematics and Communications, University of Granada, Spain; Brain-Computer Interfaces Laboratory, Research Centre for Information and Communications Technologies, University of Granada, Spain.
| | - Christian Morillas Gutiérrez
- Department of Computer Architecture and Technology, University of Granada, Spain; Brain-Computer Interfaces Laboratory, Research Centre for Information and Communications Technologies, University of Granada, Spain
| | - Ismael Carrera-Muñoz
- Cognitive Neurology Group, Neurology Unit, Hospital Universitario Virgen de las Nieves, Granada, Spain
| | - Rosa M Vílchez-Carrillo
- Cognitive Neurology Group, Neurology Unit, Hospital Universitario Virgen de las Nieves, Granada, Spain
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10
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Wu EQ, Peng XY, Chen SD, Zhao XY, Tang ZR. Detecting Alzheimer’s Dementia Degree. IEEE Trans Cogn Dev Syst 2022. [DOI: 10.1109/tcds.2020.3015131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Karami V, Nittari G, Traini E, Amenta F. An Optimized Decision Tree with Genetic Algorithm Rule-Based Approach to Reveal the Brain's Changes During Alzheimer's Disease Dementia. J Alzheimers Dis 2021; 84:1577-1584. [PMID: 34719494 DOI: 10.3233/jad-210626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND It is desirable to achieve acceptable accuracy for computer aided diagnosis system (CADS) to disclose the dementia-related consequences on the brain. Therefore, assessing and measuring these impacts is fundamental in the diagnosis of dementia. OBJECTIVE This study introduces a new CADS for deep learning of magnetic resonance image (MRI) data to identify changes in the brain during Alzheimer's disease (AD) dementia. METHODS The proposed algorithm employed a decision tree with genetic algorithm rule-based optimization to classify input data which were extracted from MRI. This pipeline is applied to the healthy and AD subjects of the Open Access Series of Imaging Studies (OASIS). RESULTS Final evaluation of the CADS and its comparison with other systems supported the potential of the proposed model as a novel tool for investigating the progression of AD and its great ability as an innovative computerized help to facilitate the decision-making procedure for the diagnosis of AD. CONCLUSION The one-second time response, together with the identified high accurate performance, suggests that this system could be useful in future cognitive and computational neuroscience studies.
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Affiliation(s)
- Vania Karami
- Department of Neurology and Neurosurgery, MontrealNeurological Institute-Hospital (MNI), McGill University, Montreal, Canada
| | - Giulio Nittari
- School of PharmaceuticalSciences and Health Products, University of Camerino, Camerino (MC), Italy
| | - Enea Traini
- School of PharmaceuticalSciences and Health Products, University of Camerino, Camerino (MC), Italy
| | - Francesco Amenta
- School of PharmaceuticalSciences and Health Products, University of Camerino, Camerino (MC), Italy
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Gouw AA, Hillebrand A, Schoonhoven DN, Demuru M, Ris P, Scheltens P, Stam CJ. Routine magnetoencephalography in memory clinic patients: A machine learning approach. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12227. [PMID: 34568539 PMCID: PMC8449227 DOI: 10.1002/dad2.12227] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 05/12/2021] [Accepted: 06/04/2021] [Indexed: 11/06/2022]
Abstract
INTRODUCTION We report the routine application of magnetoencephalography (MEG) in a memory clinic, and its value in the discrimination of patients with Alzheimer's disease (AD) dementia from controls. METHODS Three hundred sixty-six patients visiting our memory clinic underwent MEG recording. Source-reconstructed MEG data were visually assessed and evaluated in the context of clinical findings and other diagnostic markers. We analyzed the diagnostic accuracy of MEG spectral measures in the discrimination of individual AD dementia patients (n = 40) from subjective cognitive decline (SCD) patients (n = 40) using random forest models. RESULTS Best discrimination was obtained using a combination of relative theta and delta power (accuracy 0.846, sensitivity 0.855, specificity 0.837). The results were validated in an independent cohort. Hippocampal and thalamic regions, besides temporal-occipital lobes, contributed considerably to the model. DISCUSSION MEG has been implemented successfully in the workup of memory clinic patients and has value in diagnostic decision-making.
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Affiliation(s)
- Alida A. Gouw
- Alzheimer Center and Department of Neurology, VU University medical center, Amsterdam UMCAmsterdamThe Netherlands
- Department of Clinical Neurophysiology and MEG CenterNeuroscience Campus AmsterdamVU University Medical CenterAmsterdam UMCAmsterdamThe Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG CenterNeuroscience Campus AmsterdamVU University Medical CenterAmsterdam UMCAmsterdamThe Netherlands
| | - Deborah N. Schoonhoven
- Alzheimer Center and Department of Neurology, VU University medical center, Amsterdam UMCAmsterdamThe Netherlands
- Department of Clinical Neurophysiology and MEG CenterNeuroscience Campus AmsterdamVU University Medical CenterAmsterdam UMCAmsterdamThe Netherlands
| | - Matteo Demuru
- Alzheimer Center and Department of Neurology, VU University medical center, Amsterdam UMCAmsterdamThe Netherlands
- Department of Clinical Neurophysiology and MEG CenterNeuroscience Campus AmsterdamVU University Medical CenterAmsterdam UMCAmsterdamThe Netherlands
| | - Peterjan Ris
- Department of Clinical Neurophysiology and MEG CenterNeuroscience Campus AmsterdamVU University Medical CenterAmsterdam UMCAmsterdamThe Netherlands
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, VU University medical center, Amsterdam UMCAmsterdamThe Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG CenterNeuroscience Campus AmsterdamVU University Medical CenterAmsterdam UMCAmsterdamThe Netherlands
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Tăuţan AM, Ionescu B, Santarnecchi E. Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques. Artif Intell Med 2021; 117:102081. [PMID: 34127244 DOI: 10.1016/j.artmed.2021.102081] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/21/2021] [Accepted: 04/26/2021] [Indexed: 10/21/2022]
Abstract
Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.
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Affiliation(s)
- Alexandra-Maria Tăuţan
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Bogdan Ionescu
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Harvard Medical School, 330 Brookline Avenue, Boston, United States.
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14
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Amini M, Pedram MM, Moradi A, Ouchani M. Diagnosis of Alzheimer's Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5511922. [PMID: 33981355 PMCID: PMC8088352 DOI: 10.1155/2021/5511922] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 03/26/2021] [Accepted: 04/07/2021] [Indexed: 12/22/2022]
Abstract
Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation of the duration of cerebral cortex electrical activity to be registered by postsynaptic potential. Therefore, in this paper, the time-dependent power spectrum descriptors are used to diagnose the electroencephalogram signal function from three groups: mild cognitive impairment, Alzheimer's disease, and healthy control test samples. The final feature used in three modes of traditional classification methods is recorded: k-nearest neighbors, support vector machine, linear discriminant analysis approaches, and documented results. Finally, for Alzheimer's disease patient classification, the convolutional neural network architecture is presented. The results are indicated using output assessment. For the convolutional neural network approach, the accurate meaning of accuracy is 82.3%. 85% of mild cognitive impairment cases are accurately detected in-depth, but 89.1% of the Alzheimer's disease and 75% of the healthy population are correctly diagnosed. The presented convolutional neural network outperforms other approaches because performance and the k-nearest neighbors' approach is the next target. The linear discriminant analysis and support vector machine were at the low area under the curve values.
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Affiliation(s)
- Morteza Amini
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Shahid Beheshti University, Tehran, Iran
| | - Mir Mohsen Pedram
- Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Tehran, Iran
| | - AliReza Moradi
- Department of Clinical Psychology, Faculty of Psychology and Educational Science, Kharazmi University, Tehran, Iran
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - Mahshad Ouchani
- Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran
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15
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Perez-Valero E, Lopez-Gordo MA, Morillas C, Pelayo F, Vaquero-Blasco MA. A Review of Automated Techniques for Assisting the Early Detection of Alzheimer's Disease with a Focus on EEG. J Alzheimers Dis 2021; 80:1363-1376. [PMID: 33682717 DOI: 10.3233/jad-201455] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In this paper, we review state-of-the-art approaches that apply signal processing (SP) and machine learning (ML) to automate the detection of Alzheimer's disease (AD) and its prodromal stages. In the first part of the document, we describe the economic and social implications of the disease, traditional diagnosis techniques, and the fundaments of automated AD detection. Then, we present electroencephalography (EEG) as an appropriate alternative for the early detection of AD, owing to its reduced cost, portability, and non-invasiveness. We also describe the main time and frequency domain EEG features that are employed in AD detection. Subsequently, we examine some of the main studies of the last decade that aim to provide an automatic detection of AD and its previous stages by means of SP and ML. In these studies, brain data was acquired using multiple medical techniques such as magnetic resonance imaging, positron emission tomography, and EEG. The main aspects of each approach, namely feature extraction, classification model, validation approach, and performance metrics, are compiled and discussed. Lastly, a set of conclusions and recommendations for future research on AD automatic detection are drawn in the final section of the paper.
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Affiliation(s)
- Eduardo Perez-Valero
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Computer Architecture and Technology, University of Granada, Granada, Spain
| | - Miguel A Lopez-Gordo
- Department of Signal Theory, Telematics and Communications, University of Granada, Granada,Spain.,Nicolo Association, Churriana de la Vega, Spain
| | - Christian Morillas
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Computer Architecture and Technology, University of Granada, Granada, Spain
| | - Francisco Pelayo
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Computer Architecture and Technology, University of Granada, Granada, Spain
| | - Miguel A Vaquero-Blasco
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Signal Theory, Telematics and Communications, University of Granada, Granada,Spain
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16
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Safi MS, Safi SMM. Early detection of Alzheimer’s disease from EEG signals using Hjorth parameters. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102338] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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17
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Ikeda Y, Kikuchi M, Noguchi-Shinohara M, Iwasa K, Kameya M, Hirosawa T, Yoshita M, Ono K, Samuraki-Yokohama M, Yamada M. Spontaneous MEG activity of the cerebral cortex during eyes closed and open discriminates Alzheimer's disease from cognitively normal older adults. Sci Rep 2020; 10:9132. [PMID: 32499487 PMCID: PMC7272642 DOI: 10.1038/s41598-020-66034-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 05/13/2020] [Indexed: 12/13/2022] Open
Abstract
This study aimed to examine whether magnetoencephalography (MEG) is useful to detect early stage Alzheimer's disease (AD). We analyzed MEG data from the early stage AD group (n = 20; 6 with mild cognitive impairment due to AD and 14 with AD dementia) and cognitively normal control group (NC, n = 27). MEG was recorded during resting eyes closed (EC) and eyes open (EO), and the following 6 values for each of 5 bands (θ1: 4-6, θ2: 6-8, α1: 8-10, α2: 10-13, β: 13-20 Hz) in the cerebral 68 regions were compared between the groups: (1) absolute power during EC and (2) EO, (3) whole cerebral normalization (WCN) power during EC and (4) EO, (5) difference of the absolute powers between the EC and EO conditions (the EC-EO difference), and (6) WCN value of the EC-EO difference. We found significant differences between the groups in the WCN powers during the EO condition, and the EC-EO differences. Using a Support Vector Machine classifier, a discrimination accuracy of 83% was obtained and an AUC in an ROC analysis was 0.91. This study demonstrates that MEG during resting EC and EO is useful in discriminating between early stage AD and NC.
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Affiliation(s)
- Yoshihisa Ikeda
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Mitsuru Kikuchi
- Department of Psychiatry and Neurobiology, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Moeko Noguchi-Shinohara
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan.,Department of Preemptive Medicine for Dementia, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Kazuo Iwasa
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Masafumi Kameya
- Department of Psychiatry and Neurobiology, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Tetsu Hirosawa
- Department of Psychiatry and Neurobiology, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Mitsuhiro Yoshita
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan.,Department of Neurology, NHO Hokuriku National Hospital, Nanto, Japan
| | - Kenjiro Ono
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Miharu Samuraki-Yokohama
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Masahito Yamada
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan.
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Ieracitano C, Mammone N, Hussain A, Morabito FC. A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Netw 2020; 123:176-190. [DOI: 10.1016/j.neunet.2019.12.006] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 12/03/2019] [Accepted: 12/06/2019] [Indexed: 12/27/2022]
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19
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Musaeus CS, Engedal K, Høgh P, Jelic V, Mørup M, Naik M, Oeksengaard AR, Snaedal J, Wahlund LO, Waldemar G, Andersen BB. Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease. Clin Neurophysiol 2019; 130:1889-1899. [DOI: 10.1016/j.clinph.2019.07.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 05/26/2019] [Accepted: 07/03/2019] [Indexed: 12/27/2022]
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20
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Durongbhan P, Zhao Y, Chen L, Zis P, De Marco M, Unwin ZC, Venneri A, He X, Li S, Zhao Y, Blackburn DJ, Sarrigiannis PG. A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals. IEEE Trans Neural Syst Rehabil Eng 2019; 27:826-835. [DOI: 10.1109/tnsre.2019.2909100] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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21
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Khatun S, Morshed BI, Bidelman GM. A Single-Channel EEG-Based Approach to Detect Mild Cognitive Impairment via Speech-Evoked Brain Responses. IEEE Trans Neural Syst Rehabil Eng 2019; 27:1063-1070. [PMID: 30998476 DOI: 10.1109/tnsre.2019.2911970] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Mild cognitive impairment (MCI) is the preliminary stage of dementia, which may lead to Alzheimer's disease (AD) in the elderly people. Therefore, early detection of MCI has the potential to minimize the risk of AD by ensuring the proper mental health care before it is too late. In this paper, we demonstrate a single-channel EEG-based MCI detection method, which is cost-effective and portable, and thus suitable for regular home-based patient monitoring. We collected the scalp EEG data from 23 subjects, while they were stimulated with five auditory speech signals. The cognitive state of the subjects was evaluated by the Montreal cognitive assessment test (MoCA). We extracted 590 features from the event-related potential (ERP) of the collected EEG signals, which included time and spectral domain characteristics of the response. The top 25 features, ranked by the random forest method, were used for classification models to identify subjects with MCI. Robustness of our model was tested using leave-one-out cross-validation while training the classifiers. Best results (leave-one-out cross-validation accuracy 87.9%, sensitivity 84.8%, specificity 95%, and F score 85%) were obtained using support vector machine (SVM) method with radial basis kernel (RBF) (sigma = 10/cost = 102 ). Similar performances were also observed with logistic regression (LR), further validating the results. Our results suggest that single-channel EEG could provide a robust biomarker for early detection of MCI.
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22
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Sharma N, Kolekar M, Jha K, Kumar Y. EEG and Cognitive Biomarkers Based Mild Cognitive Impairment Diagnosis. Ing Rech Biomed 2019. [DOI: 10.1016/j.irbm.2018.11.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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23
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Cassani R, Estarellas M, San-Martin R, Fraga FJ, Falk TH. Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment. DISEASE MARKERS 2018; 2018:5174815. [PMID: 30405860 PMCID: PMC6200063 DOI: 10.1155/2018/5174815] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 07/12/2018] [Accepted: 07/29/2018] [Indexed: 12/17/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.
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Affiliation(s)
- Raymundo Cassani
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
| | - Mar Estarellas
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
- Department of Bioengineering, Imperial College London, London, UK
| | - Rodrigo San-Martin
- Center for Mathematics, Computation and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Francisco J. Fraga
- Engineering, Modeling and Applied Social Sciences Center, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Tiago H. Falk
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
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25
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Xia L, Malik AS, Subhani AR. A physiological signal-based method for early mental-stress detection. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.06.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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