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Kang S, Kim SW, Seong JK. Disentangling brain atrophy heterogeneity in Alzheimer's disease: A deep self-supervised approach with interpretable latent space. Neuroimage 2024; 297:120737. [PMID: 39004409 DOI: 10.1016/j.neuroimage.2024.120737] [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: 03/06/2024] [Revised: 07/03/2024] [Accepted: 07/11/2024] [Indexed: 07/16/2024] Open
Abstract
Alzheimer's disease (AD) is heterogeneous, but existing methods for capturing this heterogeneity through dimensionality reduction and unsupervised clustering have limitations when it comes to extracting intricate atrophy patterns. In this study, we propose a deep learning based self-supervised framework that characterizes complex atrophy features using latent space representation. It integrates feature engineering, classification, and clustering to synergistically disentangle heterogeneity in Alzheimer's disease. Through this representation learning, we trained a clustered latent space with distinct atrophy patterns and clinical characteristics in AD, and replicated the findings in prodromal Alzheimer's disease. Moreover, we discovered that these clusters are not solely attributed to subtypes but also reflect disease progression in the latent space, representing the core dimensions of heterogeneity, namely progression and subtypes. Furthermore, longitudinal latent space analysis revealed two distinct disease progression pathways: medial temporal and parietotemporal pathways. The proposed approach enables effective latent representations that can be integrated with individual-level cognitive profiles, thereby facilitating a comprehensive understanding of AD heterogeneity.
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Affiliation(s)
- Sohyun Kang
- Department of Artificial Intelligence, College of Informatics, Korea University, Seoul, 02841, South Korea
| | - Sung-Woo Kim
- School of Biomedical Engineering, College of Health Science, Korea University, Seoul, 02841, South Korea; Department of Neurology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, 26426, South Korea; Research Institute of Metabolism and Inflammation, Yonsei University Wonju College of Medicine, Wonju, 26426, South Korea
| | - Joon-Kyung Seong
- Department of Artificial Intelligence, College of Informatics, Korea University, Seoul, 02841, South Korea; School of Biomedical Engineering, College of Health Science, Korea University, Seoul, 02841, South Korea; Interdisciplinary Program in Precision Public Health, College of Health Science, Korea University, Seoul, 02841, South Korea.
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2
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Xu X, Zhu G, Li B, Lin P, Li X, Wang Z. Automated diagnosis of schizophrenia based on spatial-temporal residual graph convolutional network. Biomed Eng Online 2024; 23:55. [PMID: 38886737 PMCID: PMC11181588 DOI: 10.1186/s12938-024-01250-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/31/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Schizophrenia (SZ), a psychiatric disorder for which there is no precise diagnosis, has had a serious impact on the quality of human life and social activities for many years. Therefore, an advanced approach for accurate treatment is required. NEW METHOD In this study, we provide a classification approach for SZ patients based on a spatial-temporal residual graph convolutional neural network (STRGCN). The model primarily collects spatial frequency features and temporal frequency features by spatial graph convolution and single-channel temporal convolution, respectively, and blends them both for the classification learning, in contrast to traditional approaches that only evaluate temporal frequency information in EEG and disregard spatial frequency features across brain regions. RESULTS We conducted extensive experiments on the publicly available dataset Zenodo and our own collected dataset. The classification accuracy of the two datasets on our proposed method reached 96.32% and 85.44%, respectively. In the experiment, the dataset using delta has the best classification performance in the sub-bands. COMPARISON WITH EXISTING METHODS Other methods mainly rely on deep learning models dominated by convolutional neural networks and long and short time memory networks, lacking exploration of the functional connections between channels. In contrast, the present method can treat the EEG signal as a graph and integrate and analyze the temporal frequency and spatial frequency features in the EEG signal. CONCLUSION We provide an approach to not only performs better than other classic machine learning and deep learning algorithms on the dataset we used in diagnosing schizophrenia, but also understand the effects of schizophrenia on brain network features.
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Affiliation(s)
- Xinyi Xu
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Geng Zhu
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Bin Li
- Shanghai Yangpu Mental Health Center, Shanghai, China
| | - Ping Lin
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Xiaoou Li
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China.
- Shanghai Yangpu Mental Health Center, Shanghai, China.
| | - Zhen Wang
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China.
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3
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Gao Y, Zhang C, Huang J, Meng M. EEG multi-domain feature transfer based on sparse regularized Tucker decomposition. Cogn Neurodyn 2024; 18:185-197. [PMID: 38406207 PMCID: PMC10881956 DOI: 10.1007/s11571-023-09936-0] [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: 08/14/2022] [Revised: 12/21/2022] [Accepted: 01/24/2023] [Indexed: 02/19/2023] Open
Abstract
Tensor analysis of electroencephalogram (EEG) can extract the activity information and the potential interaction between different brain regions. However, EEG data varies between subjects, and the existing tensor decomposition algorithms cannot guarantee that the features across subjects are distributed in the same domain, which leads to the non-objectivity of the classification result and analysis, In addition, traditional Tucker decomposition is prone to the explosion of feature dimensions. To solve these problems, combined with the idea of feature transfer, a novel EEG tensor transfer algorithm, Tensor Subspace Learning based on Sparse Regularized Tucker Decomposition (TSL-SRT), is proposed in this paper. In TSL-SRT, new EEG samples are considered as the target domain and original samples as the source domain. The target features can be obtained by projecting the target tensor to the source feature space to ensure that all features are in the same domain. Furthermore, to solve the problem of dimension explosion caused by TSL-SRT, a redundant EEG features screening algorithm is adopted to eliminate the redundant features, and achieves 77.8%, 73.2% and 75.3% accuracy on three BCI datasets. By visualizing the spatial basic matrix of the feature space, it can be seen that TSL-SRT is effective in extracting the features of active brain regions in the BCI task and it can extract the multi-domain features of different subjects in the same domain simultaneously, which provides a new method for the tensor analysis of EEG.
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Affiliation(s)
- Yunyuan Gao
- College of Automation, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang People’s Republic of China
- Zhejiang Key Laboratory of Brain Computer Collaborative Intelligence, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang People’s Republic of China
| | - Congrui Zhang
- College of Automation, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang People’s Republic of China
| | - Jincheng Huang
- HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang People’s Republic of China
| | - Ming Meng
- College of Automation, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang People’s Republic of China
- Zhejiang Key Laboratory of Brain Computer Collaborative Intelligence, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang People’s Republic of China
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4
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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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5
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Swarnalatha R. A Greedy Optimized Intelligent Framework for Early Detection of Alzheimer's Disease Using EEG Signal. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:4808841. [PMID: 36873383 PMCID: PMC9977523 DOI: 10.1155/2023/4808841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/29/2022] [Accepted: 10/10/2022] [Indexed: 02/24/2023]
Abstract
Recent researchers have been drawn to the analysis of electroencephalogram (EEG) signals in order to confirm the disease and severity range by viewing the EEG signal which has complicated the dataset. The conventional models such as machine learning, classifiers, and other mathematical models achieved the lowest classification score. The current study proposes to implement a novel deep feature with the best solution for EEG signal analysis and severity specification. A greedy sandpiper-based recurrent neural system (SbRNS) model for predicting Alzheimer's disease (AD) severity has been proposed. The filtered data are used as input for the feature analysis and the severity range is divided into three classes: low, medium, and high. The designed approach was then implemented in the matrix laboratory (MATLAB) system, and the effectiveness score was calculated using key metrics such as precision, recall, specificity, accuracy, and misclassification score. The validation results show that the proposed scheme achieved the best classification outcome.
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Affiliation(s)
- R. Swarnalatha
- Department of Electrical & Electronics Engineering, Birla Institute of Technology & Science, Pilani, Dubai Campus, Dubai, UAE
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6
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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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7
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Zhao H, Cao J, Xie J, Liao WH, Lei Y, Cao H, Qu Q, Bowen C. Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review. Digit Health 2023; 9:20552076231173569. [PMID: 37214662 PMCID: PMC10192816 DOI: 10.1177/20552076231173569] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 04/17/2023] [Indexed: 05/24/2023] Open
Abstract
Objective Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. Methods A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. Results The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. Conclusions This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.
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Affiliation(s)
- Huan Zhao
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Junyi Cao
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Junxiao Xie
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Wei-Hsin Liao
- Department of Mechanical and Automation
Engineering, The Chinese University of Hong
Kong, Shatin, N.T., Hong Kong, China
| | - Yaguo Lei
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi'an, P.R. China
| | - Hongmei Cao
- Department of Neurology, The First
Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China
| | - Qiumin Qu
- Department of Neurology, The First
Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China
| | - Chris Bowen
- Department of Mechanical Engineering, University of Bath, Bath, UK
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8
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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: 13] [Impact Index Per Article: 6.5] [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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Li X, Zhou T, Qiu S. Alzheimer's Disease Analysis Algorithm Based on No-threshold Recurrence Plot Convolution Network. Front Aging Neurosci 2022; 14:888577. [PMID: 35619941 PMCID: PMC9127346 DOI: 10.3389/fnagi.2022.888577] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 04/07/2022] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease is a neurological disorder characterized by progressive cognitive dysfunction and behavioral impairment that occurs in old. Early diagnosis and treatment of Alzheimer's disease is great significance. Electroencephalography (EEG) signals can be used to detect Alzheimer's disease due to its non-invasive advantage. To solve the problem of insufficient analysis by single-channel EEG signal, we analyze the relationship between multiple channels and build PLV framework. To solve the problem of insufficient representation of 1D signal, a threshold-free recursive plot convolution network was constructed to realize 2D representation. To solve the problem of insufficient EEG signal characterization, a fusion algorithm of clinical features and imaging features was proposed to detect Alzheimer's disease. Experimental results show that the algorithm has good performance and robustness.
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Affiliation(s)
- Xuemei Li
- School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu, China
| | - Tao Zhou
- School of Computer Science and Engineering, North Minzu University, Yinchuan, China
- *Correspondence: Tao Zhou
| | - Shi Qiu
- Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China
- Shi Qiu
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10
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Saravanakumar S, Saravanan T. An effective convolutional neural network-based stacked long short-term memory approach for automated Alzheimer’s disease prediction. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
In today’s world, Alzheimer’s Disease (AD) is one of the prevalent neurological diseases where early disease prediction can significantly enhance the compatibility of patient treatment. Nevertheless, accurate diagnosis and optimal feature selection play a vital challenge in AD detection. Most of the existing diagnosis systems failed to attain superior prediction accuracy and precision rate. In order to mitigate these constraints, a new efficient Convolutional Neural Network-based Stacked Long Short-Term Memory (CNN-SLSTM) methodology has been proposed in this paper. The key objective of the proposed model is to examine the brain’s condition and evaluate the changes that occur throughout the interracial period. The proposed model includes multi-feature learning and categorization in which the raw Electroencephalography (EEG) data will be passed via the feature extractor to decrease the computing complexity and execution time. Afterward, the SLSTM network is constructed with completely linked layer and activation layers to record the temporal relationship between features and the next stage of AD. The proposed CNN-SLSTM model can be trained using real-time EEG sensor data. The performance results clearly apparent that the proposed model can efficiently predict the AD with superior accuracy of 98.67% and precision of 98.86% when compared with existing state-of-the-art techniques.
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Affiliation(s)
- S. Saravanakumar
- Department of Computer Science and Engineering, Adithya Institute of Technology, Coimbatore, India
| | - T. Saravanan
- Department of Computer Science and Engineering, St Martins Engineering college Telangana, India
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11
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Deepa N, Chokkalingam S. Optimization of VGG16 utilizing the Arithmetic Optimization Algorithm for early detection of Alzheimer’s disease. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103455] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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12
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Unlocking the Memory Component of Alzheimer’s Disease:Biological Processes and Pathways across Brain Regions. Biomolecules 2022; 12:biom12020263. [PMID: 35204764 PMCID: PMC8961579 DOI: 10.3390/biom12020263] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 01/26/2022] [Accepted: 02/02/2022] [Indexed: 02/04/2023] Open
Abstract
Alzheimer’s Disease (AD) is a neurodegenerative disorder characterized by a progressive loss of memory and a general cognitive decline leading to dementia. AD is characterized by changes in the behavior of the genome and can be traced across multiple brain regions and cell types. It is mainly associated with β-amyloid deposits and tau protein misfolding, leading to neurofibrillary tangles. In recent years, however, research has shown that there is a high complexity of mechanisms involved in AD neurophysiology and functional decline enabling its diverse presentation and allowing more questions to arise. In this study, we present a computational approach to facilitate brain region-specific analysis of genes and biological processes involved in the memory process in AD. Utilizing current genetic knowledge we provide a gene set of 265 memory-associated genes in AD, combinations of which can be found co-expressed in 11 different brain regions along with their functional role. The identified genes participate in a spectrum of biological processes ranging from structural and neuronal communication to epigenetic alterations and immune system responses. These findings provide new insights into the molecular background of AD and can be used to bridge the genotype–phenotype gap and allow for new therapeutic hypotheses.
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