Computer-Aided Dementia Diagnosis Based on Hierarchical Extreme Learning Machine

Wang, Zhongyang; Xin, Junchang; Wang, Zhiqiong; Gu, Huizi; Zhao, Yue; Qian, Wei

doi:10.1007/s12559-019-09708-1

Cited by 9 publications

(4 citation statements)

References 37 publications

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“…Data redundancy reduction methods are often applied to fMRI data; these can be categorized as methods based on common spatial pattern (CSP) or brain functional network (BFN). CSF-based methods produce spatial filters that maximize one group's variance while minimizing another [184]. BFN-based methods use ROI segmentation to construct a brain network where the ROI features are vertices and the functional connections are edges.…”

Section: Functional Mri Datamentioning

confidence: 99%

“…In this study, the cost function sification is replaced by minimizing the sparsity of 𝑙𝑙 2 -normalized features of speci mensions. Bi, et al [258] combined a CNN with PCA-generated filters and k-mea tering for a fully unsupervised framework for clustering MRI of AD, MCI, and NC Xin, Wang, Gu, Zhao and Qian [184] hierarchically applied extreme learning mach unsupervised feature representation extraction. Extreme learning machines are a of feedforward neural networks that applies the Moore-Penrose generalized inv stead of gradient-based backpropagation.…”

Section: Restricted Boltzmann Machine (Rbm) and Other Unsupervised Me...mentioning

confidence: 99%

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A Survey of Deep Learning for Alzheimer’s Disease

Zhou

Wang

et al. 2023

MAKE

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Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.

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Section: Functional Mri Datamentioning

confidence: 99%

Section: Restricted Boltzmann Machine (Rbm) and Other Unsupervised Me...mentioning

confidence: 99%

A Survey of Deep Learning for Alzheimer’s Disease

Zhou

Wang

et al. 2023

MAKE

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“…Although deep learning is effective for feature extraction, existing deep learning approaches typically require manual definition of sizable parameters. However, recent studies had proven the effectiveness of using hierarchical extreme learning machines, which require less manual intervention and extract features much faster than traditional deep learning algorithms [255]. In addition, feature selection is an important yet challenging issue, since there are numerous features, a small amount of clinical data exists, and there are many similarities between selected features.…”

Section: Latest Research Concerning Ai-enhanced Eeg Analysismentioning

confidence: 99%

Global research on artificial intelligence-enhanced human electroencephalogram analysis

Chen

Tao

Wang

et al. 2021

Neural Comput & Applic

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The application of artificial intelligence (AI) technologies in assisting human electroencephalogram (EEG) analysis has become an active scientific field. This study aims to present a comprehensive review of the research field of AI-enhanced human EEG analysis. Using bibliometrics and topic modeling, research articles concerning AI-enhanced human EEG analysis collected from the Web of Science database during the period 2009-2018 were analyzed. After examining 2053 research articles published around the world, it was found that the annual number of articles had significantly grown from 78 to 468, with the USA and China being the most influential and prolific. The results of the keyword analysis showed that ''electroencephalogram,'' ''brain-computer interface,'' ''classification,'' ''support vector machine,'' ''electroencephalography,'' and ''signal'' were the most frequently used. The results of topic modeling and evolution analyses highlighted several important issues, including epileptic seizure detection, brain-machine interface, EEG classification, mental disorders, emotion, and alcoholism and anesthesia. The findings suggest that such visualization and analysis of the research articles could provide a comprehensive overview of the field for communities of practice and inquiry worldwide.

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“…In the last few years, different methods have been proposed to recognize cognitive issues based on artificial intelligence (AI) [17], and several AI-based solutions have been proposed to support active and healthy aging [18][19][20].…”

Section: Iot Techniques For Detecting Locomotion Anomaliesmentioning

confidence: 99%

TraMiner: Vision-Based Analysis of Locomotion Traces for Cognitive Assessment in Smart-Homes

2021

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The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of the elderly. In this work, we investigate the use of sensor data and deep learning to recognize those patterns in instrumented smart-homes. In order to get rid of the noise introduced by indoor constraints and activity execution, we introduce novel visual feature extraction methods for locomotion data. Our solution relies on locomotion trace segmentation, image-based extraction of salient features from locomotion segments, and vision-based deep learning. We carried out extensive experiments with a large dataset acquired in a smart-home test bed from 153 seniors, including people with cognitive diseases. Results show that our system can accurately recognize the cognitive status of the senior, reaching a macro-$$F_1$$ F 1 score of 0.873 for the three categories that we target: cognitive health, mild cognitive impairment, and dementia. Moreover, an experimental comparison shows that our system outperforms state-of-the-art methods.

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Computer-Aided Dementia Diagnosis Based on Hierarchical Extreme Learning Machine

Cited by 9 publications

References 37 publications

A Survey of Deep Learning for Alzheimer’s Disease

A Survey of Deep Learning for Alzheimer’s Disease

Global research on artificial intelligence-enhanced human electroencephalogram analysis

TraMiner: Vision-Based Analysis of Locomotion Traces for Cognitive Assessment in Smart-Homes

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