Use of Deep Belief Network Model to Discriminate Mild Cognitive Impairment and Normal Controls Based on EEG, Eye Movement Signals and Neuropsychological Tests

Jiang, Jihai; Yan, Zhuangzhi; Shen, Ting; Xu, Gang; Guan, Qinglan; Yu, Zhihua

doi:10.1166/jmihi.2019.2825

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…This combined screening model can automatically complete the diagnostic tasks in just 5 min and suit for large-scale disease diagnosis, which is more efficient than the traditional lengthy test methods. Based on the above research, Jiang et al (2019b) team further developed a Deep Belief Network (DBN) model ( Figures 8ii ) for more efficient early detection of MCI patients. DBN network consists of one input layer, several hidden layers, and one output layer with the functions of learning features and achieving classification.…”

Section: Applicationsmentioning

confidence: 99%

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Current development of biosensing technologies towards diagnosis of mental diseases

Zheng

Liu

Lai

et al. 2023

Front. Bioeng. Biotechnol.

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The biosensor is an instrument that converts the concentration of biomarkers into electrical signals for detection. Biosensing technology is non-invasive, lightweight, automated, and biocompatible in nature. These features have significantly advanced medical diagnosis, particularly in the diagnosis of mental disorder in recent years. The traditional method of diagnosing mental disorders is time-intensive, expensive, and subject to individual interpretation. It involves a combination of the clinical experience by the psychiatrist and the physical symptoms and self-reported scales provided by the patient. Biosensors on the other hand can objectively and continually detect disease states by monitoring abnormal data in biomarkers. Hence, this paper reviews the application of biosensors in the detection of mental diseases, and the diagnostic methods are divided into five sub-themes of biosensors based on vision, EEG signal, EOG signal, and multi-signal. A prospective application in clinical diagnosis is also discussed.

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Section: Applicationsmentioning

confidence: 99%

“…The neuropsychological, EEG, and eye movement feature vectors were input. The features of raw input vectors were learned through RBM1 and RBM2 in order to classify MCI and healthy samples ( Jiang et al, 2019b ).…”

Section: Applicationsmentioning

confidence: 99%

Current development of biosensing technologies towards diagnosis of mental diseases

Zheng

Liu

Lai

et al. 2023

Front. Bioeng. Biotechnol.

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show abstract

Multiband decomposition and spectral discriminative analysis for motor imagery BCI via deep neural network

Wang

Zhou

et al. 2022

Front. Comput. Sci.

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Improvement and Application of Deep Belief Network Based on Sparrow Search Algorithm

Xie

2021

2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA)

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Use of Deep Belief Network Model to Discriminate Mild Cognitive Impairment and Normal Controls Based on EEG, Eye Movement Signals and Neuropsychological Tests

Cited by 5 publications

References 35 publications

Current development of biosensing technologies towards diagnosis of mental diseases

Current development of biosensing technologies towards diagnosis of mental diseases

Multiband decomposition and spectral discriminative analysis for motor imagery BCI via deep neural network

Improvement and Application of Deep Belief Network Based on Sparrow Search Algorithm

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