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A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders

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Biomedical Signal Processing and Control 2022年第PartB期72卷 103370-103370页

作者： Lei, Yu Belkacem, Abdelkader Nasreddine Wang, Xiaotian Sha, Sha Wang, Changming Chen, Chao Beijing Institute of Basic Medical Sciences Beijing China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates School of Artificial Intelligence Xidian University Xian China National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China Brain-inspired Intelligence and Clinical Translational Research Center/Department of Neurosurgery Xuanwu Hospital Capitap Medical University Beijing China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China

Background: Early and accurate diagnosis of bipolar and major depressive disorders is important in clinical practice. However, no diagnostic biomarkers can discriminate bipolar from major depressive disorder with high accuracy at present. Methods: We propose a novel convolutional neural network architecture using multichannel raw resting-state electroencephalograph signals to differentiate bipolar disorder from major depressive disorder. This method has great potential in diagnosing mental disorders. In total, 101 patients with major depressive disorder, 82 patients with bipolar disorder, and 81 healthy controls were assessed. Clinical diagnosis was performed by psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Participants were instructed to fix their eyes on a cross on the monitor during the collection of resting-state electroencephalograph signals. Results: A classification mean accuracy of 96.88% was achieved using the resting-state electroencephalograph signals with the 10-fold cross-validation method. The results indicate that deep neural networks could learn efficient feature patterns automatically without manual feature selections. Both statistical analysis and feature visualization on different brain regions showed that the classification performance and the learned features of the proposed model were consistent with that obtained in neurobiological studies on bipolar and major depressive disorders. Conclusions: The combined use of deep neural networks and electroencephalograph signals is an effective approach for the computer-aided diagnosis of bipolar and major depressive disorders. © 2021 Elsevier Ltd

关键词： Deep neural networks

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Dynamical phases of short-term memory mechanisms in RNNs

arXiv

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arXiv 2025年

作者： Kurtkaya, Bariscan Dinc, Fatih Yuksekgonul, Mert Blanco-Pozo, Marta Cirakman, Ege Schnitzer, Mark Yemez, Yucel Tanaka, Hidenori Yuan, Peng Miolane, Nina CNC Program Stanford University Stanford United States KUIS AI Center Department of Computer Engineering Koc University Turkey Geometric Intelligence Lab UC Santa Barbara Santa Barbara United States Kavli Institute for Theoretical Physics UC Santa Barbara Santa Barbara United States Stanford University Stanford United States James H. Clark Center for Biomedical Engineering & Sciences Stanford University Stanford United States Howard Hughes Medical Institute Stanford University Stanford United States Phi Lab NTT Research Sunnyvale United States Center for Brain Science Harvard University Cambridge United States State Key Laboratory of Medical Neurobiology Institute for Translational Brain Research MOE Frontiers Center for Brain Science Fudan University China Research Institute of Intelligent Complex System Fudan University China

Short-term memory is essential for cognitive processing, yet our understanding of its neural mechanisms remains unclear. A key focus in neuroscience has been the study of sequential activity patterns, where neurons fire one after another within large networks, to explain how information is maintained. While recurrent connections were shown to drive sequential dynamics, a mechanistic understanding of this process still remains unknown. In this work, we first introduce two unique mechanisms that can subserve short-term memory: slow-point manifolds generating direct sequences or limit cycles providing temporally localized approximations. Then, through analytical models, we identify fundamental properties that govern the selection of these mechanisms, i.e., we derive theoretical scaling laws for critical learning rates as a function of the delay period length, beyond which no learning is possible. We empirically verify these observations by training and evaluating more than 35,000 recurrent neural networks (RNNs) that we will publicly release upon publication. Overall, our work provides new insights into short-term memory mechanisms and proposes experimentally testable predictions for systems neuroscience. © 2025, CC BY.

关键词： Long short-term memory

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Optimization IIR Filter Design Using Symbiotic Organism Search Algorithm

Optimization IIR Filter Design Using Symbiotic Organism Sear...

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2019 International Conference on Modeling, Simulation and Big Data Analysis（MSBDA 2019）

作者： Cao Han Yongquan Zhou School of Computer Electronics and InformationGuangxi University College of Information Science and Engineering Guangxi University for Nationalities Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence

The possible multi-modal error surface is a challenge to the IIR filter design,which needs a flexible,easy to implement and stable global optimization algorithm to provide filter coefficient *** Symbiotic Organism Search(SOS) algorithm is an effect global optimization algorithm which is proposed by Min-yuan Cheng in *** simulates the symbiotic interaction between different populations in *** this paper,the IIR filter design in system recognition field based on the SOS algorithm will be *** IIR filters in different structures were used for *** simulation results will be compared with some well-known algorithms that also provide solutions for IIR *** comparative results show that:the design solution provided by the SOS algorithm is superior to Compared algorithms.

关键词： Symbiotic organism search algorithm IIR filter Global optimization

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Classification of Cognitive Ability of Healthy Elderly Individuals Using Resting-State Functional Connectivity Magnetic Resonance Imaging and An Extreme Learning Machine

Research Square

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Research Square 2021年

作者： Yang, Zekun Ge, Manling Abdelkader, Nasreddine Belkacem Fu, Xiaoxuan Zhang, Qirui Chen, Shenghua Xie, Chong Song, Zibo Chen, Chao State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Hebei Province Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability Hebei University of Technology Tianjin300130 China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain15551 United Arab Emirates Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Harvard Medical School CharlestownMA02129 United States Department of Medical Imaging Jinling Hospital Nanjing University School of Medicine General Hospital of Eastern Theater Nanjing210002 China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300003 China

Purpose Quantitative determination of the correlation between cognitive ability and functional biomarkers in the elderly brain is essential. To identify biomarkers associated with cognitive performance in the elderly, this study combined an index model specific for resting-state functional connectivity (FC) magnetic resonance imaging (fcMRI) with a supervised machine learning method. Methods Performance scores on conventional cognitive test batteries and MRI data were obtained for 98 healthy elderly individuals and 90 healthy youth from two public databases. Based on the test scores, the elderly cohort was categorized into two groups: excellent and poor. An fcMRI index model was constructed for each elderly individual to determine the relative differences in FC among brain regions compared with that in the youth cohort. Brain areas sensitive to test scores could then be identified using the fcMRI indexes. To confirm the effectiveness of constructed model, the fcMRI indexes of these brain areas were used as feature matrix inputs for training an extreme learning machine. Classification accuracy was then tested in separate groups and confirmed by N-fold cross-validation. Results This learning study could effectively classify the cognitive status of healthy elderly individuals according to frontal lobe, temporal lobe, and parietal lobe FC values with a mean accuracy of 83.5%, which is substantially higher than that achieved using conventional correlation analysis. Conclusion This fcMRI classification study may facilitate early detection of age-related cognitive decline as well as help reveal the underlying pathological mechanisms. © 2021, CC BY.

关键词： Magnetic resonance imaging

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An enhanced monarch butterfly optimization with self-adaptive butterfly adjusting and crossover operators 9th

An enhanced monarch butterfly optimization with self-adaptiv...

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9th International Conference on Swarm intelligence, ICSI 2018

作者： Wang, Gai-Ge Hao, Guo-Sheng Cui, Zhihua Department of Computer Science and Technology Ocean University of China Qingdao266100 China School of Computer Science and Technology Jiangsu Normal University XuzhouJiangsu China Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology TaiyuanShanxi030024 China

ISBN: (纸本)9783319938141

After studying the behavior of monarch butterflies in nature, Wang et al. proposed a new promising swarm intelligence algorithm, called monarch butterfly optimization (MBO), for addressing unconstrained optimization tasks. In the basic MBO algorithm, the fixed butterfly adjusting rate is used to carry out the butterfly adjusting operator. In this paper, the self-adaptive strategy is introduced to adjust the butterfly adjusting rate. In addition, the crossover operator that is generally used in evolutionary algorithms (EAs) is used to further improve the quality of butterfly individuals. The two optimization strategies, self-adaptive and crossover operator, are combined, and then self-adaptive crossover operator is proposed. After incorporating the above strategies into the basic MBO algorithm, a new version of MBO algorithm, called Self-adaptive Monarch Butterfly Optimization (SaMBO), is put forward. Also, few studies of constrained optimization has been done for MBO research. In this paper, in order to verify the performance of our proposed SaMBO algorithm, the proposed SaMBO algorithm is further benchmarked by 21 CEC 2017 constrained optimization problems. The experimental results indicate that the proposed SaMBO algorithm outperforms the basic MBO and other five state-of-the-art metaheuristic algorithms. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Constrained optimization

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Optimal parameter settings for bat algorithm

Optimal parameter settings for bat algorithm

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作者： Xue, Fei Cai, Yongquan Cao, Yang Cui, Zhihua Li, Feixiang College of Computer Science and Technology Beijing University of Technology Beijing100124 China Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi030024 China

Bat algorithm is a swarm intelligent algorithm inspired by the echolocation behaviour of bats. Recently, there are many research works focus on this algorithm, however, most of them are with different parameter settings when dealing with un-constrained numerical problems. In this paper, one optimal parameter setting for bat algorithm is discussed with orthogonal experimental design, and tested on CEC 2013 competition problems. To test the performance, the optimal setting is used to compare with other four bat algorithms. Simulation results show the validity. © 2015 Inderscience Enterprises Ltd.

关键词： Parameter estimation

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A co-evolutionary particle swarm optimization with dynamic topology for solving multi-objective optimization problems

Advances in Modelling and Analysis A

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Advances in Modelling and Analysis A 2016年第1期53卷 145-159页

作者： Wu, Daqing Tang, Lixiang Li, Haiyan Ouyang, LiJun Computer Science and Technology Institute University of South China HangyangHunan China Antai College of Economics and Management Shanghai Jiao Tong University Shanghai200240 China Zigong643000 China Key Laboratory of Guangxi High Schools for Complex System and Computational Intelligence Guangxi University for Nationalities Nanning530006 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University HefeiAnhui Province230039 China Department of Business Administration Hunan University of Finance and Economics Hunan410205 China

This paper proposes a multi-objective with dynamic topology particle swarm optimization (PSO) algorithm for solving multi-objective problems, named DTPSO. One of the main drawbacks of classical multi-objective particle swarm optimization algorithm is low diversity. To overcome this disadvantage, DTPSO uses two dynamic local best particles to lead the search particles with multiple populations to deal with multiple objectives, and maintains diversity of new found non-dominated solutions via partitioned the searching space into fixed number of cells. The proposed DTPSO is validated through comparisons with other two multi-objective algorithms using established benchmarks and metrics. Simulation results demonstrated that DTPSO shows competitive, if not better, performance as compared to the other algorithms. © 2016, AMSE Press. All rights reserved.

关键词： Multiobjective optimization

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Different force laws driving artificial physics optimisation algorithm for constrained optimisation problem

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International Journal of Wireless and Mobile Computing 2015年第3期9卷 290-299页

作者： Xie, Liping Zeng, Jianchao Yin, Jian Division of Industrial and System Engineering Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology No. 66 Waliu Road Wanbailin District Taiyuan Shanxi030024 China Institute of Computer Science and Technology Taiyuan University of Science and Technology No. 66 Waliu Road Wanbailin District Taiyuan Shanxi030024 China

Inspired by physical force, Artificial Physics Optimisation (APO) algorithm is a novel stochastic based on a physicomimetics framework. Driven by virtual force, a population of sample individuals searches for a global optimum in the problem space. The force law is a key problem associated with the performance of APO algorithm significantly. In the paper, an APO algorithm with the Feasibility and Dominance (FAD) method (FAD-APO) is employed to solve constrained optimisation problems. Three different force laws are constructed between the feasible individuals and infeasible individuals, which drive all individuals to search for a global optimum in the constrained problem space. Simulation results show that FAD3-APO algorithm may generally perform better than FAD1-APO and FAD2-APO;it is the most stable and effective among the three versions of FAD-APO algorithms. Meanwhile, a comparison with other populationbased heuristics shows that the FAD-APO algorithm is competitive on some test function. © 2015 Inderscience Enterprises Ltd.

关键词： Constrained optimization

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A special issue on wireless and mobile computing in sensor networks

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Sensor Letters 2014年第2期12卷 197-200页

作者： Cui, Zhihua Complex System and Computational Intelligence Laboratory School of Computer Science and Technology Taiyuan University of Science and Technology Wanbailin District Taiyuan Shanxi China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

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Centric bat algorithm for toy model of protein folding

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Sensor Letters 2014年第9期12卷 1388-1392页

作者： Xue, Fei Cai, Yongquan Cui, Zhihua College of Computer Science and Technology Beijing University of Technology Beijing China Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu China

The structure prediction of Toy model of protein folding is one challenging problem in Bioinformatics. Up to now, many computational intelligent algorithms have been applied to it. However, due to the amount of local optima, the accuracy is not well. In this paper, one new algorithm, centric bat algorithm is applied to solve this problem, to further improve the performance, a local search strategy, Limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) is also employed. The experimental results show it can predicts the protein folding structure precisely.

关键词： Protein folding

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