International journal of neural systems最新文献_第4页

A Modified Transformer Network for Seizure Detection Using EEG Signals. 利用脑电信号检测癫痫发作的改良变压器网络

International journal of neural systems Pub Date : 2024-11-19 DOI: 10.1142/S0129065725500030

Wenrong Hu, Juan Wang, Feng Li, Daohui Ge, Yuxia Wang, Qingwei Jia, Shasha Yuan

{"title":"A Modified Transformer Network for Seizure Detection Using EEG Signals.","authors":"Wenrong Hu, Juan Wang, Feng Li, Daohui Ge, Yuxia Wang, Qingwei Jia, Shasha Yuan","doi":"10.1142/S0129065725500030","DOIUrl":"10.1142/S0129065725500030","url":null,"abstract":"Seizures have a serious impact on the physical function and daily life of epileptic patients. The automated detection of seizures can assist clinicians in taking preventive measures for patients during the diagnosis process. The combination of deep learning (DL) model with convolutional neural network (CNN) and transformer network can effectively extract both local and global features, resulting in improved seizure detection performance. In this study, an enhanced transformer network named Inresformer is proposed for seizure detection, which is combined with Inception and Residual network extracting different scale features of electroencephalography (EEG) signals to enrich the feature representation. In addition, the improved transformer network replaces the existing Feedforward layers with two half-step Feedforward layers to enhance the nonlinear representation of the model. The proposed architecture utilizes discrete wavelet transform (DWT) to decompose the original EEG signals, and the three sub-bands are selected for signal reconstruction. Then, the Co-MixUp method is adopted to solve the problem of data imbalance, and the processed signals are sent to the Inresformer network for seizure information capture and recognition. Finally, discriminant fusion is performed on the results of three-scale EEG sub-signals to achieve final seizure recognition. The proposed network achieves the best accuracy of 100% on Bonn dataset and the average accuracy of 98.03%, sensitivity of 95.65%, and specificity of 98.57% on the long-term CHB-MIT dataset. Compared to the existing DL networks, the proposed method holds significant potential for clinical research and diagnosis applications with competitive performance.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550003"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering. 基于密度峰聚类的自适应近邻延迟尖峰神经膜系统

International journal of neural systems Pub Date : 2024-10-01 Epub Date: 2024-07-06 DOI: 10.1142/S0129065724500503

Qianqian Ren, Lianlian Zhang, Shaoyi Liu, Jin-Xing Liu, Junliang Shang, Xiyu Liu

{"title":"A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering.","authors":"Qianqian Ren, Lianlian Zhang, Shaoyi Liu, Jin-Xing Liu, Junliang Shang, Xiyu Liu","doi":"10.1142/S0129065724500503","DOIUrl":"10.1142/S0129065724500503","url":null,"abstract":"Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from high time complexity. Prior research suggests that clustering algorithms grounded in P systems can mitigate time complexity concerns. Within the realm of membrane systems (P systems), spiking neural P systems (SN P systems), inspired by biological nervous systems, are third-generation neural networks that possess intricate structures and offer substantial parallelism advantages. Thus, this study first improved the DPC by introducing the maximum nearest neighbor distance and K-nearest neighbors (KNN). Moreover, a method based on delayed spiking neural P systems (DSN P systems) was proposed to improve the performance of the algorithm. Subsequently, the DSNP-ANDPC algorithm was proposed. The effectiveness of DSNP-ANDPC was evaluated through comprehensive evaluations across four synthetic datasets and 10 real-world datasets. The proposed method outperformed the other comparison methods in most cases.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450050"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141556258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial-Temporal Dynamic Hypergraph Information Bottleneck for Brain Network Classification. 脑网络分类的时空动态超图信息瓶颈

International journal of neural systems Pub Date : 2024-10-01 Epub Date: 2024-07-17 DOI: 10.1142/S0129065724500539

Changxu Dong, Dengdi Sun

{"title":"Spatial-Temporal Dynamic Hypergraph Information Bottleneck for Brain Network Classification.","authors":"Changxu Dong, Dengdi Sun","doi":"10.1142/S0129065724500539","DOIUrl":"10.1142/S0129065724500539","url":null,"abstract":"Recently, Graph Neural Networks (GNNs) have gained widespread application in automatic brain network classification tasks, owing to their ability to directly capture crucial information in non-Euclidean structures. However, two primary challenges persist in this domain. First, within the realm of clinical neuro-medicine, signals from cerebral regions are inevitably contaminated with noise stemming from physiological or external factors. The construction of brain networks heavily relies on set thresholds and feature information within brain regions, making it susceptible to the incorporation of such noises into the brain topology. Additionally, the static nature of the artificially constructed brain network's adjacent structure restricts real-time changes in brain topology. Second, mainstream GNN-based approaches tend to focus solely on capturing information interactions of nearest neighbor nodes, overlooking high-order topology features. In response to these challenges, we propose an adaptive unsupervised Spatial-Temporal Dynamic Hypergraph Information Bottleneck (ST-DHIB) framework for dynamically optimizing brain networks. Specifically, adopting an information theory perspective, Graph Information Bottleneck (GIB) is employed for purifying graph structure, and dynamically updating the processed input brain signals. From a graph theory standpoint, we utilize the designed Hypergraph Neural Network (HGNN) and Bi-LSTM to capture higher-order spatial-temporal context associations among brain channels. Comprehensive patient-specific and cross-patient experiments have been conducted on two available datasets. The results demonstrate the advancement and generalization of the proposed framework.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450053"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Quality Assessment of Medical Images in Echocardiography Using Neural Networks with Adaptive Ranking and Structure-Aware Learning. 利用具有自适应排序和结构感知学习功能的神经网络自动评估超声心动图医学影像的质量

International journal of neural systems Pub Date : 2024-10-01 Epub Date: 2024-07-10 DOI: 10.1142/S0129065724500540

Gadeng Luosang, Zhihua Wang, Jian Liu, Fanxin Zeng, Zhang Yi, Jianyong Wang

{"title":"Automated Quality Assessment of Medical Images in Echocardiography Using Neural Networks with Adaptive Ranking and Structure-Aware Learning.","authors":"Gadeng Luosang, Zhihua Wang, Jian Liu, Fanxin Zeng, Zhang Yi, Jianyong Wang","doi":"10.1142/S0129065724500540","DOIUrl":"10.1142/S0129065724500540","url":null,"abstract":"The quality of medical images is crucial for accurately diagnosing and treating various diseases. However, current automated methods for assessing image quality are based on neural networks, which often focus solely on pixel distortion and overlook the significance of complex structures within the images. This study introduces a novel neural network model designed explicitly for automated image quality assessment that addresses pixel and semantic distortion. The model introduces an adaptive ranking mechanism enhanced with contrast sensitivity weighting to refine the detection of minor variances in similar images for pixel distortion assessment. More significantly, the model integrates a structure-aware learning module employing graph neural networks. This module is adept at deciphering the intricate relationships between an image's semantic structure and quality. When evaluated on two ultrasound imaging datasets, the proposed method outshines existing leading models in performance. Additionally, it boasts seamless integration into clinical workflows, enabling real-time image quality assessment, crucial for precise disease diagnosis and treatment.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450054"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141565421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Motion Artifact Detection for T1-Weighted Brain MR Images Using Convolutional Neural Networks. 利用卷积神经网络检测 T1 加权脑 MR 图像的运动伪影

International journal of neural systems Pub Date : 2024-10-01 Epub Date: 2024-07-12 DOI: 10.1142/S0129065724500527

Erik Roecher, Lucas Mösch, Jana Zweerings, Frank O Thiele, Svenja Caspers, Arnim Johannes Gaebler, Patrick Eisner, Pegah Sarkheil, Klaus Mathiak

{"title":"Motion Artifact Detection for T1-Weighted Brain MR Images Using Convolutional Neural Networks.","authors":"Erik Roecher, Lucas Mösch, Jana Zweerings, Frank O Thiele, Svenja Caspers, Arnim Johannes Gaebler, Patrick Eisner, Pegah Sarkheil, Klaus Mathiak","doi":"10.1142/S0129065724500527","DOIUrl":"10.1142/S0129065724500527","url":null,"abstract":"Quality assessment (QA) of magnetic resonance imaging (MRI) encompasses several factors such as noise, contrast, homogeneity, and imaging artifacts. Quality evaluation is often not standardized and relies on the expertise, and vigilance of the personnel, posing limitations especially with large datasets. Machine learning based on convolutional neural networks (CNNs) is a promising approach to address these challenges by performing automated inspection of MR images. In this study, a CNN for the detection of random head motion artifacts (RHM) in T1-weighted MRI as one aspect of image quality is proposed. A two-step approach aimed to first identify images exhibiting pronounced motion artifacts, and second to evaluate the feasibility of a more detailed three-class classification. The utilized dataset consisted of 420 T1-weighted whole-brain image volumes with isotropic resolution. Human experts assigned each volume to one of three classes of artifact prominence. Results demonstrate an accuracy of 95% for the identification of images with pronounced artifact load. The addition of an intermediate class retained an accuracy of 76%. The findings highlight the potential of CNN-based approaches to increase the efficiency of post-hoc QAs in large datasets by flagging images with potentially relevant artifact loads for closer inspection.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450052"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seizure Detection of EEG Signals Based on Multi-Channel Long- and Short-Term Memory-Like Spiking Neural Model. 基于多通道长短期记忆型尖峰神经模型的脑电信号癫痫发作检测。

International journal of neural systems Pub Date : 2024-10-01 Epub Date: 2024-07-13 DOI: 10.1142/S0129065724500515

Min Wu, Hong Peng, Zhicai Liu, Jun Wang

引用次数: 0

A Forward Learning Algorithm for Neural Memory Ordinary Differential Equations. 神经记忆常微分方程的前向学习算法

International journal of neural systems Pub Date : 2024-09-01 Epub Date: 2024-06-21 DOI: 10.1142/S0129065724500485

Xiuyuan Xu, Haiying Luo, Zhang Yi, Haixian Zhang

引用次数: 0

Abnormal Behavior Recognition Based on 3D Dense Connections. 基于三维密集连接的异常行为识别

International journal of neural systems Pub Date : 2024-09-01 Epub Date: 2024-06-25 DOI: 10.1142/S0129065724500497

Wei Chen, Zhanhe Yu, Chaochao Yang, Yuanyao Lu

{"title":"Abnormal Behavior Recognition Based on 3D Dense Connections.","authors":"Wei Chen, Zhanhe Yu, Chaochao Yang, Yuanyao Lu","doi":"10.1142/S0129065724500497","DOIUrl":"https://doi.org/10.1142/S0129065724500497","url":null,"abstract":"Abnormal behavior recognition is an important technology used to detect and identify activities or events that deviate from normal behavior patterns. It has wide applications in various fields such as network security, financial fraud detection, and video surveillance. In recent years, Deep Convolution Networks (ConvNets) have been widely applied in abnormal behavior recognition algorithms and have achieved significant results. However, existing abnormal behavior detection algorithms mainly focus on improving the accuracy of the algorithms and have not explored the real-time nature of abnormal behavior recognition. This is crucial to quickly identify abnormal behavior in public places and improve urban public safety. Therefore, this paper proposes an abnormal behavior recognition algorithm based on three-dimensional (3D) dense connections. The proposed algorithm uses a multi-instance learning strategy to classify various types of abnormal behaviors, and employs dense connection modules and soft-threshold attention mechanisms to reduce the model's parameter count and enhance network computational efficiency. Finally, redundant information in the sequence is reduced by attention allocation to mitigate its negative impact on recognition results. Experimental verification shows that our method achieves a recognition accuracy of 95.61% on the UCF-crime dataset. Comparative experiments demonstrate that our model has strong performance in terms of recognition accuracy and speed.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"34 9","pages":"2450049"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141621999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seizure Detection Based on Lightweight Inverted Residual Attention Network. 基于轻量级倒残留注意网络的癫痫发作检测

International journal of neural systems Pub Date : 2024-08-01 Epub Date: 2024-05-31 DOI: 10.1142/S0129065724500424

Hongbin Lv, Yongfeng Zhang, Tiantian Xiao, Ziwei Wang, Shuai Wang, Hailing Feng, Xianxun Zhao, Yanna Zhao

{"title":"Seizure Detection Based on Lightweight Inverted Residual Attention Network.","authors":"Hongbin Lv, Yongfeng Zhang, Tiantian Xiao, Ziwei Wang, Shuai Wang, Hailing Feng, Xianxun Zhao, Yanna Zhao","doi":"10.1142/S0129065724500424","DOIUrl":"10.1142/S0129065724500424","url":null,"abstract":"Timely and accurately seizure detection is of great importance for the diagnosis and treatment of epilepsy patients. Existing seizure detection models are often complex and time-consuming, highlighting the urgent need for lightweight seizure detection. Additionally, existing methods often neglect the key characteristic channels and spatial regions of electroencephalography (EEG) signals. To solve these issues, we propose a lightweight EEG-based seizure detection model named lightweight inverted residual attention network (LRAN). Specifically, we employ a four-stage inverted residual mobile block (iRMB) to effectively extract the hierarchical features from EEG. The convolutional block attention module (CBAM) is introduced to make the model focus on important feature channels and spatial information, thereby enhancing the discrimination of the learned features. Finally, convolution operations are used to capture local information and spatial relationships between features. We conduct intra-subject and inter-subject experiments on a publicly available dataset. Intra-subject experiments obtain 99.25% accuracy in segment-based detection and 0.36/h false detection rate (FDR) in event-based detection, respectively. Inter-subject experiments obtain 84.32% accuracy. Both sets of experiments maintain high classification accuracy with a low number of parameters, where the multiply accumulate operations (MACs) are 25.86[Formula: see text]M and the number of parameters is 0.57[Formula: see text]M.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450042"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bridging Imaging and Clinical Scores in Parkinson's Progression via Multimodal Self-Supervised Deep Learning. 通过多模态自监督深度学习连接帕金森病进展中的成像和临床评分

International journal of neural systems Pub Date : 2024-08-01 Epub Date: 2024-05-22 DOI: 10.1142/S0129065724500436

Francisco J Martinez-Murcia, Juan Eloy Arco, Carmen Jimenez-Mesa, Fermin Segovia, Ignacio A Illan, Javier Ramirez, Juan Manuel Gorriz

{"title":"Bridging Imaging and Clinical Scores in Parkinson's Progression via Multimodal Self-Supervised Deep Learning.","authors":"Francisco J Martinez-Murcia, Juan Eloy Arco, Carmen Jimenez-Mesa, Fermin Segovia, Ignacio A Illan, Javier Ramirez, Juan Manuel Gorriz","doi":"10.1142/S0129065724500436","DOIUrl":"10.1142/S0129065724500436","url":null,"abstract":"Neurodegenerative diseases pose a formidable challenge to medical research, demanding a nuanced understanding of their progressive nature. In this regard, latent generative models can effectively be used in a data-driven modeling of different dimensions of neurodegeneration, framed within the context of the manifold hypothesis. This paper proposes a joint framework for a multi-modal, common latent generative model to address the need for a more comprehensive understanding of the neurodegenerative landscape in the context of Parkinson's disease (PD). The proposed architecture uses coupled variational autoencoders (VAEs) to joint model a common latent space to both neuroimaging and clinical data from the Parkinson's Progression Markers Initiative (PPMI). Alternative loss functions, different normalization procedures, and the interpretability and explainability of latent generative models are addressed, leading to a model that was able to predict clinical symptomatology in the test set, as measured by the unified Parkinson's disease rating scale (UPDRS), with R2 up to 0.86 for same-modality and 0.441 cross-modality (using solely neuroimaging). The findings provide a foundation for further advancements in the field of clinical research and practice, with potential applications in decision-making processes for PD. The study also highlights the limitations and capabilities of the proposed model, emphasizing its direct interpretability and potential impact on understanding and interpreting neuroimaging patterns associated with PD symptomatology.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450043"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141072482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0