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The 2024 Hojjat Adeli Award for Outstanding Contributions in Neural Systems. 公告:2024 年霍贾特-阿德利神经系统杰出贡献奖。
Pub Date : 2024-12-01 Epub Date: 2024-09-23 DOI: 10.1142/S012906572482001X
Han Sun
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引用次数: 0
A Lightweight Convolutional Neural Network-Reformer Model for Efficient Epileptic Seizure Detection. 用于高效癫痫发作检测的轻量级卷积神经网络-重构器模型
Pub Date : 2024-12-01 Epub Date: 2024-09-30 DOI: 10.1142/S0129065724500655
Haozhou Cui, Xiangwen Zhong, Haotian Li, Chuanyu Li, Xingchen Dong, Dezan Ji, Landi He, Weidong Zhou

A real-time and reliable automatic detection system for epileptic seizures holds significant value in assisting physicians with rapid diagnosis and treatment of epilepsy. Aiming to address this issue, a novel lightweight model called Convolutional Neural Network-Reformer (CNN-Reformer) is proposed for seizure detection on long-term EEG. The CNN-Reformer consists of two main parts: the Data Reshaping (DR) module and the Efficient Attention and Concentration (EAC) module. This framework reduces network parameters while retaining effective feature extraction of multi-channel EEGs, thereby improving model computational efficiency and real-time performance. Initially, the raw EEG signals undergo Discrete Wavelet Transform (DWT) for signal filtering, and then fed into the DR module for data compression and reshaping while preserving local features. Subsequently, these local features are sent to the EAC module to extract global features and perform categorization. Post-processing involving sliding window averaging, thresholding, and collar techniques is further deployed to reduce the false detection rate (FDR) and improve detection performance. On the CHB-MIT scalp EEG dataset, our method achieves an average sensitivity of 97.57%, accuracy of 98.09%, and specificity of 98.11% at segment-based level, and a sensitivity of 96.81%, along with FDR of 0.27/h, and latency of 17.81 s at the event-based level. On the SH-SDU dataset we collected, our method yielded segment-based sensitivity of 94.51%, specificity of 92.83%, and accuracy of 92.81%, along with event-based sensitivity of 94.11%. The average testing time for 1[Formula: see text]h of multi-channel EEG signals is 1.92[Formula: see text]s. The excellent results and fast computational speed of the CNN-Reformer model demonstrate its potential for efficient seizure detection.

一个实时可靠的癫痫发作自动检测系统在协助医生快速诊断和治疗癫痫方面具有重要价值。为了解决这一问题,我们提出了一种名为卷积神经网络-变形器(CNN-Reformer)的新型轻量级模型,用于长期脑电图的癫痫发作检测。CNN-Reformer 由两个主要部分组成:数据重塑(DR)模块和高效注意力与集中(EAC)模块。该框架在减少网络参数的同时,保留了多通道脑电图的有效特征提取,从而提高了模型的计算效率和实时性。最初,原始脑电信号经过离散小波变换(DWT)进行信号过滤,然后送入 DR 模块进行数据压缩和重塑,同时保留局部特征。随后,这些局部特征被传送到 EAC 模块,以提取全局特征并进行分类。后期处理包括滑动窗口平均、阈值和领圈技术,以降低误检率(FDR)并提高检测性能。在 CHB-MIT 头皮脑电图数据集上,我们的方法在基于片段的水平上实现了平均 97.57% 的灵敏度、98.09% 的准确度和 98.11% 的特异性,在基于事件的水平上实现了 96.81% 的灵敏度、0.27/h 的 FDR 和 17.81 秒的延迟。在我们收集的 SH-SDU 数据集上,我们的方法获得了基于分段的灵敏度 94.51%、特异度 92.83%、准确度 92.81%,以及基于事件的灵敏度 94.11%。1[公式:见正文]小时多通道脑电信号的平均测试时间为 1.92[公式:见正文]秒。CNN-Reformer 模型的出色结果和快速计算速度证明了它在高效癫痫发作检测方面的潜力。
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引用次数: 0
Referring Image Segmentation with Multi-Modal Feature Interaction and Alignment Based on Convolutional Nonlinear Spiking Neural Membrane Systems. 基于卷积非线性尖峰神经膜系统的多模态特征交互和对齐的参考图像分割。
Pub Date : 2024-12-01 Epub Date: 2024-09-23 DOI: 10.1142/S0129065724500643
Siyan Sun, Peng Wang, Hong Peng, Zhicai Liu

Referring image segmentation aims to accurately align image pixels and text features for object segmentation based on natural language descriptions. This paper proposes NSNPRIS (convolutional nonlinear spiking neural P systems for referring image segmentation), a novel model based on convolutional nonlinear spiking neural P systems. NSNPRIS features NSNPFusion and Language Gate modules to enhance feature interaction during encoding, along with an NSNPDecoder for feature alignment and decoding. Experimental results on RefCOCO, RefCOCO[Formula: see text], and G-Ref datasets demonstrate that NSNPRIS performs better than mainstream methods. Our contributions include advances in the alignment of pixel and textual features and the improvement of segmentation accuracy.

参考图像分割的目的是根据自然语言描述,准确对齐图像像素和文本特征,以进行对象分割。本文提出的 NSNPRIS(用于指代图像分割的卷积非线性尖峰神经 P 系统)是一种基于卷积非线性尖峰神经 P 系统的新型模型。NSNPRIS 具有 NSNPFusion 和 Language Gate 模块,可增强编码过程中的特征交互,以及用于特征对齐和解码的 NSNPDecoder。在 RefCOCO、RefCOCO[公式:见正文]和 G-Ref 数据集上的实验结果表明,NSNPRIS 的性能优于主流方法。我们的贡献包括像素和文本特征对齐方面的进步以及分割准确性的提高。
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引用次数: 0
Sparse Spike Feature Learning to Recognize Traceable Interictal Epileptiform Spikes. 稀疏尖峰特征学习识别可追踪的癫痫发作间期尖峰。
Pub Date : 2024-11-30 DOI: 10.1142/S0129065724500710
Chenchen Cheng, Yunbo Shi, Yan Liu, Bo You, Yuanfeng Zhou, Ardalan Aarabi, Yakang Dai

Interictal epileptiform spikes (spikes) and epileptogenic focus are strongly correlated. However, partial spikes are insensitive to epileptogenic focus, which restricts epilepsy neurosurgery. Therefore, identifying spike subtypes that are strongly associated with epileptogenic focus (traceable spikes) could facilitate their use as reliable signal sources for accurately tracing epileptogenic focus. However, the sparse firing phenomenon in the transmission of intracranial neuronal discharges leads to differences within spikes that cannot be observed visually. Therefore, neuro-electro-physiologists are unable to identify traceable spikes that could accurately locate epileptogenic focus. Herein, we propose a novel sparse spike feature learning method to recognize traceable spikes and extract discrimination information related to epileptogenic focus. First, a multilevel eigensystem feature representation was determined based on a multilevel feature representation module to express the intrinsic properties of a spike. Second, the sparse feature learning module expressed the sparse spike multi-domain context feature representation to extract sparse spike feature representations. Among them, a sparse spike encoding strategy was implemented to effectively simulate the sparse firing phenomenon for the accurate encoding of the activity of intracranial neurosources. The sensitivity of the proposed method was 97.1%, demonstrating its effectiveness and significant efficiency relative to other state-of-the-art methods.

间期癫痫状尖峰与致痫灶密切相关。然而,部分尖峰对致痫灶不敏感,这限制了癫痫神经外科手术。因此,识别与致痫灶密切相关的尖峰亚型(可追踪的尖峰)可以促进它们作为准确追踪致痫灶的可靠信号源。然而,颅内神经元放电传输过程中的稀疏放电现象导致了无法肉眼观察到的峰内差异。因此,神经电生理学家无法识别可以准确定位致痫灶的可追踪的尖峰。在此,我们提出了一种新的稀疏尖峰特征学习方法来识别可追踪的尖峰并提取与癫痫焦点相关的判别信息。首先,在多层特征表示模块的基础上确定多层特征系统特征表示,以表达尖峰的内在特性;其次,稀疏特征学习模块对稀疏尖峰多域上下文特征表示进行表达,提取稀疏尖峰特征表示;其中,采用稀疏尖峰编码策略,有效模拟稀疏放电现象,对颅内神经源的活动进行准确编码。该方法的灵敏度为97.1%,与其他先进方法相比,具有显著的效率和有效性。
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引用次数: 0
Anomaly Detection Using Complete Cycle Consistent Generative Adversarial Network. 基于完全循环一致生成对抗网络的异常检测。
Pub Date : 2024-11-30 DOI: 10.1142/S0129065725500042
Zahra Dehghanian, Saeed Saravani, Maryam Amirmazlaghani, Mohamad Rahmati

This research presents a robust adversarial method for anomaly detection in real-world scenarios, leveraging the power of generative adversarial neural networks (GANs) through cycle consistency in reconstruction error. Traditional approaches often falter due to high variance in class-wise accuracy, rendering them ineffective across different anomaly types. Our proposed model addresses these challenges by introducing an innovative flow of information in the training procedure and integrating it as a new discriminator into the framework, thereby optimizing the training dynamics. Furthermore, it employs a supplementary distribution in the input space to steer reconstructions toward the normal data distribution. This adjustment distinctly isolates anomalous instances and enhances detection precision. Also, two unique anomaly scoring mechanisms were developed to augment detection capabilities. Comprehensive evaluations on six varied datasets have confirmed that our model outperforms one-class anomaly detection benchmarks. The implementation is openly accessible to the academic community, available on Github.a.

本研究提出了一种鲁棒的对抗方法,用于现实场景中的异常检测,利用生成对抗神经网络(GANs)的力量,通过重建误差的周期一致性。传统的方法常常因为类准确度的高差异而动摇,使得它们在不同的异常类型上无效。我们提出的模型通过在训练过程中引入创新的信息流并将其作为新的鉴别器集成到框架中来解决这些挑战,从而优化训练动态。此外,它在输入空间中使用补充分布来引导重构向正态数据分布。这种调整明显地隔离了异常实例,提高了检测精度。此外,还开发了两种独特的异常评分机制来增强检测能力。对六个不同数据集的综合评估证实了我们的模型优于一类异常检测基准。学术界可以在Github.a上公开访问该实现。
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引用次数: 0
SATEER: Subject-Aware Transformer for EEG-Based Emotion Recognition. SATEER:基于脑电图的情感识别主体感知变换器。
Pub Date : 2024-11-20 DOI: 10.1142/S0129065725500029
Romeo Lanzino, Danilo Avola, Federico Fontana, Luigi Cinque, Francesco Scarcello, Gian Luca Foresti

This study presents a Subject-Aware Transformer-based neural network designed for the Electroencephalogram (EEG) Emotion Recognition task (SATEER), which entails the analysis of EEG signals to classify and interpret human emotional states. SATEER processes the EEG waveforms by transforming them into Mel spectrograms, which can be seen as particular cases of images with the number of channels equal to the number of electrodes used during the recording process; this type of data can thus be processed using a Computer Vision pipeline. Distinct from preceding approaches, this model addresses the variability in individual responses to identical stimuli by incorporating a User Embedder module. This module enables the association of individual profiles with their EEGs, thereby enhancing classification accuracy. The efficacy of the model was rigorously evaluated using four publicly available datasets, demonstrating superior performance over existing methods in all conducted benchmarks. For instance, on the AMIGOS dataset (A dataset for Multimodal research of affect, personality traits, and mood on Individuals and GrOupS), SATEER's accuracy exceeds 99.8% accuracy across all labels and showcases an improvement of 0.47% over the state of the art. Furthermore, an exhaustive ablation study underscores the pivotal role of the User Embedder module and each other component of the presented model in achieving these advancements.

本研究介绍了一种基于主体感知变换器的神经网络,该网络专为脑电图(EEG)情绪识别任务(SATEER)而设计,需要对脑电图信号进行分析,以对人类的情绪状态进行分类和解释。SATEER 通过将脑电图波形转换为梅尔频谱图来处理脑电图波形,梅尔频谱图可以看作是图像的特殊情况,其通道数与记录过程中使用的电极数相等;因此可以使用计算机视觉管道来处理这类数据。与之前的方法不同的是,该模型通过加入用户嵌入模块,解决了对相同刺激的个体反应的差异性问题。该模块可将个体特征与脑电图关联起来,从而提高分类的准确性。我们使用四个公开数据集对该模型的功效进行了严格评估,结果表明,在所有基准测试中,该模型的性能均优于现有方法。例如,在 AMIGOS 数据集(用于对个人和群体的情感、个性特征和情绪进行多模态研究的数据集)上,SATEER 在所有标签上的准确率都超过了 99.8%,比现有技术提高了 0.47%。此外,一项详尽的消融研究强调了用户嵌入模块和所介绍模型的其他组件在实现这些进步中的关键作用。
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引用次数: 0
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
Pub Date : 2024-11-19 DOI: 10.1142/S0129065725500030
Wenrong Hu, Juan Wang, Feng Li, Daohui Ge, Yuxia Wang, Qingwei Jia, Shasha Yuan

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.

癫痫发作严重影响癫痫患者的身体功能和日常生活。癫痫发作的自动检测可以帮助临床医生在诊断过程中为患者采取预防措施。将深度学习(DL)模型与卷积神经网络(CNN)和变压器网络相结合,可有效提取局部和全局特征,从而提高癫痫发作检测性能。本研究针对癫痫发作检测提出了一种名为 Inresformer 的增强型变压器网络,该网络与提取脑电图(EEG)信号不同尺度特征的 Inception 和 Residual 网络相结合,丰富了特征表示。此外,改进后的变压器网络用两个半步前馈层取代了现有的前馈层,以增强模型的非线性表示。提议的架构利用离散小波变换(DWT)对原始脑电信号进行分解,并选择三个子带进行信号重建。然后,采用 Co-MixUp 方法解决数据不平衡问题,并将处理后的信号发送到 Inresformer 网络,以捕获和识别癫痫发作信息。最后,对三尺度脑电图子信号的结果进行判别融合,以实现最终的癫痫发作识别。所提出的网络在波恩数据集上达到了 100% 的最佳准确率,在长期 CHB-MIT 数据集上的平均准确率为 98.03%,灵敏度为 95.65%,特异性为 98.57%。与现有的 DL 网络相比,所提出的方法在临床研究和诊断应用中具有巨大的潜力,其性能极具竞争力。
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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
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

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.

虽然密度峰聚类(DPC)算法可以有效地分布样本并快速识别噪声点,但它缺乏适应性,无法考虑局部数据结构。此外,聚类算法普遍存在时间复杂度高的问题。先前的研究表明,基于 P 系统的聚类算法可以缓解时间复杂性问题。在膜系统(P 系统)领域,尖峰神经 P 系统(SN P 系统)受到生物神经系统的启发,是第三代神经网络,具有复杂的结构和巨大的并行性优势。因此,本研究首先通过引入最大近邻距离和 K 近邻(KNN)对 DPC 进行了改进。此外,还提出了一种基于延迟尖峰神经 P 系统(DSN P 系统)的方法,以提高算法的性能。随后,提出了 DSNP-ANDPC 算法。通过对四个合成数据集和十个真实世界数据集的综合评估,评估了 DSNP-ANDPC 的有效性。所提出的方法在大多数情况下都优于其他比较方法。
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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
Pub Date : 2024-10-01 Epub Date: 2024-07-17 DOI: 10.1142/S0129065724500539
Changxu Dong, Dengdi Sun

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.

最近,图神经网络(GNN)由于能够直接捕捉非欧几里得结构中的关键信息,在大脑网络自动分类任务中得到了广泛应用。然而,在这一领域仍然存在两个主要挑战。首先,在临床神经医学领域,来自脑区的信号不可避免地会受到来自生理或外部因素的噪声污染。大脑网络的构建在很大程度上依赖于设定的阈值和大脑区域内的特征信息,因此很容易将这些噪声纳入大脑拓扑结构中。此外,人工构建的大脑网络相邻结构的静态性也限制了大脑拓扑结构的实时变化。其次,基于 GNN 的主流方法往往只关注捕捉近邻节点的信息交互,忽略了高阶拓扑特征。为了应对这些挑战,我们提出了一种用于动态优化大脑网络的自适应无监督时空动态超图信息瓶颈(ST-DHIB)框架。具体来说,我们从信息论的角度出发,利用图信息瓶颈(GIB)来净化图结构,并动态更新处理后的输入大脑信号。从图论的角度来看,我们利用设计的超图神经网络(HGNN)和Bi-LSTM来捕捉大脑通道之间的高阶时空关联。在两个可用数据集上进行了针对特定患者和跨患者的综合实验。实验结果证明了所提框架的先进性和通用性。
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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
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

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.

医学图像的质量对于准确诊断和治疗各种疾病至关重要。然而,目前评估图像质量的自动方法都是基于神经网络,这些方法往往只关注像素失真,而忽略了图像中复杂结构的重要性。本研究介绍了一种新的神经网络模型,该模型专门为自动图像质量评估而设计,可解决像素和语义失真问题。该模型引入了一种自适应排序机制,通过对比度灵敏度加权来完善相似图像中微小差异的检测,从而进行像素失真评估。更重要的是,该模型集成了一个采用图神经网络的结构感知学习模块。该模块善于解读图像语义结构与质量之间错综复杂的关系。在两个超声成像数据集上进行评估时,所提出的方法在性能上超越了现有的领先模型。此外,它还能无缝集成到临床工作流程中,实现实时图像质量评估,这对精确诊断和治疗疾病至关重要。
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引用次数: 0
期刊
International journal of neural systems
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