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Exploring the Versatility of Spiking Neural Networks: Applications Across Diverse Scenarios. 探索脉冲神经网络的多功能性:跨不同场景的应用。
Pub Date : 2024-12-23 DOI: 10.1142/S0129065725500078
Matteo Cavaleri, Claudio Zandron

In the last few decades, Artificial Neural Networks have become more and more important, evolving into a powerful tool to implement learning algorithms. Spiking neural networks represent the third generation of Artificial Neural Networks; they have earned growing significance due to their remarkable achievements in pattern recognition, finding extensive utility across diverse domains such as e.g. diagnostic medicine. Usually, Spiking Neural Networks are slightly less accurate than other Artificial Neural Networks, but they require a reduced amount of energy to perform calculations; this amount of energy further reduces in a very significant manner if they are implemented on hardware specifically designed for them, like neuromorphic hardware. In this work, we focus on exploring the versatility of Spiking Neural Networks and their potential applications across a range of scenarios by exploiting their adaptability and dynamic processing capabilities, which make them suitable for various tasks. A first rough network is designed based on the dataset's general attributes; the network is then refined through an extensive grid search algorithm to identify the optimal values for hyperparameters. This dual-step process ensures that the Spiking Neural Network can be tailored to diverse and potentially very different situations in a direct and intuitive manner. We test this by considering three different scenarios: epileptic seizure detection, both considering binary and multi-classification tasks, as well as wine classification. The proposed methodology turned out to be highly effective in binary class scenarios: the Spiking Neural Networks models achieved significantly lower energy consumption compared to Artificial Neural Networks while approaching nearly 100% accuracy. In the case of multi-class classification, the model achieved an accuracy of approximately 90%, thus indicating that it can still be further improved.

在过去的几十年里,人工神经网络变得越来越重要,发展成为实现学习算法的强大工具。脉冲神经网络代表了第三代人工神经网络;由于在模式识别方面取得的显著成就,它们已经获得了越来越多的意义,在诊断医学等不同领域得到了广泛的应用。通常,脉冲神经网络比其他人工神经网络稍微不那么精确,但它们需要更少的能量来执行计算;如果它们在专门为它们设计的硬件上实现,比如神经形态硬件,那么这种能量会以非常显著的方式进一步减少。在这项工作中,我们专注于探索脉冲神经网络的多功能性及其在一系列场景中的潜在应用,通过利用它们的适应性和动态处理能力,使它们适合于各种任务。基于数据集的一般属性设计了第一个粗糙网络;然后通过广泛的网格搜索算法对网络进行细化,以确定超参数的最优值。这种双步骤过程确保了spike神经网络可以以直接和直观的方式定制各种可能非常不同的情况。我们通过考虑三种不同的场景来测试这一点:癫痫发作检测,同时考虑二元和多分类任务,以及葡萄酒分类。所提出的方法被证明在二元类场景中非常有效:与人工神经网络相比,峰值神经网络模型的能耗显著降低,同时准确率接近100%。在多类分类的情况下,该模型达到了约90%的准确率,表明该模型仍有进一步改进的空间。
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引用次数: 0
A Cloud Detection Network Based on Adaptive Laplacian Coordination Enhanced Cross-Feature U-Net. 基于自适应拉普拉斯协调增强型交叉特征 U-Net 的云检测网络。
Pub Date : 2024-12-13 DOI: 10.1142/S0129065725500054
Kaizheng Wang, Ruohan Zhou, Jian Wang, Ferrante Neri, Yitong Fu, Shunzhen Zhou

Cloud cover experiences rapid fluctuations, significantly impacting the irradiance reaching the ground and causing frequent variations in photovoltaic power output. Accurate detection of thin and fragmented clouds is crucial for reliable photovoltaic power generation forecasting. In this paper, we introduce a novel cloud detection method, termed Adaptive Laplacian Coordination Enhanced Cross-Feature U-Net (ALCU-Net). This method augments the traditional U-Net architecture with three innovative components: an Adaptive Feature Coordination (AFC) module, an Adaptive Laplacian Cross-Feature U-Net with a Multi-Grained Laplacian-Enhanced (MLE) feature module, and a Criss-Cross Feature Fused Detection (CCFE) module. The AFC module enhances spatial coherence and bridges semantic gaps across multi-channel images. The Adaptive Laplacian Cross-Feature U-Net integrates features from adjacent hierarchical levels, using the MLE module to refine cloud characteristics and edge details over time. The CCFE module, embedded in the U-Net decoder, leverages criss-cross features to improve detection accuracy. Experimental evaluations show that ALCU-Net consistently outperforms existing cloud detection methods, demonstrating superior accuracy in identifying both thick and thin clouds and in mapping fragmented cloud patches across various environments, including oceans, polar regions, and complex ocean-land mixtures.

云量波动迅速,严重影响到达地面的辐照度,导致光伏发电输出频繁变化。准确探测薄云和碎片云对于光伏发电的可靠预测至关重要。本文介绍了一种新的云检测方法,称为自适应拉普拉斯协调增强交叉特征U-Net (ALCU-Net)。该方法对传统的U-Net体系结构进行了改进,采用了三个创新组件:自适应特征协调(AFC)模块、带有多粒度拉普拉斯增强(MLE)特征模块的自适应拉普拉斯交叉特征U-Net模块和交叉特征融合检测(CCFE)模块。AFC模块增强了空间一致性,并在多通道图像之间弥合了语义差距。自适应拉普拉斯交叉特征U-Net集成了相邻层次的特征,使用MLE模块随着时间的推移细化云特征和边缘细节。CCFE模块,嵌入在U-Net解码器,利用纵横交错的特点,以提高检测精度。实验评估表明,ALCU-Net始终优于现有的云检测方法,在识别厚云和薄云以及在各种环境(包括海洋、极地和复杂的海洋-陆地混合)中绘制碎片云斑块方面表现出卓越的准确性。
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引用次数: 0
Precise Localization for Anatomo-Physiological Hallmarks of the Cervical Spine by Using Neural Memory Ordinary Differential Equation. 利用神经记忆常微分方程精确定位颈椎的解剖生理特征
Pub Date : 2024-12-01 Epub Date: 2024-07-25 DOI: 10.1142/S0129065724500564
Xi Zheng, Yi Yang, Dehan Li, Yi Deng, Yuexiong Xie, Zhang Yi, Litai Ma, Lei Xu

In the evaluation of cervical spine disorders, precise positioning of anatomo-physiological hallmarks is fundamental for calculating diverse measurement metrics. Despite the fact that deep learning has achieved impressive results in the field of keypoint localization, there are still many limitations when facing medical image. First, these methods often encounter limitations when faced with the inherent variability in cervical spine datasets, arising from imaging factors. Second, predicting keypoints for only 4% of the entire X-ray image surface area poses a significant challenge. To tackle these issues, we propose a deep neural network architecture, NF-DEKR, specifically tailored for predicting keypoints in cervical spine physiological anatomy. Leveraging neural memory ordinary differential equation with its distinctive memory learning separation and convergence to a singular global attractor characteristic, our design effectively mitigates inherent data variability. Simultaneously, we introduce a Multi-Resolution Focus module to preprocess feature maps before entering the disentangled regression branch and the heatmap branch. Employing a differentiated strategy for feature maps of varying scales, this approach yields more accurate predictions of densely localized keypoints. We construct a medical dataset, SCUSpineXray, comprising X-ray images annotated by orthopedic specialists and conduct similar experiments on the publicly available UWSpineCT dataset. Experimental results demonstrate that compared to the baseline DEKR network, our proposed method enhances average precision by 2% to 3%, accompanied by a marginal increase in model parameters and the floating-point operations (FLOPs). The code (https://github.com/Zhxyi/NF-DEKR) is available.

在评估颈椎疾病时,解剖生理特征的精确定位是计算各种测量指标的基础。尽管深度学习在关键点定位领域取得了令人瞩目的成果,但在面对医学影像时仍存在许多局限性。首先,面对颈椎数据集因成像因素而产生的固有变异,这些方法往往会遇到限制。其次,预测仅占整个 X 射线图像表面积 4% 的关键点也是一个巨大的挑战。为了解决这些问题,我们提出了一种深度神经网络架构 NF-DEKR,专门用于预测颈椎生理解剖中的关键点。利用神经记忆常微分方程的独特记忆学习分离和收敛到奇异全局吸引子的特性,我们的设计有效地缓解了固有的数据变异性。同时,我们引入了多分辨率聚焦模块,在进入分离回归分支和热图分支之前对特征图进行预处理。这种方法针对不同尺度的特征图采用了不同的策略,能更准确地预测密集定位的关键点。我们构建了一个医疗数据集 SCUSpineXray,其中包括由骨科专家注释的 X 光图像,并在公开可用的 UWSpineCT 数据集上进行了类似的实验。实验结果表明,与基线 DEKR 网络相比,我们提出的方法将平均精度提高了 2% 到 3%,同时模型参数和浮点运算 (FLOP) 略有增加。代码 (https://github.com/Zhxyi/NF-DEKR) 可供下载。
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引用次数: 0
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
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International journal of neural systems
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