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Neuroscience-informed nomogram model for early prediction of cognitive impairment in Parkinson's disease 早期预测帕金森病认知障碍的神经科学nomogram模型

Neuroscience informatics

Pub Date : 2025-02-18 DOI: 10.1016/j.neuri.2025.100189

Sudharshan Putha , Swaroop Reddy Gayam , Bhavani Prasad Kasaraneni , Krishna Kanth Kondapaka , Sateesh Kumar Nallamala , Praveen Thuniki

Cognitive impairment is a common non-motor symptom of Parkinson's disease (PD), significantly affecting patients' quality of life and posing challenges for clinical management. Early prediction of cognitive decline in PD is critical for timely diagnosis and intervention. However, the interplay of multivariate factors such as age, gender, and disease duration complicate early prediction. To address the multifactorial nature of cognitive impairment in PD, this study proposes a neuroscience-informed nomogram model constructed using multivariate logistic regression. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to identify highly correlated clinical variables influencing cognitive function. Subsequently, these variables were integrated into a visualized nomogram model to facilitate early prediction of cognitive impairment (CI) risk. Performance evaluation of the model demonstrated high accuracy, consistency, and clinical applicability, significantly enhancing diagnostic efficiency for neurologists. Furthermore, the model provides visual comparisons of patient distributions across different predictor values, enabling personalized risk assessments. According to experimental analysis and verification, the model demonstrated outstanding prediction with a region under the ROC curve of 0.872 on the original training set and 0.870 on the validation set. Because the anticipated and observed probabilities were so consistent, the model was able to forecast the patient's likelihood of cognitive impairment.

认知障碍是帕金森病（PD）常见的非运动症状，严重影响患者的生活质量，给临床管理带来挑战。早期预测帕金森病患者的认知能力下降对于及时诊断和干预至关重要。然而，年龄、性别和病程等多因素的相互作用使早期预测复杂化。为了解决帕金森病患者认知功能障碍的多因素性质，本研究提出了一个使用多变量逻辑回归构建的神经科学知识的nomogram模型。应用最小绝对收缩和选择算子（LASSO）算法识别影响认知功能的高度相关临床变量。随后，这些变量被整合到一个可视化的nomogram模型中，以促进认知障碍（CI）风险的早期预测。性能评价表明该模型具有较高的准确性、一致性和临床适用性，显著提高了神经科医生的诊断效率。此外，该模型提供了不同预测值的患者分布的可视化比较，实现了个性化的风险评估。经实验分析和验证，该模型具有较好的预测效果，在原始训练集上的ROC曲线下有一个区域为0.872，在验证集上有一个区域为0.870。由于预期和观察到的概率是如此一致，该模型能够预测患者认知障碍的可能性。

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引用次数: 0

Automated classification of epileptic seizures using modified one-dimensional convolution neural network based on empirical mode decomposition with high accuracy 基于经验模态分解的改进一维卷积神经网络在癫痫发作自动分类中的应用

Neuroscience informatics

Pub Date : 2025-02-13 DOI: 10.1016/j.neuri.2025.100188

Ibtihal Hassan Elshekhidris , Magdi B. M. Amien , Ahmed Fragoon

Background and objectives: The method of electroencephalography (EEG) is frequently employed to identify epileptic seizures. Visually inspecting nonlinear EEG waves is a very difficult and time-consuming process. Therefore, to help with patients' long-term assessment and treatment, an effective automatic detection system is required. Traditional methods of machine learning require step of feature extraction by manual which leads to time consuming, for it we modified in one-Dimensional convolution neural network architecture for features extraction and features dimension reduction for makes the classification low computational complexity and more accurate.

Methods: In this study, we did a comparison between three methods for classification: support vector machine, artificial neural network and one-dimensional convolution neural network. We used the stationary wavelet transform with mother function symlet2 for denoising EEG signal and used the empirical mode decomposition for signal decomposition. After that, features extraction step is necessary when used the support vector machine and artificial neural network, but when use the convolution neural network the features are extracted by layers.

Results: The highest value of a classification accuracy was 100%, and a sensitivity 100%, a specificity 100%, and a precision 100%, which appeared five times when using the one-dimensional convolution neural network after empirical mode decomposition method.

Conclusions: The efficiency of the three methods has been compared and evaluated by using four metrics: Accuracy, Sensitivity, specificity, and Precision, and the result showed the one-dimensional convolution neural network is the best method for classification with empirical mode decomposition.

背景和目的：脑电图（EEG）的方法经常被用来识别癫痫发作。视觉检测非线性脑电波是一个非常困难和耗时的过程。因此，为了帮助患者的长期评估和治疗，需要一个有效的自动检测系统。传统的机器学习方法需要手动进行特征提取，耗时长，在一维卷积神经网络架构上进行特征提取和特征降维，使得分类计算复杂度低，准确率高。方法：对支持向量机、人工神经网络和一维卷积神经网络三种分类方法进行比较。采用带母函数symlet2的平稳小波变换对脑电信号进行降噪，并采用经验模态分解对信号进行分解。在此之后，使用支持向量机和人工神经网络时需要进行特征提取步骤，而使用卷积神经网络时则是逐层提取特征。结果：使用经验模态分解方法后的一维卷积神经网络，分类准确率最高为100%，灵敏度最高为100%，特异性最高为100%，精度最高为100%，出现了5次。结论：通过准确度、灵敏度、特异性和精密度4个指标对3种分类方法的效率进行了比较和评价，结果表明一维卷积神经网络是经验模态分解分类的最佳方法。

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引用次数: 0

Computational intelligence in neuroinformatics: Technologies and data analytics 神经信息学中的计算智能：技术和数据分析

Neuroscience informatics

Pub Date : 2025-01-15 DOI: 10.1016/j.neuri.2025.100187

Anand Deshpande , Vania Vieira Estrela , Anitha Jude , Jude Hemanth

引用次数: 0

EEG signal based brain stimulation model to detect epileptic neurological disorders 基于脑电图信号的脑刺激模型检测癫痫性神经系统疾病

Neuroscience informatics

Pub Date : 2025-01-14 DOI: 10.1016/j.neuri.2025.100186

Haewon Byeon , Udit Mahajan , Ashish Kumar , V. Rama Krishna , Mukesh Soni , Monika Bansal

Background: Manual visual inspection and analysis of electroencephalogram (EEG) signals of patients are susceptible to the subjective influence of doctors. The introduction of GA-PSO improved the categorization accuracy of both the EP (Evoked potential) and normal groups by automatically screening and optimizing the best feature combination of brain networks. Therefore, selecting effective EEG features for automatic recognition of EP is particularly important for Neuroscience.

New method: A phase synchronization index (PSI) brain stimulation is constructed from multi-channel EEG signals, extracting 15 topological features from the perspectives of network nodes and structural functions. In order to optimize and screen feature combinations in both single and cross-frequency bands, the GA-PSO algorithm is utilized as a feature selection tool for choosing epileptic EEG network features.

Result: Feature combinations are made both within and between bands, and the optimal feature mix is found using the PSO and GA-PSO algorithms. The study found that the GA-PSO algorithm outperformed the PSO algorithm, achieving a higher EP recognition accuracy of 0.9335 under cross-frequency band conditions.

Comparison with existing methods: The results indicate that the introduction of the genetic algorithm enables the GA-PSO algorithm to maintain population diversity and avoid premature convergence to local optima, thereby enhancing the search capabilities of the population.

Conclusion: Based on the findings, topological aspects provide a good way to describe the anomalies in the brain networks of epileptic patients and enhance the classification accuracy through combination, which provides help for pathological research and clinical diagnosis of epilepsy.

背景：人工目视检查和分析患者脑电图信号容易受到医生的主观影响。GA-PSO的引入通过自动筛选和优化脑网络的最佳特征组合，提高了EP（诱发电位）和正常组的分类准确率。因此，选择有效的脑电特征进行脑电图的自动识别对于神经科学来说尤为重要。新方法：利用多通道脑电信号构建一个相同步指数（PSI）脑刺激，从网络节点和结构功能的角度提取15个拓扑特征。为了优化和筛选单频段和跨频段的特征组合，利用GA-PSO算法作为特征选择工具，选择癫痫脑电图网络特征。结果：在频带内和频带间进行了特征组合，利用粒子群算法和ga -粒子群算法找到了最优的特征组合。研究发现GA-PSO算法优于PSO算法，在交叉频带条件下EP识别准确率达到0.9335。与现有方法的比较：结果表明，遗传算法的引入使GA-PSO算法保持种群多样性，避免过早收敛到局部最优，从而增强了种群的搜索能力。结论：基于本研究结果，拓扑学方面为描述癫痫患者脑网络的异常提供了很好的方法，并通过组合提高了分类准确率，为癫痫的病理研究和临床诊断提供了帮助。

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引用次数: 0

Advances in neurosurgical procedures: Quantum computing and its applications in MRI-guided laser interstitial thermal therapy 神经外科手术的进展：量子计算及其在核磁共振引导激光间质热治疗中的应用

Neuroscience informatics

Pub Date : 2025-01-03 DOI: 10.1016/j.neuri.2024.100185

Afzal Hussain , Ashfaq Hussain , Mohammad Rashid

引用次数: 0

Training size predictably improves machine learning-based epileptic seizure forecasting from wearables 训练大小可预见地提高了基于机器学习的可穿戴设备癫痫发作预测

Neuroscience informatics

Pub Date : 2024-12-09 DOI: 10.1016/j.neuri.2024.100184

Mustafa Halimeh , Michele Jackson , Tobias Loddenkemper , Christian Meisel

Objective: Wrist-worn wearable devices that monitor autonomous nervous system function and movement have shown promise in providing non-invasive, broadly applicable seizure forecasts that increase in accuracy with larger training size. Nevertheless, challenges related to missing validation, small number of enrolled patients, insufficient training data, and lack of patient seizure cycles data hinder its clinical implementation. Here we sought to prospectively validate a previously implemented seizure forecasting algorithm using a larger cohort of pediatric patients with epilepsy (pwe), improve it by including information on seizure cycles, and (3) assess the utility of precise power-laws to predict performance as a function of dataset size.

Methods: We used video-EEG recordings from 166 pwe as ground-truth for seizures, recorded electrodermal activity (EDA), peripheral body temperature (TEMP), blood volume pulse (BVP), accelerometery (ACC) and applied a deep neural LSTM network model (NN) on these data along with information on 24-hour cycles to forecast seizures in a leave-one-subject-out cross validation. Evaluations were made using improvement over chance (IoC) and the Brier skill score (BSS), which measured the improvement of the NN Brier score compared to the Brier score of a rate-matched random (RMR) forecast.

Results: Performance quantified by IoC and BSS increased with training data following precise power-law scaling laws, thereby exceeding prior reported performance levels from smaller datasets. Including information on 24-hour seizure cycles further improved performance. For the largest training set we achieved significant IoC in 68% of pwe, an IoC of 27.3% and a BSS of 0.087.

Interpretation: Our results validate a previous forecast approach and indicate that performance improves predictably as a function of dataset size following power-law scaling.

目的：监测自主神经系统功能和运动的腕戴式可穿戴设备有望提供无创、广泛适用的癫痫发作预测，并随着训练规模的扩大而提高准确性。然而，缺少验证、入选患者数量少、培训数据不足以及缺乏患者癫痫发作周期数据等挑战阻碍了其临床实施。在这里，我们试图使用更大的儿科癫痫患者队列来前瞻性地验证先前实现的癫痫发作预测算法，通过包含癫痫发作周期信息来改进它，并且(3)评估精确幂律的效用，以预测数据集大小的性能。方法：我们使用166 pwe的视频脑电图记录作为癫痫发作的基础事实，记录皮肤电活动（EDA）、外周体温（TEMP）、血容量脉搏（BVP）、加速度计（ACC），并在这些数据上应用深度神经LSTM网络模型（NN）以及24小时周期信息，在留一被试交叉验证中预测癫痫发作。使用改进概率（IoC）和Brier技能评分（BSS）进行评估，BSS衡量了与率匹配随机（RMR）预测的Brier评分相比，NN Brier评分的改善。结果：IoC和BSS量化的性能随着训练数据遵循精确的幂律缩放规律而增加，从而超过先前报道的较小数据集的性能水平。包括24小时癫痫发作周期的信息进一步提高了性能。对于最大的训练集，我们在68%的pwe中实现了显著的IoC， IoC为27.3%，BSS为0.087。解释：我们的结果验证了之前的预测方法，并表明性能作为幂律缩放后数据集大小的函数可预测地提高。

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引用次数: 0

Understanding risk factors of post-stroke mortality 了解中风后死亡的危险因素

Neuroscience informatics

Pub Date : 2024-11-29 DOI: 10.1016/j.neuri.2024.100181

David Castro , Nuno Antonio , Ana Marreiros , Hipólito Nzwalo

Stroke is one of the leading causes of death worldwide. Understanding the risk factors for post-stroke mortality is crucial for improving patient outcomes. This study analyzes and predicts post-stroke mortality using the modified Rankin Scale (mRS), a functional neurological evaluation scale. Several Machine Learning models were developed and assessed using a dataset of 332 stroke patients from Hospital de Faro, Portugal, from 2016 to 2018. The Random Forest model outperformed others, achieving an accuracy of 98.5% and a recall of 91.3. Twenty-four risk factors were identified, with stroke severity as the most critical. These findings provide healthcare professionals with valuable tools for early identification and intervention for high-risk stroke patients, enabling informed decision-making and customized treatment plans. This research advances healthcare predictive analytics, offering a precise mortality prediction model and a comprehensive analysis of risk factors, potentially improving clinical outcomes and reducing mortality rates. Future applications could extend to patient monitoring and management across various medical conditions.

中风是世界范围内死亡的主要原因之一。了解卒中后死亡的危险因素对改善患者预后至关重要。本研究使用改良的兰金量表（mRS）分析和预测脑卒中后死亡率，这是一种功能神经学评估量表。使用2016年至2018年葡萄牙de Faro医院的332名中风患者的数据集开发和评估了几个机器学习模型。随机森林模型优于其他模型，达到了98.5%的准确率和91.3的召回率。确定了24个危险因素，其中中风的严重程度是最关键的。这些发现为医疗保健专业人员早期识别和干预高危卒中患者提供了有价值的工具，使他们能够做出明智的决策并制定个性化的治疗计划。这项研究推进了医疗预测分析，提供了精确的死亡率预测模型和全面的风险因素分析，有可能改善临床结果并降低死亡率。未来的应用可以扩展到各种医疗条件下的患者监测和管理。

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引用次数: 0

KL-FedDis: A federated learning approach with distribution information sharing using Kullback-Leibler divergence for non-IID data KL-FedDis：一种利用非iid数据的Kullback-Leibler散度实现分布信息共享的联邦学习方法

Neuroscience informatics

Pub Date : 2024-11-28 DOI: 10.1016/j.neuri.2024.100182

Md. Rahad , Ruhan Shabab , Mohd. Sultan Ahammad , Md. Mahfuz Reza , Amit Karmaker , Md. Abir Hossain

Data Heterogeneity or Non-IID (non-independent and identically distributed) data identification is one of the prominent challenges in Federated Learning (FL). In Non-IID data, clients have their own local data, which may not be independently and identically distributed. This arises because clients involved in federated learning typically have their own unique, local datasets that vary significantly due to factors like geographical location, user behaviors, or specific contexts. Model divergence is another critical challenge where the local models trained on different clients, data may diverge significantly but making it difficult for the global model to converge. To identify the non-IID data, few federated learning models have been introduced as FedDis, FedProx and FedAvg, but their accuracy is too low. To address the clients Non-IID data along with ensuring privacy, federated learning emerged with appropriate distribution mechanism is an effective solution. In this paper, a modified FedDis learning method called KL-FedDis is proposed, which incorporates Kullback-Leibler (KL) divergence as the regularization technique. KL-FedDis improves accuracy and computation time over the FedDis and FedAvg technique by successfully maintaining the distribution information and encouraging improved collaboration among the local models by utilizing KL divergence.

数据异构或非iid（非独立和同分布）数据识别是联邦学习（FL）中的突出挑战之一。在非iid数据中，客户端有自己的本地数据，这些本地数据可能不是独立的、相同的分布。这是因为参与联邦学习的客户端通常有自己独特的本地数据集，这些数据集由于地理位置、用户行为或特定上下文等因素而变化很大。模型分歧是另一个关键挑战，在不同客户端上训练的局部模型，数据可能会显著分歧，但使全局模型难以收敛。为了识别非iid数据，已经引入了一些联邦学习模型，如FedDis、FedProx和fedag，但它们的准确率太低。为了在解决客户端非iid数据的同时确保隐私，采用适当的分发机制的联邦学习是一种有效的解决方案。本文提出了一种改进的fedis学习方法KL- fedis，该方法将Kullback-Leibler （KL）散度作为正则化技术。KL-FedDis通过利用KL散度成功地维护分布信息和促进局部模型之间的协作，提高了FedDis和fedag技术的准确性和计算时间。

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引用次数: 0

Deep learning-based edge detection for random natural images 基于深度学习的随机自然图像边缘检测

Neuroscience informatics

Pub Date : 2024-11-28 DOI: 10.1016/j.neuri.2024.100183

Kanija Muntarina , Rafid Mostafiz , Sumaita Binte Shorif , Mohammad Shorif Uddin

Edge detection plays a critical role in computer vision, particularly in the analysis of random natural images. It serves as a fundamental step in tasks such as image segmentation, shape extraction, pattern recognition, auto-navigation, and motion analysis, with applications spanning various domains including radar and sonar image processing. The edge detection model attempts to identify points in digital images where significant intensity changes occur, known as edges or region boundaries. Traditionally, edge detection relied on gradient-based operators, which often produced jagged edges and were susceptible to image noise. In recent years, the emergence of deep learning technology has revolutionized this field by utilizing its ability to automatically learn complex features from natural images. Deep learning approaches offer significant advantages in capturing high-level representations, thereby improving the accuracy and robustness of edge detection algorithms. Moreover, the effectiveness of edge detection techniques varies depending on the content and classification of images, such as natural scenes, medical images, or underwater environments. This study aims to evaluate and compare the performance of five widely used deep learning-based edge detection methods to identify the most effective approach specifically tailored for natural images. Through comprehensive experimentation and analysis, this research contributes to advancing the state-of-the-art in edge detection for random natural images, providing insights into the optimal application of deep learning techniques in this domain.

边缘检测在计算机视觉中起着至关重要的作用，特别是在随机自然图像的分析中。它是图像分割、形状提取、模式识别、自动导航和运动分析等任务的基本步骤，应用范围涵盖雷达和声纳图像处理等各个领域。边缘检测模型试图识别数字图像中发生显著强度变化的点，称为边缘或区域边界。传统的边缘检测依赖于基于梯度的算子，这种算子通常会产生锯齿状的边缘，并且容易受到图像噪声的影响。近年来，深度学习技术的出现通过利用其从自然图像中自动学习复杂特征的能力，彻底改变了这一领域。深度学习方法在捕获高级表示方面具有显著优势，从而提高了边缘检测算法的准确性和鲁棒性。此外，边缘检测技术的有效性取决于图像的内容和分类，例如自然场景、医学图像或水下环境。本研究旨在评估和比较五种广泛使用的基于深度学习的边缘检测方法的性能，以确定专门为自然图像量身定制的最有效方法。通过全面的实验和分析，本研究有助于推进随机自然图像边缘检测的最新技术，为深度学习技术在该领域的最佳应用提供见解。

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引用次数: 0

Design of a computational intelligence system for detection of multiple sclerosis with visual evoked potentials 用视觉诱发电位检测多发性硬化症的计算智能系统设计

Neuroscience informatics

Pub Date : 2024-10-30 DOI: 10.1016/j.neuri.2024.100177

Moussa Mohsenpourian , Amir Abolfazl Suratgar , Heidar Ali Talebi , Mahsa Arzani , Abdorreza Naser Moghadasi , Seyed Matin Malakouti , Mohammad Bagher Menhaj

In this study, a new approach for modification of membership functions of a fuzzy inference system (FIS) is demonstrated, in order to serve as a pattern recognition tool for classification of patients diagnosed with multiple sclerosis (MS) from healthy controls (HC) using their visually evoked potential (VEP) recordings. The new approach utilizes Krill Herd (KH) optimization algorithm to modify parameters associated with membership functions of both inputs and outputs of an initial Sugeno-type FIS, while making sure that the error corresponding to training of the network is minimized.

This novel pattern recognition system is applied for classification of VEP signals in 11 MS patients and 11 HC's. A feature extraction routine was performed on the VEP signals, and later substantial features were selected in an optimized feature subset selection scheme employing Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithms. This alone provided further information regarding clinical value of many previously unused VEP features as an aide for making the diagnosis. The newly designed computational intelligence system is shown to outperform popular classifiers (e.g., multilayer perceptron, support-vector machine, etc.) and was able to distinguish MS patients from HC's with an overall accuracy of 90%.

本研究展示了一种修改模糊推理系统（FIS）隶属函数的新方法，以作为一种模式识别工具，利用视觉诱发电位（VEP）记录对诊断为多发性硬化症（MS）和健康对照（HC）的患者进行分类。该方法利用Krill Herd （KH）优化算法对初始sugeno型FIS的输入和输出的隶属度函数相关参数进行修改，同时确保网络训练对应的误差最小。将该模式识别系统应用于11例MS和11例HC的VEP信号分类。首先对VEP信号进行特征提取，然后采用蚁群优化（ACO）和模拟退火（SA）算法对特征子集进行优化选择。仅这一点就提供了关于许多以前未使用的VEP特征作为辅助诊断的临床价值的进一步信息。新设计的计算智能系统被证明优于流行的分类器（例如，多层感知器，支持向量机等），并且能够以90%的总体准确率区分MS患者和HC患者。

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引用次数: 0