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From text to code – Leveraging machine learning for neurology outpatient clinical coding 从文本到代码——利用机器学习进行神经病学门诊临床编码

Neuroscience informatics

Pub Date : 2026-03-01 Epub Date: 2026-01-19 DOI: 10.1016/j.neuri.2026.100257

Elena Purcaru , Michael George , Matthew Stammers , Christopher Kipps

Background

Most neurological care is delivered in outpatient settings without mandated clinical coding. The clinical records remain stored as unstructured text with inconsistent formatting. There is a significant opportunity to increase the value of these data through automated clinical coding utilising natural language processing (NLP). While existing models for full ICD-10 clinical coding lack sufficient accuracy for clinical use, 60 % of neurology outpatient cases fall into just five diagnostic categories. This suggests that a simplified coding system could enhance feasibility and serve as a foundation for more complex coding schemes.

Objective

We propose a simplified coding system of 29 codes for neurology outpatient episodes. We evaluate several machine learning methods in a supervised single-label classification task on real-world outpatient care notes.

Methods

We collected outpatient care notes created between 15 November 2018 and 2 December 2022. The training dataset included 14,917 care notes, most of which were annotated with ICD-10 codes during routine care and subsequently mapped to 29 simplified diagnostic categories. An external validation set of 1,042 randomly selected encounters was retrospectively coded.

Models included logistic regression, support vector machine, bidirectional LSTM, BERT-based models (DistilBERT, RoBERTa), and a generative large language model (LLM), Mistral 7B. All but the LLM were trained via 10-fold stratified cross-validation; final models were trained on the complete dataset.

Results

DistilBERT and RoBERTa outperformed traditional models, with F1-scores of 81.73 (95 % CI: 79.02–84.13) and 81.16 (95 % CI: 78.84–83.76), respectively. The LLM–DistilBERT hybrid performed worse than all but BiLSTM and produced “medical hallucinations,” making it unsuitable for clinical use. The training data were highly imbalanced. BERT-based models showed strong performance on high-frequency categories, with F1-scores over 85 % for the top five classes. At a 0.85 confidence threshold, DistilBERT achieved 96 % accuracy on 64 % of the external validation set.

Conclusions

BERT-based NLP models perform well in classifying neurology outpatient clinic notes when a reduced set of diagnostic categories is used. In a human-in-the-loop workflow, such models can meaningfully reduce the manual coding workload while preserving accuracy. To our knowledge, this is the first applied study of automated clinical coding in neurology outpatient care.

背景：大多数神经系统护理是在门诊进行的，没有强制性的临床编码。临床记录仍然存储为格式不一致的非结构化文本。通过利用自然语言处理（NLP）的自动临床编码，有一个重要的机会来增加这些数据的价值。虽然现有的ICD-10完整临床编码模型缺乏临床使用的足够准确性，但60%的神经病学门诊病例仅属于五种诊断类别。这表明简化的编码系统可以提高可行性，并为更复杂的编码方案奠定基础。目的提出一种简化的神经内科门诊发作码系统。我们评估了几种机器学习方法在一个有监督的单标签分类任务对现实世界的门诊护理记录。方法收集2018年11月15日至2022年12月2日期间创建的门诊记录。训练数据集包括14,917份护理记录，其中大部分在常规护理期间使用ICD-10代码进行注释，随后映射为29个简化诊断类别。对随机选择的1042次遭遇的外部验证集进行回顾性编码。模型包括逻辑回归、支持向量机、双向LSTM、基于bert的模型（DistilBERT, RoBERTa）和生成式大型语言模型（LLM） Mistral 7B。除LLM外，其余均通过10倍分层交叉验证进行训练；最后的模型在完整的数据集上进行训练。结果distilbert和RoBERTa的f1评分分别为81.73 （95% CI: 79.02 ~ 84.13）和81.16 (95% CI: 78.84 ~ 83.76)，优于传统模型。LLM-DistilBERT混合物的表现比除BiLSTM之外的其他混合物都差，而且会产生“医学幻觉”，因此不适合临床使用。训练数据高度不平衡。基于bert的模型在高频类别上表现出色，前五个类别的f1得分超过85%。在0.85的置信阈值下，在64%的外部验证集上，蒸馏器达到了96%的准确度。结论当使用简化的诊断类别集时，基于bert的NLP模型在神经内科门诊病历分类方面表现良好。在人在循环的工作流中，这样的模型可以有效地减少手工编码的工作量，同时保持准确性。据我们所知，这是神经病学门诊护理中首次应用自动临床编码的研究。

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引用次数: 0

Advances in acquisition and post-processing optimization of IVIM MRI for brain imaging: A systematic review IVIM MRI脑成像采集及后处理优化研究进展综述

Neuroscience informatics

Pub Date : 2026-03-01 Epub Date: 2026-01-06 DOI: 10.1016/j.neuri.2025.100256

Abhijith S. , Saikiran Pendem , Rajagopal Kadavigere , Priyanka , Dharmesh Singh , Priya P.S.

Purpose

Diffusion-weighted MRI is widely used to probe brain microstructure, but its signal reflects both diffusion and perfusion effects. Intravoxel Incoherent Motion (IVIM) MRI enables non-contrast separation of these components, offering potential clinical value in neuroimaging. However, clinical translation remains limited due to variability in acquisition and post-processing methods. This systematic review evaluates optimization strategies aimed at improving the accuracy, reproducibility, and clinical utility of IVIM parameters in brain.

Methods

Registered in PROSPERO and conducted according to PRISMA guidelines, a systematic search across five databases was performed. Original peer-reviewed studies focusing on optimization of IVIM acquisition or post-processing in human brain imaging were included, while reviews and studies lacking methodological detail were excluded. Study quality was assessed using a customized QUADAS-2 tool. Due to methodological heterogeneity, an effect direction plot was applied instead of meta-analysis.

Results

Out of 1,668 identified records, 14 studies were included. Acquisition strategies such as optimised b-value sampling, cardiac gating, and advanced sequences reduced parameter variability by up to 40 %. Post-processing methods, including Bayesian fitting, deep learning–based models, and advanced denoising, improved parameter accuracy by up to 99 % and precision by up to 95 %. Effect direction analysis demonstrated significant positive effects on accuracy and clinical utility (p < 0.001) and repeatability (p < 0.05), while scan-time reduction showed no significant benefit (p > 0.05). No study reported gold-standard validation.

Conclusion

Optimization of IVIM acquisition and post-processing enhances parameter robustness and reproducibility in brain MRI, though protocol heterogeneity remains a major obstacle to standardization and clinical adoption.

目的磁共振弥散加权成像被广泛应用于脑显微结构探测，但其信号同时反映了弥散和灌注效应。体素内非相干运动（IVIM） MRI能够实现这些成分的非对比分离，在神经成像中提供潜在的临床价值。然而，由于获取和后处理方法的差异，临床翻译仍然有限。本系统综述评估了优化策略，旨在提高大脑IVIM参数的准确性、可重复性和临床实用性。方法在普洛斯佩罗注册，并根据PRISMA指南，在五个数据库中进行系统检索。原始的同行评议研究集中于优化IVIM采集或人脑成像后处理，而缺乏方法学细节的综述和研究被排除在外。使用定制的QUADAS-2工具评估研究质量。由于方法的异质性，我们采用效应方向图代替meta分析。结果在1,668份确定的记录中，纳入了14项研究。采集策略，如优化的b值采样，心脏门控，和先进的序列减少参数可变性高达40%。后处理方法，包括贝叶斯拟合、基于深度学习的模型和高级去噪，将参数准确度提高了99%，精度提高了95%。效果方向分析显示，该方法对准确性、临床实用性（p < 0.001）和重复性（p < 0.05）均有显著的积极作用，而缩短扫描时间则无显著的益处（p < 0.05）。没有研究报告金标准验证。结论IVIM采集和后处理的优化提高了脑MRI参数的稳健性和可重复性，但方案的异质性仍然是标准化和临床应用的主要障碍。

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引用次数: 0

NeuroFusion: A forensic enriched ensemble framework for cerebellum disease classification 神经融合：小脑疾病分类的法医综合框架

Neuroscience informatics

Pub Date : 2026-03-01 Epub Date: 2025-12-23 DOI: 10.1016/j.neuri.2025.100251

Abu Hanzala , Md Sajjad , Tanjila Akter , Harpreet Kaur , Md Sadekur Rahman

Accurate and timely classification of cerebellar diseases is crucial for effective diagnostic, yet it remains challenging due to the inherent heterogeneity of these disorders and the subtlety of their neuroimaging manifestations. This study investigated a novel multi-stage ensemble framework integrating SE blocks and segmentation-assisted augmentation tailored for limited cerebellum disease MRI data. Dataset included 3296 MRI scans from four classes and we divided dataset into three parts: training, testing, and validation, and their ratio was 64:20:16. However, we performed image forensic analysis on it, such as Error Level Analysis (ELA) and Noise Residual Analysis (NRA). This study used deep learning architectures that can automatically classify cerebellum diseases and compared these models, which included six D-CNNs models, six transfer learning models, and three ensemble models. Another important contribution of our study is the significant improvement in the classification efficiency by strategically integrating squeeze and excitation and label smoothing techniques. We show that fine-tuning significantly improves the diagnostic accuracy of both D-CNNs and transfer learning models on cerebellum MRI data. Notably, our combined models consistently achieve higher performance, with FusionNet-6 reaching an exceptional accuracy of 99.83 %. K-fold cross-validation was performed, yielding consistently high performance with per-class sensitivity and specificity above 99 %. The study also greatly enhances the impact of dataset augmentation techniques, including the use of segmented data to reveal complex interactions that can enhance the performance of some models or, in some cases, dramatically reduce the performance of specific models. These results underscore the immense potential of deep learning ensembles to provide highly accurate and robust diagnostic support for cerebellum diseases, paving the way for more objective and efficient clinical workflows.

准确和及时的小脑疾病分类是有效诊断的关键，但由于这些疾病固有的异质性和其神经影像学表现的微妙性，它仍然具有挑战性。本研究研究了一种新的多阶段集成框架，将SE块和分段辅助增强相结合，为有限的小脑疾病MRI数据量身定制。数据集包括来自四个类的3296个MRI扫描，我们将数据集分为三个部分：训练、测试和验证，它们的比例为64:20:16。然而，我们对其进行了图像取证分析，如误差水平分析（ELA）和噪声残留分析（NRA）。本研究使用了可以自动对小脑疾病进行分类的深度学习架构，并对这些模型进行了比较，其中包括6个d - cnn模型、6个迁移学习模型和3个集成模型。我们研究的另一个重要贡献是通过策略性地整合挤压和激励和标签平滑技术，显著提高了分类效率。我们发现微调显著提高了d - cnn和迁移学习模型在小脑MRI数据上的诊断准确性。值得注意的是，我们的组合模型始终如一地实现更高的性能，FusionNet-6达到了99.83%的卓越准确率。进行K-fold交叉验证，获得一致的高性能，每类灵敏度和特异性均在99%以上。该研究还极大地增强了数据集增强技术的影响，包括使用分段数据来揭示复杂的相互作用，这些相互作用可以增强某些模型的性能，或者在某些情况下显着降低特定模型的性能。这些结果强调了深度学习系统在为小脑疾病提供高度准确和强大的诊断支持方面的巨大潜力，为更客观和有效的临床工作流程铺平了道路。

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引用次数: 0

Attention-Gated CNN and discrete wavelet transform based ensemble framework for brain hemorrhage classification 基于注意力门控CNN和离散小波变换的脑出血分类集成框架

Neuroscience informatics

Pub Date : 2026-03-01 Epub Date: 2025-11-12 DOI: 10.1016/j.neuri.2025.100243

Srutanik Bhaduri , Rasel Mondal , Prateek Sarangi , Vinod Kumar Kurmi , Swati Goyal , Lovely Kaushal , Mahek Sodani , Tanmay Basu

Brain hemorrhage, or Intracranial Hemorrhage (ICH), is a critical medical condition requiring rapid diagnosis. Existing Convolutional Neural Network (CNN) models often struggle to differentiate similar hemorrhage subtypes like Epidural (EDH) and Subdural (SDH) due to a lack of specific spatial feature identification. This study aims to develop a robust classification framework to address this challenge. We propose an ensemble framework combining two complementary models. The first is an attention-gated 2D CNN designed to highlight subtle hemorrhagic regions. The second is a multi-level Discrete Wavelet Transform (DWT) model that analyzes images in the frequency domain to capture deeper contextual and textural information from the 3D brain volume. The proposed ensemble model was evaluated on the RSNA, CQ500, and a new GMC clinical dataset. The empirical study demonstrates that our model consistently outperforms state-of-the-art methods across standard evaluation metrics, including accuracy, macro-averaged AUC-ROC, specificity, sensitivity, and F1-score. The novel ensembling of an attention-gated CNN and a DWT-based model provides a more comprehensive feature representation, leading to significantly improved accuracy and robustness in ICH classification, particularly in distinguishing challenging subtypes like EDH and SDH.

脑出血或颅内出血（ICH）是一种需要快速诊断的危重医学病症。由于缺乏特定的空间特征识别，现有的卷积神经网络（CNN）模型往往难以区分类似的出血亚型，如硬膜外（EDH）和硬膜下（SDH）。本研究旨在开发一个强大的分类框架来应对这一挑战。我们提出了一个结合两个互补模型的集成框架。第一个是注意门控的二维CNN，用来突出细微的出血区域。第二种是多层离散小波变换（DWT）模型，该模型在频域分析图像，从3D脑体积中捕获更深层次的上下文和纹理信息。在RSNA、CQ500和一个新的GMC临床数据集上对所提出的集成模型进行了评估。实证研究表明，我们的模型在标准评估指标上始终优于最先进的方法，包括准确性、宏观平均AUC-ROC、特异性、敏感性和f1评分。注意力门控CNN和基于dwt的模型的新颖组合提供了更全面的特征表示，从而显著提高了ICH分类的准确性和鲁棒性，特别是在区分EDH和SDH等具有挑战性的亚型方面。

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引用次数: 0

Neuroscience informatics

Pub Date : 2026-03-01 Epub Date: 2026-01-03 DOI: 10.1016/j.neuri.2025.100255

My N. Nguyen, Yoshiki Kubota, Akimasa Hirata

This study investigated age-related changes of brain fiber pathways from diffusion tensor imaging (DTI) tractograms with directional decomposition. Two hundred subjects were stratified into three age groups. Tractograms were generated at two levels: from individual DTI images (subject-level), and from group-averaged images (group-level). Fiber tracking was performed within the cerebral white matter, brainstem, thalamus, and cerebellum at both the levels. Each tractogram was decomposed into directional tracts. At the subject-level, original and decomposed tracts were used to quantify tract density and correlations with age. Tract density was highest in the thalamus and brainstem, while the cerebellum showed the greatest inter-subject variability. Tract count exhibited some significant correlations with age: in cerebral white matter, it decreased overall, especially along S-I and A-P directions; in thalamus, S-I and A-P tracts decreased, while L-R and mixed-direction tracts increased. The brainstem tracts demonstrated its overall stability during aging. At the group level, ∼60 % of brainstem tracts were oriented along the S–I direction, and ∼64 % of cerebellar tracts along the A–P direction. Notably, the posterolateral tracts of the cerebellum showed asymmetry, with the left side associated with visuospatial processing, containing fewer tracts than the right side associated with language pathways. These findings highlight region- and direction-specific changes with age, revealing structural patterns that are not captured by conventional scalar measures. They suggested candidate biomarkers for brain aging and provided useful references for longitudinal neuroimaging and brain stimulation studies, with potential applications in the early detection of neurodegeneration and optimization of stimulation strategies.

本研究利用定向分解扩散张量成像（DTI）图研究脑纤维通路的年龄相关性变化。200名受试者被分为三个年龄组。在两个层次上生成束状图：来自个体DTI图像（受试者水平）和来自组平均图像（组水平）。在脑白质、脑干、丘脑和小脑两个水平上进行纤维跟踪。每个束图被分解成方向束。在受试者水平上，使用原始和分解的束来量化束密度及其与年龄的相关性。丘脑和脑干的束密度最高，而小脑则表现出最大的主体间变异性。脑道数与年龄有显著的相关性：脑白质总体减少，尤其是沿S-I和A-P方向；丘脑S-I束和A-P束减少，L-R束和混合方向束增加。脑干束在衰老过程中表现出整体稳定性。在组水平上，约60%的脑干束沿S-I方向定向，约64%的小脑束沿A-P方向定向。值得注意的是，小脑的后外侧束表现出不对称，左侧与视觉空间处理有关，比右侧与语言通路有关的束少。这些发现突出了区域和方向随年龄的变化，揭示了传统标量测量无法捕获的结构模式。他们提出了脑老化的候选生物标志物，为纵向神经成像和脑刺激研究提供了有用的参考，在神经退行性疾病的早期检测和刺激策略的优化方面具有潜在的应用价值。

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引用次数: 0

Advancing brain tumor diagnostics and therapy (2023–2025): A global bibliometric perspective on innovation and collaboration 推进脑肿瘤诊断和治疗（2023-2025）：创新和合作的全球文献计量学视角

Neuroscience informatics

Pub Date : 2026-03-01 Epub Date: 2025-12-21 DOI: 10.1016/j.neuri.2025.100250

Rosnah Sutan , Afzal Hussain , Rizal Abdul Manaf , Zaleha Md Isa , Ashfaq Hussain

Background

Brain tumors present a daunting clinical challenge, necessitating unwavering innovation in diagnostics, imaging, and therapeutics. Emerging advances in artificial intelligence (AI), molecular biomarkers, and neuroimaging have transformed the research landscape.

Objective

The current research conducted bibliometric analysis to map global research trends in brain tumor diagnosis, imaging, and treatment strategies between 2023 and 2025, with a particular focus on AI applicability and biomarker-driven precision medicine.

Methods

A systematic literature search of the Scopus database was performed in July 2025 for English original research articles between 2023 and 2025. The search keywords included: “brain tumor,” “glioma,” “glioblastoma,” “meningioma,” “astrocytoma,” “diagnosis,” “MRI,” “CT,” “artificial intelligence,” “deep learning,” “machine learning,” “radiotherapy,” “chemotherapy,” “surgery,” “biomarkers,” “prognosis,” “segmentation,” and “classification.” Bibliographic data were analyzed using Biblioshiny to explore publication output, citation impact, prominent authors, institutional productivity, keyword trends, and collaboration networks.

Results

The analysis included 23,496 papers from over 93,000 researchers. It indicated a research boom in AI-enhanced diagnostics, radiomics, and individualized treatments. Both China and the U.S. were leading producers, but the U.S. recorded greater international collaboration and citation impact. Glioma classification, MRI-based segmentation, and deep learning applications were the most common topics. Collaboration networks were geographically focused, with a particular concentration in East Asia.

Conclusion

Brain tumor research is rapidly moving towards precision and AI-driven strategies. While there is a growing scientific output, more international and intersectoral collaboration is needed to make these advances translate to equitable clinical gain.

脑肿瘤是一项令人生畏的临床挑战，需要在诊断、成像和治疗方面进行坚定不移的创新。人工智能（AI）、分子生物标志物和神经影像学的新兴进展已经改变了研究领域。本研究通过文献计量学分析，绘制2023年至2025年全球脑肿瘤诊断、成像和治疗策略的研究趋势，特别关注人工智能的适用性和生物标志物驱动的精准医学。方法于2025年7月对Scopus数据库中2023 - 2025年间的英文原创研究论文进行系统的文献检索。搜索关键词包括：“脑肿瘤”、“胶质瘤”、“胶质母细胞瘤”、“脑膜瘤”、“星形细胞瘤”、“诊断”、“MRI”、“CT”、“人工智能”、“深度学习”、“机器学习”、“放疗”、“化疗”、“手术”、“生物标志物”、“预后”、“分割”和“分类”。使用Biblioshiny对书目数据进行分析，以探索出版物产出、引文影响、知名作者、机构生产力、关键词趋势和合作网络。该分析包括来自93,000多名研究人员的23,496篇论文。它预示着人工智能增强诊断、放射组学和个性化治疗方面的研究热潮。中国和美国都是主要生产国，但美国的国际合作和引用影响更大。胶质瘤分类、基于mri的分割和深度学习应用是最常见的主题。协作网络在地理上是集中的，尤其集中在东亚。结论脑肿瘤研究正迅速向精准化和人工智能驱动的方向发展。虽然科学产出不断增加，但需要更多的国际和部门间合作，以使这些进展转化为公平的临床收益。

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引用次数: 0

TRELLIS -enhanced surface features for comprehensive intracranial aneurysm analysis TRELLIS增强的表面特征用于颅内动脉瘤的综合分析

Neuroscience informatics

Pub Date : 2026-03-01 Epub Date: 2026-01-13 DOI: 10.1016/j.neuri.2026.100259

Clément Hervé, Paul Garnier, Jonathan Viquerat, Elie Hachem

Intracranial aneurysms pose a significant clinical risk yet are difficult to detect, delineate, and model due to limited annotated 3D data. We propose a cross-domain feature-transfer approach that leverages the latent geometric embeddings learned by TRELLIS, a generative model trained on large-scale non-medical 3D datasets, to augment neural networks for aneurysm analysis. By replacing conventional point normals or mesh descriptors with TRELLIS surface features, we systematically enhance three downstream tasks: (i) classifying aneurysms versus healthy vessels in the Intra3D dataset, (ii) segmenting aneurysm and vessel regions on 3D meshes, and (iii) predicting time-evolving blood-flow fields using a graph neural network on the AnXplore dataset. Our experiments show that the inclusion of these features yields strong gains in accuracy, F1-score, and segmentation quality over state-of-the-art baselines, and reduces simulation error by 15%. These results illustrate the broader potential of transferring 3D representations from general-purpose generative models to specialized medical tasks.

颅内动脉瘤具有显著的临床风险，但由于有限的注释三维数据，难以检测，描绘和建模。我们提出了一种跨域特征转移方法，该方法利用TRELLIS学习的潜在几何嵌入来增强动脉瘤分析的神经网络。TRELLIS是一种基于大规模非医疗3D数据集训练的生成模型。通过用TRELLIS表面特征取代传统的点法线或网格描述符，我们系统地增强了三个下游任务：(i)在Intra3D数据集中对动脉瘤和健康血管进行分类，（ii）在3D网格上分割动脉瘤和血管区域，以及（iii）在AnXplore数据集中使用图神经网络预测随时间变化的血流场。我们的实验表明，与最先进的基线相比，这些特征的包含在准确性、f1分数和分割质量方面产生了巨大的收益，并将模拟误差减少了15%。这些结果说明了将3D表示从通用生成模型转移到专业医疗任务的更广泛潜力。

引用次数: 0

Spatiotemporal dynamics of TMS-Evoked responses: A dual damped sine model analysis of cortical site and stimulation condition effects tms诱发反应的时空动态：皮质部位和刺激条件效应的双重阻尼正弦模型分析

Neuroscience informatics

Pub Date : 2026-03-01 Epub Date: 2025-12-25 DOI: 10.1016/j.neuri.2025.100254

Damián Jan

Background

Transcranial magnetic stimulation combined with EEG (TMS-EEG) provides a non-invasive window into cortical excitability and connectivity. However, interpreting TMS-evoked potentials (TEPs) remains challenging due to pervasive artifacts and the limited physiological interpretability of descriptive analytical approaches.

New method

We introduce the Dual Damped Sine (DDS) model, a parametric framework that decomposes TEPs into physiologically meaningful parameters: amplitudes (A₁, A₂), frequencies (f₁, f₂), and damping constants (γ₁, γ₂). We applied DDS to the publicly available OpenNeuro dataset ds001849 to assess its ability to capture site- and condition-specific cortical responses.

Results

DDS achieved excellent model fits (median R² ≈ 0.95; RMSE ≤10⁻⁶) and revealed significant site- and condition-specific differences in the early TEP window (15–80 ms). Active TMS produced larger amplitudes and stronger damping, particularly at DLPFC, with frequencies constrained to physiological bands. These findings are consistent with previous evidence that early TEP components reflect site-specific cortical activation (Siebner et al., 2019; Freedberg et al., 2020).

Comparison with existing methods:While traditional similarity metrics quantify global waveform differences, DDS provides mechanistic interpretation of TEP dynamics through its parametric decomposition. The model captures how cortical responses evolve in time, offering insights into excitatory-inhibitory dynamics.

Conclusions

DDS represents a novel analytical approach that not only confirms established findings about early TEP specificity but also provides physiologically interpretable parameters describing cortical response dynamics. This parametric framework advances TMS-EEG methodology by bridging the gap between waveform analysis and neurophysiological interpretation.

经颅磁刺激联合脑电图（TMS-EEG）提供了一个研究皮层兴奋性和连通性的非侵入性窗口。然而，由于普遍存在的人工产物和描述性分析方法的有限生理可解释性，解释tms诱发电位（TEPs）仍然具有挑战性。我们引入了双阻尼正弦（DDS）模型，这是一个参数框架，将tep分解为有生理意义的参数：振幅（A1, A2），频率（f1, f2）和阻尼常数（γ1, γ2）。我们将DDS应用于公开可用的OpenNeuro数据集ds001849，以评估其捕获部位和条件特异性皮层反应的能力。结果dds获得了极好的模型拟合（中位数R2≈0.95;RMSE≤10−6），并在早期TEP窗口（15-80 ms）显示出显著的部位和条件特异性差异。主动经颅磁刺激产生更大的振幅和更强的阻尼，特别是在DLPFC，频率限制在生理波段。这些发现与之前的证据一致，即早期TEP成分反映了部位特异性皮层激活（Siebner et al., 2019; Freedberg et al., 2020）。与现有方法的比较：传统的相似性度量量化了全局波形差异，而DDS通过参数分解提供了TEP动态的机制解释。该模型捕捉了皮层反应如何随时间演变，为兴奋-抑制动力学提供了见解。结论sdds代表了一种新的分析方法，不仅证实了早期TEP特异性的既定发现，而且提供了描述皮质反应动力学的生理可解释参数。该参数框架通过弥合波形分析和神经生理解释之间的差距，推进了TMS-EEG方法。

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引用次数: 0

CleanEEG: A U-Net based deep learning framework for robust EEG artifact removal CleanEEG：一个基于U-Net的深度学习框架，用于鲁棒脑电信号伪迹去除

Neuroscience informatics

Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.neuri.2026.100264

Dhruva P. Achar , Shavantrevva Bilakeri , Karunakar A. Kotegar , Kurupath Radhakrishnan

High-frequency oscillations (HFOs) are vital biomarkers for identifying the seizure onset zone (SOZ) in patients with drug-resistant epilepsy (DRE). However, EEG artifacts especially muscle and power-line noise overlapping with the HFO frequency range (80–250 Hz) pose significant challenges for accurate detection. Traditional artifact removal methods like independent component analysis (ICA) are labor-intensive and subjective, highlighting the need for automated pre-processing techniques. This study introduces CleanEEG, a U-Net based encoder–decoder model designed to automate artifact removal from clinical EEG. CleanEEG was trained on paired noisy and clean sleep EEG segments from 25 DRE patients (177 segment pairs) at a 512 Hz sampling rate, with clean targets generated through ICA pre-processing. Model performance was quantitatively evaluated on an independent validation set comprising 24 segment pairs from six separate patients excluded from training. Evaluation metrics included relative root mean square error (RRMSE), correlation coefficient (CC), and signal-to-noise ratio (SNR). CleanEEG effectively removed muscle and power-line noise artifacts while preserving important clinical features such as interictal epileptiform discharges (IEDs) and brief potentially ictal rhythmic discharges (BIRDs). The model significantly improved signal quality across electrodes, reducing reconstruction errors and increasing SNR. Additionally, CleanEEG preserved neural activity without introducing distortions and qualitatively demonstrated artifact removal capability on unseen awake EEG data. In a representative DRE patient, critical spatial patterns of HFOs were maintained, essential for accurate SOZ localization. Overall, CleanEEG offers an automated, robust, and efficient solution for artifact removal, enhancing diagnostic accuracy in epilepsy monitoring and HFO analysis, particularly in long-term scalp EEG recordings.

高频振荡（HFOs）是识别耐药癫痫（DRE）患者癫痫发作区（SOZ）的重要生物标志物。然而，与HFO频率范围（80-250 Hz）重叠的EEG伪影，特别是肌肉和电力线噪声，对准确检测构成了重大挑战。传统的人工制品去除方法，如独立成分分析（ICA）是劳动密集型和主观的，突出了对自动化预处理技术的需求。本文介绍了一种基于U-Net的编码器-解码器模型CleanEEG，该模型旨在自动去除临床脑电图中的伪影。CleanEEG以512 Hz采样率对25例DRE患者（177对）的有噪声和干净睡眠脑电片段进行训练，并通过ICA预处理生成干净目标。模型的性能在一个独立的验证集上进行定量评估，该验证集包括来自6个排除在训练之外的单独患者的24个片段对。评价指标包括相对均方根误差（RRMSE）、相关系数（CC）和信噪比（SNR）。CleanEEG有效地去除肌肉和电力线噪声，同时保留重要的临床特征，如间歇癫痫样放电（ied）和短暂的潜在间歇节律性放电（BIRDs）。该模型显著改善了电极间的信号质量，减少了重构误差，提高了信噪比。此外，CleanEEG在不引入扭曲的情况下保留了神经活动，并定性地证明了对未见的清醒脑电图数据的伪影去除能力。在一个典型的DRE患者中，hfo的关键空间模式得以维持，这对于精确定位SOZ至关重要。总的来说，CleanEEG提供了一个自动化的、强大的、高效的解决方案，用于去除工件，提高癫痫监测和HFO分析的诊断准确性，特别是在长期头皮EEG记录中。

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引用次数: 0

From epilepsy seizure classification to detection: A deep learning-based approach for raw EEG signals 从癫痫发作分类到检测：基于深度学习的原始脑电图信号处理方法

Neuroscience informatics

Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.neuri.2026.100263

Davy Darankoum , Manon Villalba , Clélia Allioux , Baptiste Caraballo , Carine Dumont , Eloïse Gronlier , Corinne Roucard , Yann Roche , Chloé Habermacher , Sergei Grudinin , Julien Volle

Epilepsy is the most prevalent neurological disorder in the world. Although epilepsy has been recognized for centuries, clinical doctors still lack reliable automated tools to diagnose epileptic seizures in electroencephalograms (EEGs). The research community has made significant efforts to develop automated systems for identifying and quantifying epileptic seizures, with many studies reporting excellent accuracy. However, clinicians continue to rely on manual annotations because automated techniques exhibit poor generalization performance when applied to EEG data from new patients. Another challenge in the field is translating the results of preclinical studies conducted on animals to clinical applications in humans.

This work contributes to both challenges. Firstly, we investigate the reasons behind the lack of generalization in automatic models. We find that most existing techniques are evaluated on seizure classification tasks, while clinical doctors primarily encounter detection tasks in their practice. We demonstrate that the performance of automated pipelines differs significantly between the two and identify the key distinction between the tasks: classification presumes a prior separation between seizure and non-seizure EEG signals, whereas detection requires no such prior knowledge. Secondly, we bridge the gap between preclinical and clinical studies by developing novel deep learning architectures. Our best model, trained on EEG data from epileptic mice, demonstrates excellent generalization with an F1-score of 93% when tested on human data.

癫痫是世界上最普遍的神经系统疾病。虽然癫痫已经被认识了几个世纪，但临床医生仍然缺乏可靠的自动化工具来诊断脑电图（eeg）中的癫痫发作。研究界在开发用于识别和量化癫痫发作的自动化系统方面做出了重大努力，许多研究报告了出色的准确性。然而，临床医生仍然依赖于手动注释，因为自动化技术在应用于新患者的脑电图数据时表现出较差的泛化性能。该领域的另一个挑战是将在动物身上进行的临床前研究结果转化为人类的临床应用。这项工作有助于应对这两个挑战。首先，我们研究了自动模型缺乏泛化的原因。我们发现大多数现有的技术都是在癫痫分类任务上进行评估的，而临床医生在实践中主要遇到的是检测任务。我们证明了自动化管道的性能在两者之间存在显着差异，并确定了任务之间的关键区别：分类假设癫痫发作和非癫痫发作脑电图信号之间的预先分离，而检测不需要这样的先验知识。其次，我们通过开发新的深度学习架构来弥合临床前和临床研究之间的差距。我们的最佳模型在癫痫小鼠的脑电图数据上进行了训练，在人类数据上测试时，其f1得分为93%，表现出出色的通用性。

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引用次数: 0