Neuroscience informatics最新文献_第6页

MindLift: AI-powered mental health assessment for students MindLift：为学生提供的人工智能心理健康评估

Neuroscience informatics

Pub Date : 2025-05-05 DOI: 10.1016/j.neuri.2025.100208

Shanky Goyal , RishiRaj Dutta , Saurabh Dev , Kola Narasimha Raju , Mohammed Wasim Bhatt

This study introduces MindLift, a student-specific AI-powered mental health assessment and intervention platform. The goal of this research is to create a real-time, multimodal system that can assess mental health through the use of behavioral pattern tracking, audio tone analysis, facial expression recognition, and text sentiment interpretation. By integrating convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based natural language processing (NLP) models, MindLift provides a comprehensive emotional analysis. Through evidence-based techniques like Cognitive Behavioral Therapy (CBT), an intelligent chatbot built into the system provides individualized mental health support. Responses and interventions are customized using important parameters like sentiment polarity, mood detection, and behavioral abnormalities. MindLift emphasizes ethical AI deployment, with strong safeguards for privacy, consent, and fairness. Preliminary studies show a notable increase in student engagement, emotional control, and willingness to seek help. Future developments include deeper personalization, wearable device integration, and wider deployment across educational institutions. The system is evaluated using metrics including accuracy, precision, recall, and F1-score across several modalities.

本研究介绍了MindLift，一个针对学生的人工智能心理健康评估和干预平台。本研究的目标是创建一个实时的、多模式的系统，通过使用行为模式跟踪、音频音调分析、面部表情识别和文本情感解释来评估心理健康。通过集成卷积神经网络（cnn）、循环神经网络（rnn）和基于变换的自然语言处理（NLP）模型，MindLift提供了全面的情绪分析。通过认知行为疗法（CBT）等循证技术，系统内置的智能聊天机器人提供个性化的心理健康支持。响应和干预是使用重要参数定制的，如情绪极性、情绪检测和行为异常。MindLift强调道德的人工智能部署，对隐私、同意和公平有强有力的保障。初步研究表明，学生的参与度、情绪控制和寻求帮助的意愿显著提高。未来的发展包括更深层次的个性化、可穿戴设备集成以及在教育机构中更广泛的部署。该系统的评估指标包括准确率、精密度、召回率和f1分数。

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引用次数: 0

A MATLAB-based tool for converting fNIRS time-series data to Homer3-compatible formats 一个基于matlab的工具，用于将fNIRS时间序列数据转换为homer3兼容格式

Neuroscience informatics

Pub Date : 2025-05-05 DOI: 10.1016/j.neuri.2025.100205

Chao Wang , Xiaojun Cheng , Shichao Liu

Functional Near-Infrared Spectroscopy (fNIRS) is increasingly used in cognitive neuroscience and clinical research, yet preprocessing raw time-series data remains challenging. We introduce a lightweight MATLAB tool to automate the conversion of fNIRS data into Homer3-compatible “*.nirs” format. Our solution targets non-SNIRF raw data and offers a standardized, user-friendly method to streamline fNIRS data preparation. This Technical Note describes the tool's design, workflow, and potential improvements for future development.

功能近红外光谱（fNIRS）在认知神经科学和临床研究中的应用越来越广泛，但原始时间序列数据的预处理仍然具有挑战性。我们介绍了一个轻量级的MATLAB工具来自动将fNIRS数据转换为homer3兼容的“*”。nirs”格式。我们的解决方案针对非snirf原始数据，并提供标准化，用户友好的方法来简化fNIRS数据准备。本技术说明描述了该工具的设计、工作流程和未来开发的潜在改进。

引用次数: 0

Deep learning-based multi-brain capsule network for Next-Gen Clinical Emotion recognition using EEG signals 基于深度学习的多脑胶囊网络在新一代临床情绪识别中的应用

Neuroscience informatics

Pub Date : 2025-04-28 DOI: 10.1016/j.neuri.2025.100203

Ritu Dahiya , Mamatha G , Shila Sumol Jawale , Santanu Das , Sagar Choudhary , Vinod Motiram Rathod , Bhawna Janghel Rajput

Deep learning techniques are crucial for next-generation clinical applications, particularly in Next-Gen Clinical Emotion recognition. To enhance classification accuracy, we propose an Attention mechanism based Capsule Network Model (At-CapNet) for Multi-Brain Region. EEG-tNIRS signals were collected using Next-Gen Clinical Emotion-inducing visual stimuli to construct the TYUT3.0 dataset, from which EEG and tNIRS features were extracted and mapped into matrices. A multi-brain region attention mechanism was applied to integrate EEG and tNIRS features, assigning different weights to features from distinct brain regions to obtain high-quality primary capsules. Additionally, a capsule network module was introduced to optimize the number of capsules entering the dynamic routing mechanism, improving computational efficiency. Experimental validation on the TYUT3.0 Next-Gen Clinical Emotion dataset demonstrates that integrating EEG and tNIRS improves recognition accuracy by 1.53% and 14.35% compared to single-modality signals. Moreover, the At-CapNet model achieves an average accuracy improvement of 4.98% over the original CapsNet model and outperforms existing CapsNet-based Next-Gen Clinical Emotion recognition models by 1% to 5%. This research contributes to the advancement of non-invasive neurotechnology for precise Next-Gen Clinical Emotion recognition, with potential implications for next-generation clinical diagnostics and interventions.

深度学习技术对下一代临床应用至关重要，特别是在下一代临床情绪识别方面。为了提高分类精度，提出了一种基于注意机制的多脑区胶囊网络模型（At-CapNet）。采用下一代临床情绪诱导视觉刺激采集EEG-tNIRS信号，构建TYUT3.0数据集，提取EEG和tNIRS特征并映射成矩阵。采用多脑区注意机制整合EEG和tNIRS特征，对不同脑区的特征赋予不同权重，获得高质量的初级胶囊。引入胶囊网络模块，优化进入动态路由机制的胶囊数量，提高计算效率。在TYUT3.0下一代临床情绪数据集上进行的实验验证表明，与单模态信号相比，EEG和tNIRS相结合的识别准确率分别提高了1.53%和14.35%。此外，At-CapNet模型比原始CapsNet模型平均准确率提高了4.98%，比现有的基于CapsNet的下一代临床情绪识别模型高出1%至5%。这项研究有助于非侵入性神经技术的进步，以精确的下一代临床情绪识别，对下一代临床诊断和干预具有潜在的意义。

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引用次数: 0

Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology

Neuroscience informatics

Pub Date : 2025-04-28 DOI: 10.1016/j.neuri.2025.100202

Senthil Kumar , J. Ramprasath , V. Kalpana , Manikandan Rajagopal , Maheswaran S , Rupesh Gupta

Introduction

Neuroradiology encounters considerable difficulties owing to imaging data's intricacy and high-dimensional characteristics. Conventional diagnostic techniques often encounter challenges regarding precision and scalability, resulting in delays and possible misinterpretations. This paper presents the Big-Data Analytics-based Diagnostics (BDA-D) framework, a revolutionary method using computational models derived from neural architectures and sophisticated analytics to tackle these difficulties.

Methods

The BDA-D architecture utilizes data mining, pattern recognition, and machine learning to glean useful neuroanatomical characteristics from massive datasets. By simulating human thought processes, this method speeds up clinical decision-making and improves diagnostic accuracy. To evaluate the effectiveness of the framework, it is put to the test in a clinical environment.

Results and Discussion

Diagnostic precision, processing speed, and dependability are all enhanced by experimental validation. By detecting even the most minute neuroanatomical changes, BDA-D allows for more accurate diagnosis than traditional approaches. Based on the results, neuroradiologists may improve their practices by using cutting-edge computational methods to close the gap between data-driven analytics and their practical use in the clinic. BDA-D discovers important patterns from high-dimensional neuroimaging data through biologically inspired neural networks, reaching a remarkable diagnosis accuracy of 97.18%. Its 95.42% increase in processing speed allows rapid study of important disorders such as strokes and neurodegenerative diseases. BDA-D reduces inter-observer variability with a dependable value of 94.96%, increasing clinical confidence in AI-assisted diagnosis.

Conclusion

A revolutionary change in neurodiagnostics, the BDA-D framework improves efficiency and reliability. This method has the potential to completely transform neuroradiology by combining big-data analytics with sophisticated computer models. It will allow for more rapid and precise diagnosis.

神经放射学由于成像数据的复杂性和高维特征而遇到相当大的困难。传统的诊断技术经常遇到精度和可扩展性方面的挑战，导致延迟和可能的误解。本文介绍了基于大数据分析的诊断（BDA-D）框架，这是一种革命性的方法，使用源自神经架构和复杂分析的计算模型来解决这些困难。方法BDA-D架构利用数据挖掘、模式识别和机器学习从海量数据集中收集有用的神经解剖学特征。通过模拟人类的思维过程，该方法加快了临床决策，提高了诊断的准确性。为了评估该框架的有效性，在临床环境中对其进行了测试。结果与讨论经实验验证，该方法提高了诊断精度、处理速度和可靠性。通过检测即使是最微小的神经解剖变化，BDA-D允许比传统方法更准确的诊断。基于结果，神经放射学家可以通过使用尖端的计算方法来缩小数据驱动分析与临床实际应用之间的差距，从而改进他们的实践。BDA-D通过生物学启发的神经网络从高维神经成像数据中发现重要模式，达到了97.18%的显著诊断准确率。它的处理速度提高了95.42%，可以快速研究中风和神经退行性疾病等重要疾病。BDA-D降低了观察者之间的可变性，可靠值为94.96%，增加了人工智能辅助诊断的临床可信度。结论BDA-D框架是神经诊断学的革命性变革，提高了效率和可靠性。通过将大数据分析与复杂的计算机模型相结合，这种方法有可能彻底改变神经放射学。它将允许更快速和准确的诊断。

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引用次数: 0

AI-driven EEG neuroscientific analysis for evaluating the influence of emotions on false memory 人工智能驱动的EEG神经科学分析评估情绪对错误记忆的影响

Neuroscience informatics

Pub Date : 2025-04-15 DOI: 10.1016/j.neuri.2025.100201

V. Mahalakshmi

Investigating the brain mechanisms behind memory processing depends on an awareness of how emotions influence false memory. This study used AI-driven EEG microstate analysis to investigate how emotions affect the generation of false memories from both a temporal and a geographic perspective. Within emotional groups, AI-augmented computational models showed distinct brain processing patterns, particularly during the recall processing stage. By altering cognitive processing dynamics, these results support the hypothesis that AI-enhanced brain activity analysis can effectively mimic the influence of emotional states on the formation of false memories. This work explores emotional implications on false memory by combining artificial intelligence (AI) with EEG-based microstate analysis, therefore offering greater understanding of brain dynamics at several cognitive phases. EEG data collected under various emotional states were analyzed using AI-powered techniques to enable exact extraction of microstate templates (Microstates 1–5) for every emotional group. Phase-locked value (AI-PLV) brain functional networks were built inside microstates displaying notable temporal coverage variations. Driven by artificial intelligence, temporal and geographical analysis of EEG signals revealed different brain processing mechanisms among emotional groupings. The group with pleasant emotions showed continuous activity in prefrontal Microstates 3 and 5, therefore suggesting improved cognitive processing. Reflecting a concentration on information integration, the neutral group showed extended involvement in central-active Microstates 3 and 4. These results emphasize how artificial intelligence is helping neuroscientific research to progress by offering a strong framework for comprehending AI-driven emotional-based aberrations in memory recall.

研究记忆处理背后的大脑机制取决于对情绪如何影响错误记忆的认识。本研究使用人工智能驱动的脑电图微状态分析，从时间和地理角度研究情绪如何影响错误记忆的产生。在情绪组中，人工智能增强的计算模型显示出不同的大脑处理模式，特别是在回忆处理阶段。通过改变认知加工动态，这些结果支持了人工智能增强的大脑活动分析可以有效地模拟情绪状态对错误记忆形成的影响的假设。本研究通过将人工智能（AI）与基于脑电图的微观状态分析相结合，探索了错误记忆的情感影响，从而更好地理解了几个认知阶段的大脑动力学。在各种情绪状态下收集的EEG数据使用人工智能技术进行分析，以便准确提取每个情绪组的微状态模板（Microstates 1-5）。锁相值（AI-PLV）脑功能网络在微状态内构建，呈现出显著的时间覆盖变化。在人工智能的驱动下，脑电图信号的时间和地理分析揭示了不同情绪群体的大脑处理机制。具有愉快情绪的那一组前额叶微状态3和微状态5持续活跃，因此表明认知加工得到改善。中性组表现出对中枢活跃的微状态3和4的广泛参与，这反映了他们对信息整合的专注。这些结果强调了人工智能是如何通过提供一个强大的框架来理解人工智能驱动的记忆回忆中基于情感的畸变，从而帮助神经科学研究取得进展的。

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引用次数: 0

Enhanced detection of headache presentation in unruptured brain arteriovenous malformation through combined radiologic features: A cross-sectional study 通过综合放射学特征增强未破裂脑动静脉畸形头痛表现的检测：一项横断面研究

Neuroscience informatics

Pub Date : 2025-04-04 DOI: 10.1016/j.neuri.2025.100200

Chia-Yu Liu , Chia-Feng Lu , Jr-Wei Wu , Yong-Sin Hu , Jih-Yuan Lin , Huai-Che Yang , Jing-Kai Loo , Feng-Chi Chang , Kang-Du Liu , Chung-Jung Lin

Background

Although determining angioarchitecture provide qualitative insights into headache-susceptible brain arteriovenous malformation (BAVM), the potential of quantitative radiomics to detect headache in unruptured BAVM remains unclear. We developed classification models that integrate radiomic features and angioarchitecture to assist unruptured BAVM headache treatment decision-making.

Methods

We considered patients with unruptured BAVM who underwent magnetic resonance imaging between 2010 and 2023. 146 radiomic features were assessed. Radiomic features were delineated, and angioarchitecture was analyzed. Statistical analyses, including least absolute shrinkage and selection operator regression and logistic regression, were used to select features and develop models. Receiver operating characteristic and decision curve analyses were performed to evaluate performance.

Results

The clinical model based on age, sex, and parieto-occipital lesion location achieved an area under the curve (AUC) of 0.741. Adding two significant radiomic features and one angioarchitecture feature enhanced the models. The radiomic and angioarchitecture models achieved an AUC of 0.763. The combined model, with an AUC of 0.799, significantly outperformed the clinical model (

P = 0.046

). Decision curve analysis indicated that the combined model performed best at threshold probabilities between 15% and 40%.

Conclusion

Integrating radiomic features and angioarchitecture enhances the identification of unruptured BAVM headache.

背景：虽然血管结构的确定为头痛易感脑动静脉畸形（BAVM）提供了定性的见解，但定量放射组学在未破裂的BAVM中检测头痛的潜力仍不清楚。我们开发了结合放射学特征和血管结构的分类模型，以辅助未破裂的BAVM头痛治疗决策。方法选取2010年至2023年间接受磁共振成像的未破裂脑脊髓瘤患者。评估146个放射学特征。放射学特征勾画，血管结构分析。统计分析，包括最小绝对收缩和选择算子回归和逻辑回归，用于选择特征和开发模型。采用受试者工作特征和决策曲线分析来评价受试者的表现。结果基于年龄、性别、枕顶病变部位的临床模型曲线下面积（AUC）为0.741。添加两个重要的放射学特征和一个血管建筑学特征增强了模型。放射学和血管建筑学模型的AUC为0.763。联合模型的AUC为0.799，显著优于临床模型（P=0.046）。决策曲线分析表明，组合模型在阈值概率在15%到40%之间时表现最佳。结论放射学特征与血管造影相结合可提高对未破裂性脑脊髓型头痛的识别。

{"title":"Enhanced detection of headache presentation in unruptured brain arteriovenous malformation through combined radiologic features: A cross-sectional study","authors":"Chia-Yu Liu , Chia-Feng Lu , Jr-Wei Wu , Yong-Sin Hu , Jih-Yuan Lin , Huai-Che Yang , Jing-Kai Loo , Feng-Chi Chang , Kang-Du Liu , Chung-Jung Lin","doi":"10.1016/j.neuri.2025.100200","DOIUrl":"10.1016/j.neuri.2025.100200","url":null,"abstract":"<div><h3>Background</h3><div>Although determining angioarchitecture provide qualitative insights into headache-susceptible brain arteriovenous malformation (BAVM), the potential of quantitative radiomics to detect headache in unruptured BAVM remains unclear. We developed classification models that integrate radiomic features and angioarchitecture to assist unruptured BAVM headache treatment decision-making.</div></div><div><h3>Methods</h3><div>We considered patients with unruptured BAVM who underwent magnetic resonance imaging between 2010 and 2023. 146 radiomic features were assessed. Radiomic features were delineated, and angioarchitecture was analyzed. Statistical analyses, including least absolute shrinkage and selection operator regression and logistic regression, were used to select features and develop models. Receiver operating characteristic and decision curve analyses were performed to evaluate performance.</div></div><div><h3>Results</h3><div>The clinical model based on age, sex, and parieto-occipital lesion location achieved an area under the curve (AUC) of 0.741. Adding two significant radiomic features and one angioarchitecture feature enhanced the models. The radiomic and angioarchitecture models achieved an AUC of 0.763. The combined model, with an AUC of 0.799, significantly outperformed the clinical model (<span><math><mi>P</mi><mo>=</mo><mn>0.046</mn></math></span>). Decision curve analysis indicated that the combined model performed best at threshold probabilities between 15% and 40%.</div></div><div><h3>Conclusion</h3><div>Integrating radiomic features and angioarchitecture enhances the identification of unruptured BAVM headache.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100200"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807506","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

Leveraging transparent ontology learning to refine constructs in neuroscience 利用透明的本体学习来完善神经科学的结构

Neuroscience informatics

Pub Date : 2025-03-28 DOI: 10.1016/j.neuri.2025.100199

David Moreau, Kristina Wiebels

引用次数: 0

Feature fusion based deep learning model for Alzheimer's neurological disorder classification 基于特征融合的深度学习阿尔茨海默病神经障碍分类模型

Neuroscience informatics

Pub Date : 2025-03-19 DOI: 10.1016/j.neuri.2025.100196

Arhath Kumar , S. Pradeep , Kumud Arora , G. Sreeram , A. Pankajam , Trupti Patil , Aradhana Sahu

Alzheimer's disease (AD) is a severe brain disorder that can cause degradation of brain tissue and memory loss. Owing to Alzheimer's disease's high cost, a number of deep learning-based models have been put out to accurately identify the illness. This study introduces a new way to classify Alzheimer's disease using deep learning and combining different types of features. The 3D lightweight MBANet developed in this research has less parameters and can concentrate on more discriminative deep structures than conventional artificial neural networks like CNN, according to experimental data. We first create a Multi-Branch Attention Network (MBANet) to gather detailed features of the hippocampus from large sets of data. A new method is created to capture texture features in the hippocampus. It uses two techniques: multi-Tree Wavelet Transform (MTWT) and Gray Length Matrix (GLM). This method works in three dimensions and at different scales. Also, standard methods are used to measure the size and shape of the hippocampus. A mixed feature fusion network is created to simplify and combine data from the hippocampus, helping to classify Alzheimer's disease more effectively. Tests on the EADC-ADNI dataset show that the proposed method for classifying Alzheimer's disease achieves an accuracy of 93.39%, a

F_{1}

-score of 93.10%, and an AUC of 93.21%. The test results show that the proposed method for classifying Alzheimer's disease is effective and better than traditional methods.

阿尔茨海默病（AD）是一种严重的脑部疾病，可导致脑组织退化和记忆力丧失。由于阿尔茨海默病的高成本，许多基于深度学习的模型已经问世，以准确识别这种疾病。本研究提出了一种利用深度学习和结合不同类型特征对阿尔茨海默病进行分类的新方法。实验数据表明，与CNN等传统人工神经网络相比，本研究开发的3D轻量级MBANet具有更少的参数，可以专注于更具判别性的深层结构。我们首先创建了一个多分支注意网络（MBANet），从大量数据中收集海马体的详细特征。提出了一种捕捉海马纹理特征的新方法。它使用了两种技术：多树小波变换（MTWT）和灰度长度矩阵（GLM）。这种方法适用于三维空间和不同的尺度。此外，采用标准方法测量海马的大小和形状。一个混合特征融合网络被创建来简化和组合来自海马体的数据，帮助更有效地分类阿尔茨海默病。在EADC-ADNI数据集上的测试表明，本文提出的阿尔茨海默病分类方法的准确率为93.39%，f1得分为93.10%，AUC为93.21%。实验结果表明，该方法对阿尔茨海默病进行分类是有效的，并且优于传统的分类方法。

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引用次数: 0

Non-invasive brain stimulation-based sleep stage classification using transcranial infrared based electrocardiogram 基于无创脑刺激的经颅红外心电图睡眠阶段分类

Neuroscience informatics

Pub Date : 2025-03-18 DOI: 10.1016/j.neuri.2025.100197

Janjhyam Venkata Naga Ramesh , Aadam Quraishi , Yassine Aoudni , Mustafa Mudhafar , Divya Nimma , Monika Bansal

Non-invasive brain stimulation (NIBS) techniques, such as transcranial infrared (tNIR) stimulation, offer promising advancements in sleep monitoring and regulation. To enhance sleep stage classification without relying on traditional polysomnography (PSG) systems, we propose a novel approach integrating single-channel electrocardiogram (ECG) signals, heart rate variability (HRV) features, and tNIR stimulation. The maximal overlap discrete wavelet transform (MODWT) is applied for multi-resolution analysis of ECG signals, followed by peak information extraction. Based on the first-order deviation of peak positions, multi-dimensional HRV features are extracted. To identify HRV features strongly associated with different sleep stages, we introduce a feature selection method combining the ReliefF algorithm and Gini index. The selected features are then processed using the INFO-ABC Logit Boost method to establish correlations between HRV dynamics and sleep stages. Experimental results on publicly available datasets demonstrate that the proposed model achieves an overall accuracy of 83.67%, a precision of 82.59%, a Kappa coefficient of 77.94%, and an F1-score of 82.97%. Compared with conventional sleep staging methods, our approach enhances sleep quality assessment and facilitates real-time, non-invasive monitoring in home and mobile healthcare settings, leveraging the potential of tNIR-based NIBS for sleep modulation.

非侵入性脑刺激（NIBS）技术，如经颅红外（tNIR）刺激，在睡眠监测和调节方面提供了有希望的进步。为了在不依赖传统多导睡眠图（PSG）系统的情况下增强睡眠阶段分类，我们提出了一种整合单通道心电图（ECG）信号、心率变异性（HRV）特征和tNIR刺激的新方法。采用最大重叠离散小波变换（MODWT）对心电信号进行多分辨率分析，提取峰值信息。基于峰值位置的一阶偏差，提取了多维HRV特征。为了识别与不同睡眠阶段密切相关的HRV特征，我们引入了一种结合ReliefF算法和基尼指数的特征选择方法。然后使用INFO-ABC Logit Boost方法对选定的特征进行处理，以建立HRV动态与睡眠阶段之间的相关性。在公开数据集上的实验结果表明，该模型的总体准确率为83.67%，精密度为82.59%，Kappa系数为77.94%，f1分数为82.97%。与传统的睡眠分期方法相比，我们的方法增强了睡眠质量评估，促进了家庭和移动医疗环境中的实时、无创监测，充分利用了基于tnir的NIBS在睡眠调节方面的潜力。

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引用次数: 0

Analysis and development of clinically recorded dysarthric speech corpus for patients affected with various stroke conditions 不同脑卒中患者临床记录的运动障碍语料库分析与开发

Neuroscience informatics

Pub Date : 2025-03-18 DOI: 10.1016/j.neuri.2025.100198

Oindrila Banerjee , K.V.N. Sita Mahalakshmi , M.V.S. Jyothi , D. Govind , U.K. Rakesh , A. Rajeev , K. Samudravijaya , Akhilesh Kumar Dubey , Suryakanth V. Gangashetty

The manuscript presents the work related to the development of a dysarthric speech corpus for various types of stroke conditions. The corpus consists of speech recorded from 50 stroke patients and 50 healthy controls in clinical environments. Severity of stroke for each patient has been assessed by the clinician based on the National Institute of Health Stroke Scale. The text read by patients and healthy controls comprises (a) five sustained vowels, (b) three words consisting of the plosive consonant and vowels, and (c) 10 phonetically rich sentences in Telugu language. A discriminative analysis is carried out using conventional Mel Frequency Cepstral Coefficients and Convolutional Neural Networks to quantify the perceptual variations in dysarthric speech of stroke patients and healthy controls. Vowels and word utterances of the speech corpus exhibited better class discrimination characteristics compared to sentences for text dependent and speaker independent scenarios.

手稿提出了有关的工作，为各种类型的中风条件的发展困难的言语语料库。语料库由50名中风患者和50名健康对照者在临床环境下的语音记录组成。每位患者的中风严重程度已由临床医生根据美国国立卫生研究院中风量表进行评估。患者和健康对照者阅读的文本包括(a)五个持续元音，(b)三个由爆破辅音和元音组成的单词，以及(c) 10个语音丰富的泰卢固语句子。采用传统的Mel频率倒谱系数和卷积神经网络进行判别分析，量化脑卒中患者和健康对照者在言语困难中的感知变化。语音语料库中的元音和单词话语比文本依赖和说话人独立情景下的句子表现出更好的类别区分特征。

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引用次数: 0