Neuroscience informatics最新文献_第6页

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。

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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

Integration of software-based cognitive approaches and brain-like computer machinery for efficient cognitive computing 基于软件的认知方法与类脑计算机机器的集成，实现高效的认知计算

Neuroscience informatics

Pub Date : 2025-03-13 DOI: 10.1016/j.neuri.2025.100194

Chitrakant Banchhor , Manoj Kumar Rawat , Rahul Joshi , Dharmesh Dhabliya , Omkaresh Kulkarni , Sandeep Dwarkanath Pande , Umesh Pawar

The widespread adoption of the Internet has transformed various industries, driving significant systemic reforms across different sectors. This transformation has enhanced the Internet's role in information dissemination, resource sharing, and global connectivity, allowing for more efficient distribution of knowledge and services. The development of the Internet model and its research bring significant benefits from the network, enabling people to use and learn from it. However, the traditional education model provides only limited knowledge, restricting growth and progress. Moreover, there is a vast world of knowledge yet to be explored. Nowadays, with the help of network tools, people can understand the dynamics of the whole world and accept the culture and knowledge of different regions without going out. Throughout the study of English legacy problems in various countries, efficient learning methods and high levels of English skills are the goals pursued, while the traditional English model can't meet the students' learning needs in a short time. The model construction of data mining algorithm based on large open network courses is a model for solving legacy problems adopted both domestically and internationally. According to the survey data of universities in various countries, the use of data mining algorithm can fundamentally meet the student's desire and demand for English knowledge. This research, integrates the mining algorithm into English research, which will essentially improve the English legacy problems.

互联网的广泛应用改变了各行各业，推动了不同领域的重大系统性改革。这一转变增强了互联网在信息传播、资源共享和全球互联互通方面的作用，使知识和服务的分配更加有效。互联网模式的发展及其研究为网络带来了巨大的利益，使人们能够使用网络并从中学习。然而，传统的教育模式只提供有限的知识，制约了成长和进步。此外，还有一个广阔的知识世界有待探索。如今，借助网络工具，人们足不出户就能了解整个世界的动态，接受不同地区的文化和知识。纵观各国对英语遗留问题的研究，高效的学习方法和高水平的英语技能是追求的目标，而传统的英语模式在短时间内无法满足学生的学习需求。基于大型开放网络课程的数据挖掘算法模型构建是国内外普遍采用的解决遗留问题的模型。根据各国大学的调查数据，数据挖掘算法的使用可以从根本上满足学生对英语知识的渴望和需求。本研究将挖掘算法整合到英语研究中，将从根本上改善英语遗留问题。

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引用次数: 0

Bayesian Inference General Procedures for A Single-subject Test study 单受试者检验研究的贝叶斯推断一般程序

Neuroscience informatics

Pub Date : 2025-03-12 DOI: 10.1016/j.neuri.2025.100195

Jie Li , Gary Green , Sarah J.A. Carr , Peng Liu , Jian Zhang

Abnormality detection in identifying a single-subject which deviates from the majority of a control group dataset is a fundamental problem. Typically, the control group is characterised using standard Normal statistics, and the detection of a single abnormal subject is in that context. However, in many situations, the control group cannot be described by Normal statistics, making standard statistical methods inappropriate. This paper presents a Bayesian Inference General Procedures for A Single-subject Test (BIGPAST) designed to mitigate the effects of skewness under the assumption that the dataset of the control group comes from the skewed Student t distribution. BIGPAST operates under the null hypothesis that the single-subject follows the same distribution as the control group. We assess BIGPAST's performance against other methods through simulation studies. The results demonstrate that BIGPAST is robust against deviations from normality and outperforms the existing approaches in accuracy, nearest to the nominal accuracy 0.95. BIGPAST can reduce model misspecification errors under the skewed Student t assumption by up to 12 times, as demonstrated in Section 3.3. We apply BIGPAST to a Magnetoencephalography (MEG) dataset consisting of an individual with mild traumatic brain injury and an age and gender-matched control group. For example, the previous method failed to detect abnormalities in 8 brain areas, whereas BIGPAST successfully identified them, demonstrating its effectiveness in detecting abnormalities in a single-subject.

识别偏离对照组数据集大部分的单个受试者的异常检测是一个基本问题。通常，使用标准的正常统计来描述对照组的特征，并且在此背景下检测单个异常受试者。然而，在许多情况下，对照组不能用正常统计来描述，使得标准统计方法不合适。本文提出了一个单受试者测试的贝叶斯推断通用程序（BIGPAST），旨在减轻偏性的影响，假设对照组的数据集来自偏斜的Student t分布。BIGPAST在单一受试者遵循与对照组相同分布的零假设下运行。我们通过模拟研究来评估BIGPAST与其他方法的性能。结果表明，BIGPAST对偏离正态性具有鲁棒性，并且在精度上优于现有方法，最接近名义精度0.95。如3.3节所示，在倾斜的Student t假设下，BIGPAST可以将模型误规范误差减少12倍。我们将BIGPAST应用于脑磁图（MEG）数据集，该数据集由轻度创伤性脑损伤个体和年龄和性别匹配的对照组组成。例如，之前的方法未能检测到8个大脑区域的异常，而BIGPAST成功地识别了它们，证明了它在检测单个受试者异常方面的有效性。

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引用次数: 0

Analyzing infant cry to detect birth asphyxia using a hybrid CNN and feature extraction approach 利用混合CNN和特征提取方法分析婴儿哭声以检测出生窒息

Neuroscience informatics

Pub Date : 2025-02-21 DOI: 10.1016/j.neuri.2025.100193

Samrat Kumar Dey , Khandaker Mohammad Mohi Uddin , Arpita Howlader , Md. Mahbubur Rahman , Hafiz Md. Hasan Babu , Nitish Biswas , Umme Raihan Siddiqi , Badhan Mazumder

Asphyxia, a critical respiratory condition, poses significant risks to newborns and can lead to catastrophic outcomes. Early detection of asphyxia is crucial for reducing infant mortality rates. Traditional medical diagnosis methods can be time-consuming, whereas early detection through artificial intelligence (AI) can expedite the process and improve survival rates. Despite the importance of early asphyxia detection, existing methods are often delayed and not always effective. This research addresses the need for a faster, more accurate approach to detecting infant asphyxia using machine learning (ML) and deep learning (DL) techniques. This study aims to develop a robust AI-driven system to detect asphyxia in newborns using ML and DL models, focusing on improving accuracy and efficiency over traditional diagnostic methods. This study explores feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs), where the features are categorized into time and frequency domains. Data preprocessing techniques, such as noise removal, handling missing values, outliers, and label encoding, are applied to ensure clean data. To address class imbalance, the Random Oversampling (ROS) technique is employed. Hyperparameter optimization is performed using GridSearchCV for various machine-learning models. Deep learning models, including custom artificial neural networks (ANN1) and convolutional neural networks (CNN1, CNN2), are introduced with hidden layers for improved performance. The performance of different ML and DL models is evaluated, with Logistic Regression (LR) achieving an accuracy of 99.16% and a 0.008% error rate. In comparison, ANN1 outperforms other DL models with an accuracy of 98.20% and a 0.018% error rate. The results demonstrate that both ML and DL techniques can significantly enhance early asphyxia detection in newborns. The Logistic Regression model offers the highest accuracy in machine learning, while ANN1 performs optimally in deep learning, suggesting their potential for deployment in clinical settings to improve neonatal care.

窒息是一种严重的呼吸系统疾病，对新生儿构成重大风险，并可能导致灾难性后果。早期发现窒息对降低婴儿死亡率至关重要。传统的医疗诊断方法可能很耗时，而通过人工智能（AI）进行的早期检测可以加快过程并提高生存率。尽管早期窒息检测的重要性，现有的方法往往是延迟的，并不总是有效的。本研究解决了使用机器学习（ML）和深度学习（DL）技术更快，更准确地检测婴儿窒息的方法的需求。本研究旨在开发一个强大的人工智能驱动系统，使用ML和DL模型检测新生儿窒息，重点是提高传统诊断方法的准确性和效率。本研究探索了使用Mel-Frequency倒谱系数（MFCCs）的特征提取，其中特征被分类为时域和频域。数据预处理技术，如去噪、处理缺失值、异常值和标签编码，被用于确保干净的数据。为了解决类不平衡问题，采用了随机过采样（ROS）技术。使用GridSearchCV对各种机器学习模型进行超参数优化。深度学习模型，包括自定义人工神经网络（ANN1）和卷积神经网络（CNN1, CNN2），引入了隐藏层以提高性能。对不同ML和DL模型的性能进行了评估，其中逻辑回归（LR）的准确率为99.16%，错误率为0.008%。相比之下，ANN1的准确率为98.20%，错误率为0.018%，优于其他DL模型。结果表明，ML和DL技术都能显著提高新生儿早期窒息的检测。逻辑回归模型在机器学习中提供了最高的准确性，而ANN1在深度学习中表现最佳，这表明它们有潜力在临床环境中部署，以改善新生儿护理。

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引用次数: 0