EEG-based cerebral pattern analysis for neurological disorder detection via hybrid machine and deep learning approaches

IF 2.3 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods Pub Date : 2025-08-14 DOI:10.1016/j.jneumeth.2025.110551

Kusum Tara , Ruimin Wang , Yoshitaka Matsuda , Satoru Goto , Takako Mitsudo , Takao Yamasaki , Takenao Sugi

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引用次数: 0

Abstract

Background

Monitoring neurological disorders is crucial for the early detection of neurodegeneration and abnormal neural activity of the human brain.

New methods

This study combines a feature-based random forest (RF) machine learning model with an image-based convolutional neural network (CNN) deep-learning approach, forming a hybrid random forest-convolutional neural network (RF-CNN) model to detect neurological disorders such as mild cognitive impairment (MCI), Alzheimer’s disease (AD), and epilepsy (Ep) using electroencephalography (EEG) signals. EEG data from 19 channels were segmented into delta, theta, alpha, and beta frequency bands, generating power-based features, spectral topographic maps, and continuous wavelet transform (CWT) based scalograms, as inputs for cerebral pattern analysis.

Results

The experimental results demonstrated detection accuracy of 88 % and F1-score of 84.85 % with RF, accuracy of 97.58 % and F1-score of 95.16 % using scalograms, accuracy of 98.39 % and F1-score of 97.64 % using spectral maps, and an outstanding 99.19 % accuracy and 98.32 % F1-score with hybrid RF-CNN model.

Comparison with existing methods

Unlike previous models that relied solely on feature-based machine learning or image-based deep learning, this approach enhances disorder detection with greater accuracy by integrating both features and images. Features like power asymmetry increase with cognitive decline, indicating hemispheric imbalance, while a declining cognition index reflects interhemispheric communication loss. Additionally, images including spectral topographic maps and CWT-based scalograms provide a comprehensive view of spatial power distribution and time-frequency characteristics.

Conclusion

The hybrid RF-CNN approach enhances more reliable analysis of altered non-linear brain dynamics and transitional phases, making it a valuable tool for detecting neurological disorders.

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基于脑电图的脑模式分析在神经系统疾病检测中的应用

监测神经系统疾病对于早期发现人类大脑的神经变性和异常神经活动至关重要。新方法本研究将基于特征的随机森林（RF）机器学习模型与基于图像的卷积神经网络（CNN）深度学习方法相结合，形成混合随机森林-卷积神经网络（RF-CNN）模型，利用脑电图（EEG）信号检测轻度认知障碍（MCI）、阿尔茨海默病（AD）和癫痫（Ep）等神经系统疾病。将19个通道的脑电数据分割为delta、theta、alpha和beta频段，生成基于功率的特征、频谱地形图和基于连续小波变换（CWT）的尺度图，作为大脑模式分析的输入。结果RF检测准确率为88 %，F1-score为84.85 %；尺度图检测准确率为97.58 %，F1-score为95.16 %；光谱图检测准确率为98.39 %，F1-score为97.64 %；RF- cnn混合模型检测准确率为99.19 %，F1-score为98.32 %。与之前仅依赖基于特征的机器学习或基于图像的深度学习的模型相比，该方法通过整合特征和图像来提高混乱检测的准确性。权力不对称等特征随着认知能力的下降而增加，表明大脑半球失衡，而认知指数下降则反映了大脑半球间沟通能力的丧失。此外，包括光谱地形图和基于cwt的尺度图在内的图像提供了空间功率分布和时频特性的全面视图。结论混合RF-CNN方法可以更可靠地分析改变的非线性脑动力学和过渡阶段，使其成为检测神经系统疾病的有价值的工具。

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来源期刊

Journal of Neuroscience Methods 医学-神经科学

CiteScore

7.10

自引率

3.30%

发文量

226

审稿时长

52 days

期刊介绍： The Journal of Neuroscience Methods publishes papers that describe new methods that are specifically for neuroscience research conducted in invertebrates, vertebrates or in man. Major methodological improvements or important refinements of established neuroscience methods are also considered for publication. The Journal''s Scope includes all aspects of contemporary neuroscience research, including anatomical, behavioural, biochemical, cellular, computational, molecular, invasive and non-invasive imaging, optogenetic, and physiological research investigations.