Deep Learning Based Classification and Combined Transform Based Feature Extraction Approach for Mental Stress Prediction of Human Beings Using EEG

IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS
Shashibala Agarwal, Maria Jamal, Parmod Kumar
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引用次数: 0

Abstract

Stress is a psychological condition in which a person feels overwhelmed with pressure. Early identification of psychological stress is critical for preventing illness progression and saving lives. Electroencephalography (EEG) is often used to collect psychological information such as brain rhythms in the form of electric waves. Traditional deep learning techniques face limitations like temporal dynamics and feature extraction issues. To address these shortcomings, a deep learning-based classification model was created, combining advanced transform-based feature extraction techniques to more effectively predict mental stress by using EEG signals. The process begins by utilizing physiological parameters extracted from the EEG Psychiatric Disorders Dataset. The raw EEG signals undergo pre-processing to enhance their quality, which includes smoothing, alignment, and addressing non-uniform sampling issues. The signals are then decomposed and their components extracted using the Adaptive Flexible Analytic Wavelet Transform (AFAWT). Short-Term Fourier Transform-Randon Transform (STFT-RT) approach is used to extract the key features of signals. Feature selection is optimized using the Young's Double Slit Experiment Optimizer (YDSE) to ensure only the most relevant features are chosen for classification. Finally, these selected features are fed into the Parallel Neural Networks with Extreme Efficiency (ParNeXt v1-DB) model, which utilizes a drop block mechanism to enhance model generalization and prevent overfitting, ensuring highly effective mental stress prediction. According to simulated research, the proposed approach demonstrated significant improvements over existing algorithms. In Dataset 1, the method achieved an accuracy of 97.8%, selectivity of 95.4%, while Dataset 2 recorded an accuracy of 96.3%, PPV of 93.8%. Thus, the proposed method is the most effective method for predicting human mental stress using EEG.

基于深度学习分类和组合变换特征提取的脑电心理压力预测方法
压力是一种心理状态,一个人感到压力过大。早期识别心理压力对于预防疾病进展和挽救生命至关重要。脑电图(EEG)常用于以电波形式收集脑节律等心理信息。传统的深度学习技术面临着时间动态和特征提取等问题的局限性。为了解决这些不足,我们建立了一个基于深度学习的分类模型,结合先进的基于变换的特征提取技术,更有效地利用脑电图信号预测精神压力。该过程首先利用从脑电图精神障碍数据集提取的生理参数。原始EEG信号经过预处理以提高其质量,其中包括平滑,对齐和解决非均匀采样问题。然后利用自适应柔性解析小波变换(AFAWT)对信号进行分解并提取其分量。采用短时傅里叶变换-随机变换(STFT-RT)方法提取信号的关键特征。使用杨氏双缝实验优化器(YDSE)优化特征选择,以确保只选择最相关的特征进行分类。最后,将这些选择的特征输入到Parallel Neural Networks with Extreme Efficiency (ParNeXt v1-DB)模型中,该模型利用drop block机制增强模型泛化并防止过拟合,从而确保高效的心理压力预测。仿真研究表明,该方法比现有算法有显著改进。在数据集1中,该方法的准确率为97.8%,选择性为95.4%;在数据集2中,该方法的准确率为96.3%,PPV为93.8%。因此,该方法是利用脑电图预测人类精神压力的最有效方法。
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来源期刊
CiteScore
8.90
自引率
13.90%
发文量
249
期刊介绍: ransactions on Emerging Telecommunications Technologies (ETT), formerly known as European Transactions on Telecommunications (ETT), has the following aims: - to attract cutting-edge publications from leading researchers and research groups around the world - to become a highly cited source of timely research findings in emerging fields of telecommunications - to limit revision and publication cycles to a few months and thus significantly increase attractiveness to publish - to become the leading journal for publishing the latest developments in telecommunications
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