Andhare Maithili Shailesh, Vijayan T, Karthik B, Urooj Shabana
Department of Electronics Communication Engineering, Bharath Institute of Higher Education and Research, Chennai 600073, India.
Department of Electronics and Telecommunication Engineering, Pimpri Chinchwad College of Engineering and Research Ravet, Pune 412101, India.
Brain Sci. 2025 Aug 4;15(8):835. doi: 10.3390/brainsci15080835.
Mental stress is a psychological or emotional strain that typically occurs because of threatening, challenging, and overwhelming conditions and affects human behavior. Various factors, such as professional, environmental, and personal pressures, often trigger it. In recent years, various deep learning (DL)-based schemes using electroencephalograms (EEGs) have been proposed. However, the effectiveness of DL-based schemes is challenging because of the intricate DL structure, class imbalance problems, poor feature representation, low-frequency resolution problems, and complexity of multi-channel signal processing. This paper presents a novel hybrid DL framework, BDDNet, which combines a deep convolutional neural network (DCNN), bidirectional long short-term memory (BiLSTM), and deep belief network (DBN). BDDNet provides superior spectral-temporal feature depiction and better long-term dependency on the local and global features of EEGs. BDDNet accepts multiple EEG features (MEFs) that provide the spectral and time-domain features of EEGs. A novel improved crow search algorithm (ICSA) was presented for channel selection to minimize the computational complexity of multichannel stress detection. Further, the novel employee optimization algorithm (EOA) is utilized for the hyper-parameter optimization of hybrid BDDNet to enhance the training performance. The outcomes of the novel BDDNet were assessed using a public DEAP dataset. The BDDNet-ICSA offers improved recall of 97.6%, precision of 97.6%, F1-score of 97.6%, selectivity of 96.9%, negative predictive value NPV of 96.9%, and accuracy of 97.3% to traditional techniques.
心理压力是一种心理或情绪上的紧张状态，通常由于具有威胁性、挑战性和压倒性的状况而产生，并影响人类行为。各种因素，如职业、环境和个人压力，常常会引发心理压力。近年来，已经提出了各种基于深度学习（DL）的脑电图（EEG）方案。然而，由于深度学习结构复杂、类别不平衡问题、特征表示不佳、低频分辨率问题以及多通道信号处理的复杂性，基于深度学习的方案的有效性具有挑战性。本文提出了一种新颖的混合深度学习框架BDDNet，它结合了深度卷积神经网络（DCNN）、双向长短期记忆（BiLSTM）和深度信念网络（DBN）。BDDNet提供了卓越的频谱-时间特征描述，并且对脑电图的局部和全局特征具有更好的长期依赖性。BDDNet接受多个脑电图特征（MEF），这些特征提供了脑电图的频谱和时域特征。提出了一种新颖的改进乌鸦搜索算法（ICSA）用于通道选择，以最小化多通道压力检测的计算复杂性。此外，新颖的员工优化算法（EOA）用于混合BDDNet的超参数优化，以提高训练性能。使用公开的DEAP数据集评估了新颖的BDDNet的结果。与传统技术相比，BDDNet-ICSA的召回率提高到了97.6%，精确率为97.6%，F1分数为97.6%，选择性为96.9%，阴性预测值NPV为96.9%，准确率为97.3%。
