Department of Instrumentation and Control Engineering, Dr B R Ambedkar National Institute of Technology, Jalandhar, Punjab, 144008, India.
Department of Instrumentation and Control Engineering, Dr B R Ambedkar National Institute of Technology, Jalandhar, Punjab, 144008, India.
Psychiatry Res Neuroimaging. 2024 Oct;344:111886. doi: 10.1016/j.pscychresns.2024.111886. Epub 2024 Aug 28.
Verifying schizophrenia (SZ) can be assisted by deep learning techniques and patterns in brain activity observed in alpha-EEG recordings. The suggested research provides evidence of the reliability of alpha-EEG rhythm in a Gated-Recurrent-Unit-based deep-learning model for investigating SZ. This study suggests Rudiment Densely-Coupled Convolutional Gated Recurrent Unit (RDCGRU) for the various EEG-rhythm-based (gamma, beta, alpha, theta, and delta) diagnoses of SZ. The model includes multiple 1-D-Convolution (Con-1-D) folds with steps greater than 1, which enables the model to programmatically and effectively learn how to reduce the incoming signal. The Con-1-D layers and numerous Gated Recurrent Unit (GRU) layers comprise the Exponential-Linear-Unit activation function. This powerful activation function facilitates in-deep-network training and improves classification performance. The Densely-Coupled Convolutional Gated Recurrent Unit (DCGRU) layers enable RDCGRU to address the training accuracy loss brought on by vanishing or exploding gradients, and this might make it possible to develop intense, deep versions of RDCGRU for more complex problems. The sigmoid activation function is implemented in the digital (binary) classifier's output nodes. The RDCGRU deep learning model attained the most excellent accuracy, 88.88 %, with alpha-EEG rhythm. The research achievements: The RDCGRU deep learning model's GRU cells responded superiorly to the alpha-EEG rhythm in EEG-based verification of SZ.
深度学习技术和大脑活动模式可辅助精神分裂症（SZ）的诊断，从α-EEG 记录中观察到的模式。本研究提出的基于门控循环单元（GRU）的深度学习模型中，α-EEG 节律具有可靠的研究结果，为探索 SZ 提供了证据。该研究建议使用 Rudiment Densely-Coupled Convolutional Gated Recurrent Unit（RDCGRU）进行各种 EEG 节律（γ、β、α、θ 和 δ）的 SZ 诊断。该模型包括多个 1-D-卷积（Con-1-D）折叠，步长大于 1，这使模型能够以编程和有效的方式学习如何减少输入信号。Con-1-D 层和多个门控循环单元（GRU）层组成指数线性单元激活函数。这种强大的激活函数有助于在深网络训练中，提高分类性能。Densely-Coupled Convolutional Gated Recurrent Unit（DCGRU）层使 RDCGRU 能够解决因梯度消失或爆炸而导致的训练精度损失问题，这可能使开发更复杂问题的密集、深度版本的 RDCGRU 成为可能。在数字（二进制）分类器的输出节点中实现了 sigmoid 激活函数。RDCGRU 深度学习模型在基于 EEG 的 SZ 验证中，利用α-EEG 节律达到了最佳的 88.88%的准确率。研究成果：RDCGRU 深度学习模型的 GRU 细胞对 EEG 中α-EEG 节律的反应优于其他节律。
