Jalan Alka, Mishra Deepti, Gupta Manjari
Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi 221005, India.
Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 2815 Gjøvik, Norway.
Biomimetics (Basel). 2025 Jul 7;10(7):449. doi: 10.3390/biomimetics10070449.
Diagnosing schizophrenia using Electroencephalograph (EEG) signals is a challenging task due to the subtle and overlapping differences between patients and healthy individuals. To overcome this difficulty, deep learning has shown strong potential, especially given its success in image recognition tasks. In many studies, one-dimensional EEG signals are transformed into two-dimensional representations to allow for image-based analysis. In this work, we have used the Markov Transition Field for converting EEG signals into two-dimensional images, capturing both the temporal patterns and statistical dynamics of the data. EEG signals are continuous time-series recordings from the brain, where the current state is often influenced by the immediately preceding state. This characteristic makes MTF particularly suitable for representing such data. After the transformation, a pre-trained VGG-16 model is employed to extract meaningful features from the images. The extracted features are then passed through two separate classification pipelines. The first uses a traditional machine learning model, Support Vector Machine, while the second follows a deep learning approach involving an autoencoder for feature selection and a neural network for final classification. The experiments were conducted using EEG data from the open-access Schizophrenia EEG database provided by MV Lomonosov Moscow State University. The proposed method achieved a highest classification accuracy of 98.51 percent and a recall of 100 percent across all folds using the deep learning pipeline. The Support Vector Machine pipeline also showed strong performance with a best accuracy of 96.28 percent and a recall of 97.89 percent. The proposed deep learning model represents a biomimetic approach to pattern recognition and decision-making.
由于精神分裂症患者与健康个体之间存在细微且相互重叠的差异，利用脑电图（EEG）信号诊断精神分裂症是一项具有挑战性的任务。为了克服这一困难，深度学习已显示出强大的潜力，特别是鉴于其在图像识别任务中的成功。在许多研究中，一维EEG信号被转换为二维表示，以便进行基于图像的分析。在这项工作中，我们使用马尔可夫转移场将EEG信号转换为二维图像，以捕捉数据的时间模式和统计动态。EEG信号是大脑的连续时间序列记录，其中当前状态通常受紧前状态的影响。这一特性使得MTF特别适合表示此类数据。转换后，采用预训练的VGG-16模型从图像中提取有意义的特征。然后将提取的特征通过两个单独的分类管道。第一个使用传统机器学习模型支持向量机，而第二个采用深度学习方法，包括用于特征选择的自动编码器和用于最终分类的神经网络。实验使用了由莫斯科国立罗蒙诺索夫大学提供的开放获取精神分裂症EEG数据库中的EEG数据。所提出的方法在使用深度学习管道的所有折叠中实现了98.51%的最高分类准确率和100%的召回率。支持向量机管道也表现出强大的性能，最佳准确率为96.28%，召回率为97.89%。所提出的深度学习模型代表了一种用于模式识别和决策的仿生方法。
