Bagherzadeh Sara, Shalbaf Ahmad
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Cogn Neurodyn. 2024 Oct;18(5):2767-2778. doi: 10.1007/s11571-024-10121-0. Epub 2024 May 9.
Schizophrenia (SZ) is a serious mental disorder that can mainly be distinguished by symptoms including delusions and hallucinations. This mental disorder makes difficult conditions for the person and her/his relatives. Electroencephalogram (EEG) signal is a sophisticated neuroimaging technique that helps neurologists to diagnose this mental disorder. Estimating and evaluating brain effective connectivity between electrode pairs is an appropriate way of diagnosing brain states in neuroscience studies. In this study, we construct a novel image from multi-channels of EEG based on the fusion of three effective connectivity, partial directed coherence (PDC), and direct directed transfer function (dDTF) and transfer entropy (TE) at three consecutive time windows. Then, this image was used as input of five well-known convolutional neural networks (CNNs) through transfer learning (TL) to learn patterns related to SZ patients to diagnose this disorder from normal participants from two public databases. Also, the majority voting method was used to improve these results based on ensemble results of the five CNNs, i.e., ResNet-50, Inception-v3, DenseNet-201, EfficientNetB0, and NasNet-Mobile. The highest average accuracy, specificity and sensitivity to diagnose SZ patients from healthy participants were obtained using EfficientNetB0 through the Leave-One-Subject-out (LOSO) Cross-Validation criterion equal to 96.67%, 96.23%, 96.82%, 95.15%, 94.42% and 96.28% for the first and second databases, respectively. Also, as we suggested, the ensemble approach of EfficientNetB0, ResNet-50 and NasNet-Mobile increased the accuracy by approximately 3%. Our results show the effectiveness of providing fused images from multichannel EEG signals to the ensemble of CNNs through TL to diagnose SZ than state-of-the-art studies.
精神分裂症（SZ）是一种严重的精神障碍，主要可通过妄想和幻觉等症状来区分。这种精神障碍给患者及其亲属带来了困难。脑电图（EEG）信号是一种复杂的神经成像技术，有助于神经科医生诊断这种精神障碍。估计和评估电极对之间的大脑有效连接性是神经科学研究中诊断大脑状态的一种合适方法。在本研究中，我们基于三个连续时间窗口的三种有效连接性、偏相干（PDC）、直接定向传递函数（dDTF）和转移熵（TE）的融合，从多通道EEG构建了一幅新颖的图像。然后，通过迁移学习（TL）将这幅图像用作五个著名卷积神经网络（CNN）的输入，以学习与SZ患者相关的模式，从而从两个公共数据库的正常参与者中诊断这种疾病。此外，基于五个CNN（即ResNet - 50、Inception - v3、DenseNet - 201、EfficientNetB0和NasNet - Mobile）的集成结果，使用多数投票方法来改进这些结果。通过留一受试者出（LOSO）交叉验证标准，使用EfficientNetB0诊断SZ患者与健康参与者时，在第一个和第二个数据库中分别获得了最高平均准确率、特异性和敏感性，分别为96.67%、96.23%、96.82%、95.15%、94.42%和96.28%。此外，正如我们所建议的，EfficientNetB0、ResNet - 50和NasNet - Mobile的集成方法使准确率提高了约3%。我们的结果表明，通过TL为CNN集成提供多通道EEG信号的融合图像来诊断SZ比现有研究更有效。
