Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab, India.
Machinery Fault Diagnostics & Signal Processing Laboratory, Department of Mechanical Engineering, University Institute of Technology, Guru Nanak Dev University, Amritsar, Punjab, India.
Proc Inst Mech Eng H. 2021 Feb;235(2):167-184. doi: 10.1177/0954411920966937. Epub 2020 Oct 30.
Schizophrenia is a fatal mental disorder, which affects millions of people globally by the disturbance in their thinking, feeling and behaviour. In the age of the internet of things assisted with cloud computing and machine learning techniques, the computer-aided diagnosis of schizophrenia is essentially required to provide its patients with an opportunity to own a better quality of life. In this context, the present paper proposes a spectral features based convolutional neural network (CNN) model for accurate identification of schizophrenic patients using spectral analysis of multichannel EEG signals in real-time. This model processes acquired EEG signals with filtering, segmentation and conversion into frequency domain. Then, given frequency domain segments are divided into six distinct spectral bands like delta, theta-1, theta-2, alpha, beta and gamma. The spectral features including mean spectral amplitude, spectral power and Hjorth descriptors (Activity, Mobility and Complexity) are extracted from each band. These features are independently fed to the proposed spectral features-based CNN and long short-term memory network (LSTM) models for classification. This work also makes use of raw time-domain and frequency-domain EEG segments for classification using temporal CNN and spectral CNN models of same architectures respectively. The overall analysis of simulation results of all models exhibits that the proposed spectral features based CNN model is an efficient technique for accurate and prompt identification of schizophrenic patients among healthy individuals with average classification accuracies of 94.08% and 98.56% for two different datasets with optimally small classification time.
精神分裂症是一种致命的精神障碍，它通过干扰人们的思维、感觉和行为，影响着全球数百万人。在物联网时代，借助云计算和机器学习技术，计算机辅助诊断精神分裂症对于为患者提供更好的生活质量的机会至关重要。在这种情况下，本文提出了一种基于频谱特征的卷积神经网络（CNN）模型，用于使用实时多通道 EEG 信号的频谱分析来准确识别精神分裂症患者。该模型对采集的 EEG 信号进行滤波、分割和转换到频域。然后，将频域段分为六个不同的频谱带，如 delta、theta-1、theta-2、alpha、beta 和 gamma。从每个频段提取频谱特征，包括平均频谱幅度、频谱功率和 Hjorth 描述符（活动、移动性和复杂性）。这些特征分别输入到基于频谱特征的 CNN 和长短时记忆网络（LSTM）模型中进行分类。这项工作还利用原始时域和频域 EEG 段，分别使用相同架构的时域 CNN 和频域 CNN 模型进行分类。对所有模型的模拟结果的综合分析表明，基于频谱特征的 CNN 模型是一种有效的技术，可以准确、迅速地识别健康个体中的精神分裂症患者，在两个不同的数据集上的平均分类准确率分别为 94.08%和 98.56%，且分类时间最优。
