Sharma Manish, Acharya U Rajendra
Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad, India.
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore.
Cogn Neurodyn. 2021 Aug;15(4):661-674. doi: 10.1007/s11571-020-09655-w. Epub 2021 Jan 15.
Schizophrenia (SZ) is a mental disorder, which affects the ability of human thinking, memory, and way of living. Manual screening of SZ patients is tedious, laborious and prone to human errors. Hence, we developed a computer-aided diagnosis (CAD) system to diagnose SZ patients accurately using single-channel electroencephalogram (EEG) signals. The EEG signals are nonlinear and non-stationary. Hence, we have used wavelet-based features to capture the hidden non-stationary nature present in the signal. First, the EEG signals are subjected to the the wavelet decomposition through six iterations, which yields seven sub-bands. The norm is computed for each sub-band. The extracted norm features are disseminated to various classification algorithms. We have obtained the highest accuracy of 99.21% and 97.2% using K-nearest neighbor classifiers with ten-fold and leave-one-subject-out cross-validations. The developed single-channel EEG wavelet-based CAD model can help the clinicians to confirm the outcome of their manual screening and obtain an accurate diagnosis.
精神分裂症（SZ）是一种精神障碍，它会影响人类的思维能力、记忆力和生活方式。对SZ患者进行人工筛查既繁琐又费力，而且容易出现人为错误。因此，我们开发了一种计算机辅助诊断（CAD）系统，以使用单通道脑电图（EEG）信号准确诊断SZ患者。EEG信号是非线性且非平稳的。因此，我们使用基于小波的特征来捕捉信号中存在的隐藏的非平稳特性。首先，将EEG信号进行六次迭代的小波分解，产生七个子带。计算每个子带的范数。提取的范数特征被分发给各种分类算法。使用具有十折交叉验证和留一法交叉验证的K近邻分类器，我们分别获得了99.21%和97.2%的最高准确率。所开发的基于单通道EEG小波的CAD模型可以帮助临床医生确认其人工筛查的结果并获得准确的诊断。
