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 \(l_1\) 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，它影响人类的思维能力、记忆能力和生活方式。SZ 患者的人工筛查繁琐、费力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 信号是非线性和非平稳的。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性质。首先，脑电信号经过六次迭代进行小波分解，产生七个子带。\ ( l_1\)为每个子带计算范数。提取的规范特征被传播到各种分类算法。我们使用具有十倍和留一主体交叉验证的 K-最近邻分类器获得了 99.21% 和 97.2% 的最高准确率。开发的基于单通道脑电图小波的 CAD 模型可以帮助临床医生确认其手动筛查的结果并获得准确的诊断。