This paper proposes a new approach to identifying patients with insomnia using a single EEG channel, without the need for sleep stage annotation. Data preprocessing, feature extraction, feature selection, and classification techniques are used to automatically detect insomnia based on features extracted from spectral and temporal domains, including relative power in the delta, sigma, beta and gamma bands, total power, absolute slow wave power, power ratios, mean, zero crossing rate, mobility, and complexity. A Pearson correlation coefficient, t-test, p-value, and two rules are used to select the optimal set of features for accurately classifying insomnia patients and rejecting negatively affecting features. Classification schemes including a general artificial neural network, convolutional neural network, and support vector machine are applied to the optimal feature set to distinguish between insomnia patients and healthy subjects. The performance of the model is validated using 50 insomnia patients and 50 healthy subjects, with the Fp2 channel and 1D-CNN classifier achieving the highest accuracy and Cohen's kappa coefficient at 97.85% and 94.15%, respectively. The developed model has the potential to simplify current sleep monitoring systems and enable in-home ambulatory monitoring.

翻译：本文提出了一种基于单通道脑电信号、无需睡眠分期标注的失眠患者识别新方法。该方法通过数据预处理、特征提取、特征选择及分类技术，利用从频谱域和时域提取的特征自动检测失眠，这些特征包括δ、σ、β和γ波段的相对功率、总功率、绝对慢波功率、功率比、均值、过零率、活动性及复杂度。采用皮尔逊相关系数、t检验、p值及两条规则来选择最优特征集，以准确分类失眠患者并剔除负面影响特征。将通用人工神经网络、卷积神经网络和支持向量机等分类方案应用于最优特征集，以区分失眠患者与健康受试者。模型性能通过50名失眠患者和50名健康受试者进行验证，其中Fp2通道和1D-CNN分类器取得了最高的准确率和Cohen's kappa系数，分别为97.85%和94.15%。所开发的模型有望简化现有睡眠监测系统，并实现家庭动态监测。