Vocational School, Mardin Artuklu University, Mardin, Turkey.
Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey.
J Neural Eng. 2023 Oct 9;20(5). doi: 10.1088/1741-2552/acfe3a.
Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and excessive occurrences of arousals disrupt sleep stage patterns and lead to poor sleep quality, negatively impacting physical and mental health. Polysomnography is a traditional method for arousal and sleep stage detection that is time-consuming and prone to high variability among experts.. In this paper, we propose a novel multi-task learning approach for arousal and sleep stage detection using fully convolutional neural networks. Our model, FullSleepNet, accepts a full-night single-channel EEG signal as input and produces segmentation masks for arousal and sleep stage labels. FullSleepNet comprises four modules: a convolutional module to extract local features, a recurrent module to capture long-range dependencies, an attention mechanism to focus on relevant parts of the input, and a segmentation module to output final predictions.By unifying the two interrelated tasks as segmentation problems and employing a multi-task learning approach, FullSleepNet achieves state-of-the-art performance for arousal detection with an area under the precision-recall curve of 0.70 on Sleep Heart Health Study and Multi-Ethnic Study of Atherosclerosis datasets. For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0.88 and an F1-score of 0.80 on the former and an accuracy of 0.83 and an F1-score of 0.76 on the latter.. Our results demonstrate that FullSleepNet offers improved practicality, efficiency, and accuracy for the detection of arousal and classification of sleep stages using raw EEG signals as input.
睡眠是至关重要的生理过程，对维持身心健康起着至关重要的作用。准确检测觉醒和睡眠阶段对于睡眠障碍的诊断至关重要，因为频繁和过度的觉醒会破坏睡眠阶段模式，导致睡眠质量差，对身心健康产生负面影响。多导睡眠图是一种传统的觉醒和睡眠阶段检测方法，它既耗时又容易在专家之间产生很大的变异性。在本文中，我们提出了一种使用全卷积神经网络进行觉醒和睡眠阶段检测的新的多任务学习方法。我们的模型 FullSleepNet 接受整晚单通道 EEG 信号作为输入，并为觉醒和睡眠阶段标签生成分割掩模。FullSleepNet 由四个模块组成：卷积模块用于提取局部特征，递归模块用于捕获长程依赖关系，注意力机制用于关注输入的相关部分，分割模块用于输出最终预测。通过将两个相关的任务统一为分割问题，并采用多任务学习方法，FullSleepNet 在睡眠心脏健康研究和动脉粥样硬化多民族研究数据集上实现了最先进的觉醒检测性能，精度-召回曲线下面积为 0.70。对于睡眠阶段分类，FullSleepNet 在两个数据集上都获得了可比的性能，在前一个数据集上的准确率为 0.88，F1 得分为 0.80，在后一个数据集上的准确率为 0.83，F1 得分为 0.76。我们的结果表明，FullSleepNet 提供了改进的实用性、效率和准确性，可使用原始 EEG 信号作为输入来检测觉醒和分类睡眠阶段。
