Nunes Adonay S, Patterson Matthew R, Gerstel Dawid, Khan Sheraz, Guo Christine C, Neishabouri Ali
ActiGraph LLC, Pensacola, FL 32502, USA.
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Sensors (Basel). 2024 Dec 14;24(24):7982. doi: 10.3390/s24247982.
Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural network (DACNN) model to this task and demonstrated that this new model outperformed existing sleep algorithms in classifying sleep-wake and estimating sleep outcomes based on wrist-worn accelerometry. This model generalized well to another dataset based on different wearable devices and activity counts, achieving an accuracy of 80.1% (sensitivity 84% and specificity 58%). Compared to commonly used sleep algorithms, this model resulted in the smallest error in wake after sleep onset (MAE of 48.7, Cole-Kripke of 86.2, Sadeh of 108.2, z-angle of 57.5) and sleep efficiency (MAE of 11.8, Cole-Kripke of 18.4, Sadeh of 23.3, z-angle of 9.3) outcomes. Despite being around for many years, accelerometer-alone devices continue to be useful due to their low cost, long battery life, and ease of use. Improving the accuracy and generalizability of sleep algorithms for accelerometer wrist devices is of utmost importance. We here demonstrated that domain adversarial convolutional neural networks can improve the overall accuracy, especially the specificity, of sleep-wake classification using wrist-worn accelerometer data, substantiating its use as a scalable and valid approach for sleep outcome assessment in real life.
可穿戴式加速度计作为一种生态有效且可扩展的解决方案，在临床研究和护理中被广泛用于长期居家睡眠监测。在本研究中，我们将深度学习领域对抗卷积神经网络（DACNN）模型应用于该任务，并证明该新模型在基于手腕佩戴的加速度计数据进行睡眠-清醒分类和估计睡眠结果方面优于现有睡眠算法。该模型能够很好地推广到基于不同可穿戴设备和活动计数的另一个数据集，准确率达到80.1%（敏感性84%，特异性58%）。与常用的睡眠算法相比，该模型在睡眠开始后的清醒时间（平均绝对误差为48.7，科尔-克里普克指数为86.2，萨德指数为108.2，z角为57.5）和睡眠效率（平均绝对误差为11.8，科尔-克里普克指数为18.4，萨德指数为23.3，z角为9.3）结果方面产生的误差最小。尽管加速度计单独使用的设备已经存在多年，但由于其成本低、电池续航时间长且易于使用，仍然很有用。提高用于加速度计手腕设备的睡眠算法的准确性和通用性至关重要。我们在此证明，领域对抗卷积神经网络可以提高使用手腕佩戴的加速度计数据进行睡眠-清醒分类的整体准确性，尤其是特异性，证实了其作为一种可扩展且有效的现实生活中睡眠结果评估方法的用途。
