Padovani Ederli Rebeca, Vega-Oliveros Didier A, Soriano-Vargas Aurea, Rocha Anderson, Dias Zanoni
Institute of Computing, University of Campinas (Unicamp), Campinas, SP, Brazil.
Department of Science and Technology, Federal University of Sao Paulo (Unifesp), São José dos Campos, SP, Brazil.
PLoS One. 2025 May 21;20(5):e0323689. doi: 10.1371/journal.pone.0323689. eCollection 2025.
Polysomnography is the standard method for sleep stage classification; however, it is costly and requires controlled environments, which can disrupt natural sleep patterns. Smartwatches offer a practical, non-invasive, and cost-effective alternative for sleep monitoring. Equipped with multiple sensors, smartwatches allow continuous data collection in home environments, making them valuable for promoting health and improving sleep habits. Traditional methods for sleep stage classification using smartwatch data often rely on raw data or extracted features combined with artificial intelligence techniques. Transforming time series into visual representations enables the application of two-dimensional convolutional neural networks, which excel in classification tasks. Despite their success in other domains, these methods are underexplored for sleep stage classification. To address this, we evaluated visual representations of time series data collected from accelerometer and heart rate sensors in smartwatches. Techniques such as Gramian Angular Field, Recurrence Plots, Markov Transition Field, and spectrograms were implemented. Additionally, image patching and ensemble methods were applied to enhance classification performance. The results demonstrated that Gramian Angular Field, combined with patching and ensembles, achieved superior performance, exceeding 82% balanced accuracy for two-stage classification and 62% for three-stage classification. A comparison with traditional approaches, conducted under identical conditions, showed that the proposed method outperformed others, offering improvements of up to 8 percentage points in two-stage classification and 9 percentage points in three-stage classification. These findings show that visual representations effectively capture key sleep patterns, enhancing classification accuracy and enabling more reliable health monitoring and earlier interventions. This study highlights that visual representations not only surpass traditional methods but also emerge as a competitive and effective approach for sleep stage classification based on smartwatch data, paving the way for future research.
多导睡眠图是睡眠阶段分类的标准方法；然而，它成本高昂且需要可控环境，这可能会扰乱自然睡眠模式。智能手表为睡眠监测提供了一种实用、非侵入性且经济高效的替代方案。智能手表配备了多个传感器，能够在家庭环境中持续收集数据，使其在促进健康和改善睡眠习惯方面具有重要价值。使用智能手表数据进行睡眠阶段分类的传统方法通常依赖原始数据或提取的特征，并结合人工智能技术。将时间序列转换为视觉表示能够应用二维卷积神经网络，这类网络在分类任务中表现出色。尽管这些方法在其他领域取得了成功，但在睡眠阶段分类方面尚未得到充分探索。为了解决这一问题，我们评估了从智能手表中的加速度计和心率传感器收集的时间序列数据的视觉表示。实施了诸如格拉姆角场、递归图、马尔可夫转移场和频谱图等技术。此外，还应用了图像分块和集成方法来提高分类性能。结果表明，格拉姆角场结合分块和集成方法取得了卓越的性能，在两阶段分类中平衡准确率超过82%，在三阶段分类中超过62%。在相同条件下与传统方法进行比较，结果表明所提出的方法优于其他方法，在两阶段分类中提高了多达8个百分点，在三阶段分类中提高了9个百分点。这些发现表明，视觉表示有效地捕捉了关键睡眠模式，提高了分类准确性，并实现了更可靠的健康监测和早期干预。这项研究突出表明，视觉表示不仅超越了传统方法，而且成为基于智能手表数据进行睡眠阶段分类的一种有竞争力且有效的方法，为未来的研究铺平了道路。
