Actigraphy is widely used in sleep studies but lacks a universal unsupervised algorithm for sleep/wake identification. An unsupervised algorithm is useful in large-scale population studies and in cases where polysomnography (PSG) is unavailable, as it does not require sleep outcome labels to train the model but utilizes information solely contained in actigraphy to learn sleep and wake characteristics and separate the two states. In this study, we proposed a machine learning unsupervised algorithm based on the Hidden Markov Model (HMM) for sleep/wake identification. The proposed algorithm is also an individualized approach that takes into account individual variabilities and analyzes each individual actigraphy profile separately to infer sleep and wake states. We used Actiwatch and PSG data from 43 individuals in the Multi-Ethnic Study of Atherosclerosis study to evaluate the method performance. Epoch-by-epoch comparisons and sleep variable comparisons were made between our algorithm, the unsupervised algorithm embedded in the Actiwatch software (AS), and the pre-trained supervised UCSD algorithm. Using PSG as the reference, the accuracy was 85.7% for HMM, 84.7% for AS, and 85.0% for UCSD. The sensitivity was 99.3%, 99.7%, and 98.9% for HMM, AS, and UCSD, respectively, and the specificity was 36.4%, 30.0%, and 31.7%, respectively. The Kappa statistic was 0.446 for HMM, 0.399 for AS, and 0.311 for UCSD, suggesting fair to moderate agreement between PSG and actigraphy. The Bland–Altman plots further show that the total sleep time, sleep latency, and sleep efficiency estimates by HMM were closer to PSG with narrower 95% limits of agreement than AS and UCSD. All three methods tend to overestimate sleep and underestimate wake compared to PSG. Our HMM approach is also able to differentiate relatively active and sedentary individuals by quantifying variabilities in activity counts: individuals with higher estimated activity variabilities tend to show more frequent sedentary behaviors. Our unsupervised data-driven HMM algorithm achieved better performance than the commonly used Actiwatch software algorithm and the pre-trained UCSD algorithm. HMM can help expand the application of actigraphy in cases where PSG is hard to acquire and supervised methods cannot be trained. In addition, the estimated HMM parameters can characterize individual activity patterns and sedentary tendencies that can be further utilized in downstream analysis.

书法在睡眠研究中被广泛使用，但缺乏通用的无监督算法来识别睡眠/觉醒。无监督算法在大规模人群研究中以及在无法使用多导睡眠图（PSG）的情况下非常有用，因为它不需要睡眠结果标签来训练模型，而是利用仅包含在书法中的信息来学习睡眠和唤醒特征并分离睡眠两种状态。在这项研究中，我们提出了一种基于隐马尔可夫模型（HMM）的机器学习无监督算法，用于睡眠/唤醒识别。所提出的算法也是一种个性化的方法，该方法考虑了个体差异性并分别分析每个个体书法轮廓以推断睡眠和唤醒状态。在多族裔动脉粥样硬化研究中，我们使用了来自43个个体的Actiwatch和PSG数据来评估方法的性能。在我们的算法，Actiwatch软件（AS）中嵌入的非监督算法与预训练的监督UCSD算法之间进行了逐时比较和睡眠变量比较。以PSG为参考，HMM的准确度为85.7％，AS的准确度为84.7％，UCSD的准确度为85.0％。HMM，AS和UCSD的敏感性分别为99.3％，99.7％和98.9％，特异性分别为36.4％，30.0％和31.7％。HMM的Kappa统计数据为0.446，AS的Kappa统计数据为0.399，而UCSD的Kappa统计数据为0.311，这表明PSG与书法之间的共识是中等至中等。布兰德·奥特曼（Bland–Altman）图进一步显示总睡眠时间，睡眠潜伏期，HMM估计的睡眠效率更接近PSG，其协议限制比AS和UCSD缩小了95％。与PSG相比，这三种方法都倾向于高估睡眠和低估唤醒。我们的HMM方法还能够通过量化活动计数的差异来区分相对活跃和久坐的个体：具有较高估计活动变异性的个体往往表现出更频繁的久坐行为。我们的无监督数据驱动HMM算法取得了比常用Actiwatch软件算法和预训练UCSD算法更好的性能。在PSG难以获取且无法训练监督方法的情况下，HMM可以帮助扩大书画的应用范围。此外，