Sleep monitoring has traditionally required expensive equipment and expert assessment. Wearable devices are however becoming a viable option for monitoring sleep. This study investigates methods for autonomously identifying sleep segments base on wearable device data. We employ and evaluate machine and deep learning models on the benchmark MESA dataset, with results showing that they outperform traditional methods in terms of accuracy, F1 score, and Matthews Correlation Coefficient (MCC). The most accurate model, namely Light Gradient Boosting Machine, obtained an F1 score of 0.93 and an MCC of 0.73. Additionally, sleep quality metrics were used to assess the models. Furthermore, it should be noted that the proposed approach is device-agnostic, and more accessible and cost-effective than the traditional polysomnography (PSG) methods.