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Sleep is a crucial aspect of our overall health and well-being. It plays avital role in regulating our mental and physical health, impacting our mood,memory, and cognitive function to our physical resilience and immune system.The classification of sleep stages is a mandatory step to assess sleep quality,providing the metrics to estimate the quality of sleep and how well our body isfunctioning during this essential period of rest. Photoplethysmography (PPG)has been demonstrated to be an effective signal for sleep stage inference,meaning it can be used on its own or in a combination with others signals todetermine sleep stage. This information is valuable in identifying potentialsleep issues and developing strategies to improve sleep quality and overallhealth. In this work, we present a machine learning sleep-wake classificationmodel based on the eXtreme Gradient Boosting (XGBoost) algorithm and featuresextracted from PPG signal and activity counts. The performance of our methodwas comparable to current state-of-the-art methods with a Sensitivity of 91.15$\pm$ 1.16%, Specificity of 53.66 $\pm$ 1.12%, F1-score of 83.88 $\pm$ 0.56%,and Kappa of 48.0 $\pm$ 0.86%. Our method offers a significant improvement overother approaches as it uses a reduced number of features, making it suitablefor implementation in wearable devices that have limited computational power.

A machine-learning sleep-wake classification model using a reduced number of features derived from photoplethysmography and activity signals