A machine-learning sleep-wake classification model using a reduced
  number of features derived from photoplethysmography and activity signals
Douglas A. Almeida Felipe M. Dias Marcelo A. F. Toledo Diego A. C. Cardenas Filipe A. C. Oliveira Estela Ribeiro Jose E. Krieger Marco A. Gutierrez
Sleep is a crucial aspect of our overall health and well-being. It plays a
vital 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 is
functioning 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 to
determine sleep stage. This information is valuable in identifying potential
sleep issues and developing strategies to improve sleep quality and overall
health. In this work, we present a machine learning sleep-wake classification
model based on the eXtreme Gradient Boosting (XGBoost) algorithm and features
extracted from PPG signal and activity counts. The performance of our method
was 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 over
other approaches as it uses a reduced number of features, making it suitable
for implementation in wearable devices that have limited computational power.