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

• URL: http://arxiv.org/abs/2308.05759v1
• Date: Mon, 7 Aug 2023 13:43:19 GMT
• Title: A machine-learning sleep-wake classification model using a reduced number of features derived from photoplethysmography and activity signals
• Authors: 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
• Abstract summary: Photoplethys (mography) has been demonstrated to be an effective signal for sleep stage inference. In this work, we present a machine learning sleep-wake classification model based on the eXtreme Gradient Boosting algorithm. 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%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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