19 citations · 23 across the 4 of their papers we have counts for
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eess.SP2024
Optimizing Photoplethysmography-Based Sleep Staging Models by Leveraging Temporal Context for Wearable Devices Applications
Joseph A. P. Quino, Diego A. C. Cardenas, Marcelo A. F. Toledo +4
Accurate sleep stage classification is crucial for diagnosing sleep disorders and evaluating sleep quality. While polysomnography (PSG) remains the gold standard, photoplethysmogra…
eess.SP2023★ 4 cited
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 +5
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 functi…