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- W4379743175 abstract "Automatic pain assessment systems can help patients get timely and effective pain relief treatment whenever needed. Such a system aims to provide the service with pain identification and pain intensity rating functions. Among the physiological signals, the electrodermal activity (EDA) signal emerges as a promising feature to support both functions in pain assessment. In this work, we propose a machine learning framework to implement pain identification and pain intensity rating using only EDA and its derived features. Our solution also explores the feasibility of using ultra-short EDA segmentation of about 5 seconds to meet real-time requirements. We evaluate our system on two datasets: Biovid, a publicly available dataset, and Apon, the one we build. Experimental results demonstrate that using just the ultra-short EDA signal as input, our algorithm outperforms state-of-the-art baselines and achieves a low regression error of 0.90." @default.
- W4379743175 created "2023-06-08" @default.
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- W4379743175 date "2023-03-27" @default.
- W4379743175 modified "2023-10-18" @default.
- W4379743175 title "Automatic Pain Assessment with Ultra-short Electrodermal Activity Signal" @default.
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- W4379743175 doi "https://doi.org/10.1145/3555776.3577721" @default.
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