ospEDA decomposes EDA signals into tonic and phasic parts via valley detection, orthogonal subspace projection, and NNLS deconvolution, achieving lower RMSE and higher F1 scores than six prior methods across simulated noise levels and five real datasets.
Towards real-world wearable sleepiness detection: Electrodermal activity data during speech can identify sleep deprivation
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ospEDA: Orthogonal Subspace Projection for Electrodermal Activity Decomposition
ospEDA decomposes EDA signals into tonic and phasic parts via valley detection, orthogonal subspace projection, and NNLS deconvolution, achieving lower RMSE and higher F1 scores than six prior methods across simulated noise levels and five real datasets.