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arXiv Machine Learning By Andrei Velichko, Mehmet Tahir Huyut

State-specific respiratory signatures for affective and stress recognition: Interpretable respiratory markers, autocorrelation lags, and compact CNN models

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The study investigates respiratory signals from the WESAD dataset to detect stress and other affective states. It compares compact 1‑D CNN models trained on raw 60‑second signals with handcrafted respiratory signatures that capture timing, variability, waveform, spectral, and autocorrelation features. While the CNN achieves the highest accuracy for stress detection, the handcrafted signatures provide stronger, physiologically interpretable markers for baseline, amusement, and especially meditation states.

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