{
  "abstract": "Respiratory activity is a direct and interpretable physiological channel for wearable stress and affective-state recognition, yet many studies emphasize classification accuracy without identifying which respiratory properties separate different states. Using the chest respiratory channel of the WESAD dataset, we analyze 60 s windows under leave-one-subject-out validation and combine two complementary branches: compact raw-signal one-dimensional convolutional neural networks (1D-CNNs) and physically grouped handcrafted respiratory signatures. The primary task is stress versus non-stress detection, while baseline, stress, amusement, and meditation are additionally analyzed in a one-vs-rest setting to reveal state-specific respiratory markers. The feature space is organized into respiratory timing, breath-to-breath variability, waveform statistics, spectral/time-frequency descriptors, and autocorrelation/nonlinear predictability descriptors, with the raw 60 s signal treated as a sixth representation for the CNN branch. We introduce autocorrelation transition lags (Zpm/Zmp) as interpretable markers of respiratory correlation scale and separately evaluate exploratory forecast-error-growth/Lyapunov-like descriptors. In the final CNN refit setting, the raw-signal model achieved the strongest stress-vs-rest performance (accuracy 96.72%, macro-F1 95.30%, MCC 90.61%). In contrast, compact feature models were stronger for baseline (MCC 65.34%), amusement (MCC 35.69%), and especially meditation (MCC 88.65%). Strict nested reanalysis of the auxiliary Top-20 stress search yielded mean MCC=82.29% versus 85.45% originally, indicating modest selection optimism. These results show that CNNs are most useful for the practical stress detector, whereas interpretable respiratory signatures provide stronger and more physiologically transparent state-specific markers for several non-stress conditions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Biomedical Signal Processing and Control, Volume 129, Part A, 1 January 2027, Article 111373",
  "arxiv": "2606.26723",
  "author_total": 2,
  "authors": [
    {
      "name": "Andrei Velichko"
    },
    {
      "name": "Mehmet Tahir Huyut"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/52kny2jsprkx7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.26723",
  "fulltext_url": null,
  "id": "52kny2jsprkx7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.26723",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "eess.SP"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2606.26723",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/52kny2jsprkx7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:12Z",
      "native_id": "2606.26723",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.26723"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "State-specific respiratory signatures for affective and stress recognition: Interpretable respiratory markers, autocorrelation lags, and compact CNN models",
  "title_text": "State-specific respiratory signatures for affective and stress recognition: Interpretable respiratory markers, autocorrelation lags, and compact CNN models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:12Z",
  "url": "https://research.draconitic.com/paper/52kny2jsprkx7",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1016/j.bspc.2026.111373",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}