2026-10-19T01:04:42Zhttps://ebyu-rdlopen.rawdatalibrary.net/oai/request

oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/102832026-10-18T20:36:54Zopenairedrivercom_ebyu_10000col_ebyu_10001

State-specific respiratory signatures for affective and stress recognition: Interpretable respiratory markers, autocorrelation lags, and compact CNN modelsVelichko, AndreiHuyut, Mehmet TahirAffective-state recognitionAutocorrelation lagInterpretable featuresLOSO validationOne-dimensional convolutional neural networkOne-vs-rest analysisRespiratory signalStress recognitionWESADConvolutionConvolutional neural networksCorrelation detectorsFeature extractionPhysiological modelsPhysiologyRespiratorsRespiratory mechanicsSignal analysisState estimationStressesWearable technologyAffective stateAuto correlationConvolutional neural networkInterpretable featureOne-dimensionalOne-vs-rest analyzeRespiratory signalsState recognitionAutocorrelationRespiratory 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. This work reframes RESP-based recognition as a joint predictive and explanatory problem. 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 application task is binary 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. © 2026 Elsevier LtdElsevier Ltd2027Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/10283https://doi.org/10.1016/j.bspc.2026.1113731746-8094enBiomedical Signal Processing and Controlinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/10283