{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T16:40:48Z","timestamp":1793119248528,"version":"4.5.0"},"reference-count":23,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"New Energy and Industrial Technology Development Organization"},{"id":[{"id":"https:\/\/ror.org\/0055k7a87","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Clocks &amp; Sleep"],"abstract":"<jats:p>Respiratory frequency is a critical biomarker in sleep medicine and circadian biology. We investigated whether the high-frequency (HF) component of heart-rate variability (HRV)\u2014which reflects respiratory sinus arrhythmia (RSA)\u2014can serve as a non-invasive proxy for breathing rate estimation from ECG or PPG. We employed a two-stage validation design: (1) a physiologically calibrated simulation study (N = 30 per condition, five conditions, 180 s recordings) for controlled error characterization using the PhysioNet ECG-ID Database (Electrocardiogram Identification Database, DOI: 10.13026\/C2XW26) as the processing pipeline reference; and (2) pilot real-data validation in N = 10 older adult participants (mean age 71.3 years) with simultaneous ECG and thermistor respiratory reference measured using the East Medic Biotope Mini (1000 Hz). Results: Under controlled resting conditions, simulation yielded MAE = 0.46 bpm (SNR = 4.8). An empirical error formula MAE = 2.017 \u00d7 SNR^(\u22121.187) (R2 = 0.71) was derived. In the real-data validation, 4\/10 participants achieved MAE \u2264 2.0 bpm; the remaining 6\/10 showed errors of 6\u201317 bpm attributable to non-respiratory HF oscillations, harmonic confusion, and breathing rate variability. The HF-peak method is reliable when SNR is high and breathing is regular but requires additional quality criteria beyond SNR alone in older adult populations where non-respiratory HF oscillations may confound spectral peak detection.<\/jats:p>","DOI":"10.3390\/clockssleep8030054","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T03:19:26Z","timestamp":1788232766000},"page":"54","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["HRV-Based Respiratory-Rate Estimation in Older Adults: Error Modeling and Pilot Validation for Wearable Sleep Monitoring"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1865-7735","authenticated-orcid":false,"given":"Emi","family":"Yuda","sequence":"first","affiliation":[{"name":"Innovation Center for Semiconductor and Digital Future, Mie University, Tsu 514-8507, Japan"},{"name":"Department of Management Science and Technology, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Naoya","family":"Morikawa","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Graduate School of Engineering, Mie University, Tsu 514-8507, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5340-6325","authenticated-orcid":false,"given":"Junichiro","family":"Hayano","sequence":"additional","affiliation":[{"name":"School of Medicine, Nagoya City University, Nagoya 467-8601, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"zsaf091","DOI":"10.1093\/sleep\/zsaf091","article-title":"Incorporating respiratory signals for machine learning-based multimodal sleep stage classification","volume":"48","author":"Krauss","year":"2025","journal-title":"Sleep"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1111\/jir.13060","article-title":"Automated sleep staging in people with intellectual disabilities using heart rate and respiration variability","volume":"67","author":"Ross","year":"2023","journal-title":"J. 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