A1 Refereed original research article in a scientific journal
Adaptive Autocorrelation Based Heart Rate Estimation from Single-Axis Seismocardiogram: A Comprehensive Benchmark Across Six Diverse Datasets
Authors: Ullah, Ajdar; Elnaggar, Ismail; Seifizarei, Sepehr; Lahdenoja, Olli; Azmat, Usman; Jaakkola, Jussi; Jaakkola, Samuli; Vasankari, Tuija; Airaksinen, Juhani; Kiviniemi, Tuomas; Koivisto, Tero; Liljeberg, Pasi
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publication year: 2026
Journal: IEEE Journal of Biomedical and Health Informatics
ISSN: 2168-2194
eISSN: 2168-2208
DOI: https://doi.org/10.1109/JBHI.2026.3665979
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Partially Open Access publication channel
Web address : https://doi.org/10.1109/jbhi.2026.3665979
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/515912195
Self-archived copy's licence: CC BY
Self-archived copy's version: Publisher`s PDF
We introduce AACFD (Adaptive Autocorrelation Function Detector), a lightweight, fully automatic pipeline for estimating window-averaged heart rate (HR) from a single-axis seismocardiogram (SCG) without ECG calibration or machine learning. AACFD combines two periodicity detectors—(i) a YIN-style difference-function analysis of an adaptive SCG envelope and (ii) a short-window autocorrelation branch—operating on 3 s segments. The two branches are fused by feature-aware weighting based on signal-quality indices and multi-scale features; and a Hampel filter plus temporal consistency checks remove outlier windows. The algorithm is efficient enough for realtime implementation on commodity hardware. AACFD was validated on 535 subjects spanning six heterogeneous datasets (sampling rates 200 Hz–5 kHz): laboratory mechanocardiograms (MCG), controlled-breathing SCG (CEBS), motion-rich multichannel SCG (MC-SCG), valvular heart-disease clinics (VHD), invasive right-heart catheterization (RHC), and smartphone recordings from 300 atrial-fibrillation/sinus-rhythm subjects. For each recording we analyzed non-overlapping windows of 10, 20, 30, and 60 s and compared the SCG-derived mean HR with ECGderived (or telemetry) reference values. After ECG- and SCG-based quality control and temporal refinement, the mean per-subject mean absolute error (MAE) on 30-s windows was 0.99 bpm in MCG, 0.72 bpm in CEBS, 4.38 bpm in VHD, 1.34 bpm in MC-SCG after removal of recordings with corrupted ECG, 3.58 bpm in RHC, and 7.67 bpm in the smartphone cohort. Across all datasets, more than 90% of non-overlapping 10–60 s windows passed ECG- and SCGbased quality control, so the reported errors reflect nearly the full usable recording duration rather than a few selected clean segments. This cross-dataset benchmark indicates that a shortwindow hybrid YIN and autocorrelation routine, guarded by simple robustness checks, attains sub-bpm accuracy on resting datasets and clinically acceptable accuracy on several pathological cohorts using only a single accelerometer axis. Importantly, AACFD is deliberately designed and evaluated for reliable window-averaged HR estimation on 10–60 s time scales rather than instantaneous beat-to-beat HR; deriving robust heart-rate-variability indices from SCG remains an important direction for future work.
Downloadable publication This is an electronic reprint of the original article. |  |
Funding information in the publication:
European Union’s Horizon Europe program under the Marie Skłodowska Curie grant agreement No 101119941.
