Unobtrusive monitoring of cardiac activity during sleep is a key enabler for home-based screening of sleep disorders. Piezoelectric strips on a mattress can capture ballistocardiographic signals without any body-worn device, but their reliability across sleeping positions and mattress stiffness remains insufficiently characterized.
We present a signal processing pipeline for detecting beat-to-beat cardiac intervals from 16 equidistant piezoelectric strips spanning head to feet, validated against simultaneous ECG. The dataset comprises recordings from 7 healthy participants, each lying in four standardized positions for 3 minutes per position on 6 mattresses of varying stiffness, totalling 139 recordings. Cardiac detection uses template matching with dominant shape extraction; signals from all 16 strips are then fused into a single ensemble estimate weighted by per-strip signal quality. Detected intervals are matched to ECG intervals within a 30 ms tolerance. Performance is assessed by (i) the proportion of matched intervals per recording, (ii) the residual duration difference of matched beats as a precision metric, and (iii) the effect of mattress and body position on matching performance, evaluated against a pre-specified 5 percentage-point (pp) equivalence margin.
Across 139 recordings, ensemble fusion achieves a median interval-matching rate of 97.6% (IQR 92.6%–99.5%). The residual duration difference between matched piezoelectric and ECG intervals has a standard deviation of 7.29 ms, well within clinical tolerance for downstream HRV analysis. Mattress stiffness and body position have no practically meaningful effect on matching performance: the maximum pairwise difference between any two mattress medians is 4.58 pp, and 3.42 pp across positions.
These results demonstrate that piezoelectric mattress sensor strips, combined with template-matching detection and quality-weighted ensemble fusion, deliver cardiac interval estimates that are simultaneously accurate, precise, and robust. This supports their deployment as a practical tool for long-term, unobtrusive cardiac and HRV monitoring.