Adaptive heartbeat modeling for ballistocardiograms yielded a mean beat-to-beat interval error of 13 ms and detected an average of 54% of intervals during overnight recordings.
Does an adaptive heartbeat modeling method accurately measure beat-to-beat heart rate from ballistocardiograms?
An adaptive heartbeat modeling method for ballistocardiograms can measure beat-to-beat intervals with a mean error of 13 ms, though only detecting 54% of intervals on average.
We present a method for measuring beat-to-beat heart rate from ballistocardiograms acquired with force sensors. First, a model for the heartbeat shape is adaptively inferred from the signal using hierarchical clustering. Then, beat-to-beat intervals are detected by finding positions where the heartbeat shape best fits the signal. The method was validated with overnight recordings from 46 subjects in varying setups (sleep clinic, home, single bed, double bed, two sensor types). The mean beat-to-beat interval error was 13 ms and on an average 54% of the beat-to-beat intervals were detected. The method is part of a home-use e-health system for an unobtrusive sleep measurement.
Paalasmaa et al. (2014) studied this question. Adaptive heartbeat modeling was evaluated on mean beat-to-beat interval error and detection rate. Adaptive heartbeat modeling for ballistocardiograms yielded a mean beat-to-beat interval error of 13 ms and detected an average of 54% of intervals during overnight recordings.
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