Template-Based Statistical Modeling and Synthesis for Noise Analysis of Ballistocardiogram Signals: A Cycle-Averaged Approach.

Ballistocardiogram (BCG) can be recorded using inexpensive and non-invasive hardware to estimate physiological changes in the heart. In this paper, a methodology is developed to evaluate the impact of additive noise on the BCG signal. A statistical model is built that incorporates subject-specific BCG morphology. BCG signals segmented by electrocardiogram RR intervals (BCG heartbeats) are averaged to estimate a parent template and subtemplates leveraging the quasi-periodic nature of the heart. Noise statistics are obtained for subtemplates with respect to the parent template. Then, a synthesis algorithm with adjustable additive noise is devised to generate subtemplates based on the individual's parent template and statistics. For the example use of the synthesis algorithm, the average correlation coefficient between subtemplates and the parent template (subtemplate versus parent template approach) is tested as a signal quality index. A BCG heartbeat synthesis framework that incorporates an individual's BCG morphology and physiological variability was developed to quantify variations in the BCG signal against additive noise. The signal quality assessment of a person's BCG recording can be performed without requiring any a priori knowledge of the person's BCG morphology. A data-driven constraint on the required minimum number of heartbeats for a reliable template estimation was provided. The impact of additive noise on BCG morphology and estimated physiological parameters can be analyzed using the developed methodology without requiring prior statistics. This paper can facilitate the performance evaluation of BCG analysis algorithms against additive noise.