Key result
An automatic algorithm for analyzing long-lasting seismocardiogram recordings during sleep successfully identified cardiac fiducial points in over 36,000 beats with a precision of ≥99.2%.
A newly developed algorithm can automatically and accurately analyze long-lasting seismocardiogram recordings during sleep to explore cardiac mechanics, even in microgravity.
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SCG sleep analysis in microgravity is hypothesis-generating; leaves open whether it extends terrestrial cardiac monitoring or warrants clinical validation.
Seismocardiogram, SCG, is the measure of precordial vibrations produced by the beating heart, from which cardiac mechanics may be explored on a beat-to-beat basis. We recently collected a large amount of SCG data (>69 recording hours) from an astronaut to investigate cardiac mechanics during sleep aboard the International Space Station and on Earth. SCG sleep recordings are characterized by a prolonged duration and wide heart rate swings, thus a specific algorithm was developed for their analysis. In this article we describe the new algorithm and its performance. The algorithm is composed of three parts: 1) artifacts removal, 2) identification in each SCG waveform of four fiducial points associated with the opening and closure of the aortic and mitral valves, 3) beat-to-beat computation of indexes of cardiac mechanics from the SCG fiducial points. The algorithm was tested on two sleep recordings and yielded the identification of the fiducial points in more than 36,000 beats with a precision, quantified by the Positive Predictive Value, ≥99.2%. These positive findings provide the first evidence that cardiac mechanics may be explored by the automatic analysis of SCG long-lasting recordings, taken out of the laboratory setting, and in presence of significant heart rate modulations.
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Rienzo et al. (2017) studied Healthy (sleep monitoring) (n=3). Automatic algorithm for seismocardiogram analysis was evaluated on Precision of fiducial point identification (Positive Predictive Value). An automatic algorithm for analyzing long-lasting seismocardiogram recordings during sleep successfully identified cardiac fiducial points in over 36,000 beats with a precision of ≥99.2%.
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