Why the study?
Several ballistocardiogram processing methods were evaluated to determine which method achieves the best performance for signals captured with a polymer optical fiber specklegram sensor.
Hilbert, PSD, and Savitzky-Golay filters provide the best balance of accuracy and computational efficiency for real-time processing of fiber specklegram sensor-based ballistocardiograms.
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POF specklegram sensor acquires BCG in small cohort; leaves open clinical validation and adoption.
The ballistocardiogram (BCG) is a graphic representation of the movements of the body associated with cardiac activity. In this article, a 10-min BCG has been captured for ten different volunteers with a polymer optical fiber (POF) specklegram sensor. This transducer, which is composed of a charge-coupled device (CCD) camera, a laser emitting diode, and two meters of POF, allows capturing the BCG by analyzing how the induced speckle pattern changes over time. These changes are related to cardiac activity. Several processing methods have been compared to determine which method achieves the best performance: complex cepstrum, power of spectral density (PSD), Pam–Tompkins algorithm, wavelet, autocorrelation, Savitzky–Golay filter, mean absolute deviation, and Hilbert transform. Accuracy and resource consumption have been characterized and compared for these methods. Hilbert, PSD, and Savitzky–Golay exhibit both small errors and computational costs. This article describes a baseline for the main frequency determination of POF-based BCG signals in real-time.
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Reyes-González et al. (2022) studied this question.
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