Comparative Study of Heart Rate Extraction Methods for a Novel Intelligent Mattress

Unobtrusive monitoring of the heart rate (HR) is essential for improving medical intervention. A new generation of mattress-based fiber optic sensor (FOS) is emerging for HR monitoring. The use of this FOS mattress for medical diagnosis requires appropriate advanced signal processing algorithms. In our study, we aim to weigh the performances of a novel and cheaper microbend FOS mattress by applying ballistocardiogram and HR extraction algorithms. Therefore, our study targets comparing four types of HR extraction algorithms on the FOS mattress, namely MODWT, CEEMDAN, cepstrum and clustering. The goal is to select, based on their accuracy and computational speed, the most suitable one for online or offline application purposes. Results of applying these four chosen algorithms on the FOS mattress show that the cepstrum is the most accurate algorithm with a mean absolute error (MAE) of 4.62 ± 1.68 BPM. However, the cepstrum is more appropriate for offline monitoring with a runtime of 662.9 ms for a 10-second signal segment. The results also show that the Maximal Overlap Direct Wavelet Transform (MODWT) is more efficient with a runtime of 4.1 milliseconds for online purposes, but with a slightly bigger MAE (6.87 ± 1.94 BPM). Both methods proved to be as efficient on the new mattress technology as past intelligent mattresses.