Abstract
The Heart Rate Variability (HRV) signal computation relies on fiducial points typically obtained from the electrocardiogram (ECG) or the photoplethysmogram (PPG). Generally, these fiducial points correspond to the peaks of the ECG or PPG. Consequently, the HRV quality depends on the fiducial points detection accuracy. In a previous work, this subject has been addressed using Long Short-Term Memory (LSTM) Deep Learning algorithms for PPG segmentation, from which peak detection can be achieved. In the herein presented work, a Simulink® implementation of the LSTM algorithm is obtained for real-time PPG peak detection. HRV and outlier removal blocks are also implemented. The obtained code can be used to be embedded in hardware systems for real-time PPG acquisition and HRV visualization. A Root Mean Square Error (RMSE) mean of 0.0439 ± 0.0175 s was obtained, and no significant differences (p-value < 0.05) were found between the ground truth and the real-time implementation.
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Acknowledgments
This work was funded and supported by the Fundação para a Ciência e Tecnologia (FCT, Portugal) and NMT, S.A in the scope of the PhD grant PD/BDE/150312/2019.
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Esgalhado, F., Batista, A., Vassilenko, V., Ortigueira, M. (2022). Real-Time PPG-Based HRV Implementation Using Deep Learning and Simulink. In: Camarinha-Matos, L.M. (eds) Technological Innovation for Digitalization and Virtualization. DoCEIS 2022. IFIP Advances in Information and Communication Technology, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-031-07520-9_10
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DOI: https://doi.org/10.1007/978-3-031-07520-9_10
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