Nakamura, M2 student in our lab, gave a poster presentation at BioCAS2019 (Biomedical Circuits and Systems Conference) held at Nara Kasugano International Forum on October 17-19, 2019.

Nakamura’s presentation is related to a method of heart rate estimation from photoelectric volumetric pulse wave (PPG). Although PPG is suitable for daily measurement due to its simplicity, it is susceptible to noise due to motion artifacts. In this study, we proposed a noise-resistant heart rate estimation using feature extraction by convolutional neural networks. As a result of evaluation, we confirmed that the proposed method can estimate the heart rate with higher accuracy than FFT, which is a common frequency analysis algorithm for this purpose.

  • Masaki Nakamura and Takashi Sato, “Heart rate estimation during exercise from photoplethysmographic signals using convolutional neural network,” in Proc. Biomedical Circuits and Systems Conference (BioCAS), October 2019.
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