TY - GEN
T1 - PPGANet
T2 - 2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023
AU - Sawangjai, Phattarapong
AU - Wilaiprasitporn, Theerawit
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - With the emergence of smartwatches and fitness trackers, photoplethysmography (PPG) signal has become more widespread in daily life. However, the signal's usefulness is still limited by motion artifacts. This study explores the potential of using generative adversarial networks (GANs) to eliminate motion artifacts from the PPG signal without relying on additional sensor data from accelerometers or gyroscopes. Our evaluation methods include PPG pulse detection, heart rate estimation, and waveform morphology. While the proposed method lags slightly behind one state-of-the-art technique that utilized additional sensors in performance, it requires less input signal, making it more beneficial for portable or low-cost devices. As shown in the results, this study can serve as a foundation for future single channel-based algorithms.
AB - With the emergence of smartwatches and fitness trackers, photoplethysmography (PPG) signal has become more widespread in daily life. However, the signal's usefulness is still limited by motion artifacts. This study explores the potential of using generative adversarial networks (GANs) to eliminate motion artifacts from the PPG signal without relying on additional sensor data from accelerometers or gyroscopes. Our evaluation methods include PPG pulse detection, heart rate estimation, and waveform morphology. While the proposed method lags slightly behind one state-of-the-art technique that utilized additional sensors in performance, it requires less input signal, making it more beneficial for portable or low-cost devices. As shown in the results, this study can serve as a foundation for future single channel-based algorithms.
KW - PPG signal
KW - generative adversarial networks
KW - motion artifact removal
KW - photoplethysmography
UR - https://www.scopus.com/pages/publications/85184982828
U2 - 10.1109/BioCAS58349.2023.10388620
DO - 10.1109/BioCAS58349.2023.10388620
M3 - Conference contribution
AN - SCOPUS:85184982828
T3 - BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings
BT - BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 October 2023 through 21 October 2023
ER -