TY - GEN
T1 - Heart Rate Estimation by PCA with LSTM from Video-based Plethysmography Under Periodic Noise
AU - Traivinidsreesuk, Chetsadaporn
AU - Yodrabum, Nutcha
AU - Chaikangwan, Irin
AU - Titijaroonroj, Taravichet
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - A remote photoplethysmography (rPPG) analysis can extract vital signs from the source video, including heart rate estimation. One of the problems of heart rate estimation is periodic noise embedded in the source video. It is difficult for an rPPG analysis to discriminate between vital signal information and noise, increasing prediction error. To alleviate this problem, this paper used principal component analysis (PCA) to extract rPPG signals from the input video before forwarding the signal to Long Short Term Memory (LSTM) to estimate heart rate. The experimental results show that, among discrete Fourier Transform method, neural networks, and neural network with LSTM, the proposed method accomplished a much lower MAEP at 15.05, 13.90, and 17.90 in the cases of overall, with no periodic noise, and with periodic noise, respectively.
AB - A remote photoplethysmography (rPPG) analysis can extract vital signs from the source video, including heart rate estimation. One of the problems of heart rate estimation is periodic noise embedded in the source video. It is difficult for an rPPG analysis to discriminate between vital signal information and noise, increasing prediction error. To alleviate this problem, this paper used principal component analysis (PCA) to extract rPPG signals from the input video before forwarding the signal to Long Short Term Memory (LSTM) to estimate heart rate. The experimental results show that, among discrete Fourier Transform method, neural networks, and neural network with LSTM, the proposed method accomplished a much lower MAEP at 15.05, 13.90, and 17.90 in the cases of overall, with no periodic noise, and with periodic noise, respectively.
KW - Heart Rate Estimation
KW - LSTM
KW - PCA
KW - Remote Photoplethys-mography
KW - rPPG signal
UR - https://www.scopus.com/pages/publications/85149659708
U2 - 10.1109/ICSEC56337.2022.10049315
DO - 10.1109/ICSEC56337.2022.10049315
M3 - Conference contribution
AN - SCOPUS:85149659708
T3 - ICSEC 2022 - International Computer Science and Engineering Conference 2022
SP - 20
EP - 25
BT - ICSEC 2022 - International Computer Science and Engineering Conference 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th International Computer Science and Engineering Conference, ICSEC 2022
Y2 - 21 December 2022 through 23 December 2022
ER -