Respiration Monitoring through Thoraco-Abdominal Video with an LSTM

Abstract

In this manuscript, we demonstrate the estimation of the respiratory signal from a thoraco-abdominal video of a person using an LSTM based learning model. The video is captured with a regular consumer grade camera and the respiratory signal is recorded using an impedance pneumograph simultaneously. The optical flow capturing the motion of the chest wall during an inhalation and exhalation is extracted at each video frame and fed as features to the LSTM model. We then train the LSTM model to estimate the respiratory signal. We fix the design parameters of the LSTM model based on cross-validation. The comparison between the predicted and the ground-truth pneumograph signal shows that the trained LSTM model predicts the respiratory signal quite accurately achieving a strong amplitude correlation of 0.74. Moreover, we estimate the respiration rates from the predicted respiratory signal. The estimated respiration rates have less than ±3 BPM error for more than 95% cases. Also, we achieve a correlation of 0.9 between the ground-truth respiration rates and the estimated respiration rates.