Nilanjan Ray

Professor
Department of Computing Science
University of Alberta, Canada


Continuous BP Monitoring


Calf measurement of blood pressure is intermittent and its invasive nature limits its use during biking or exercise. In a recent study we used recurrent neural networks (RNN) to continuously predict BP from ECG, PPG, and signals from accelerometer and gyroscope. These signals are continuously collected by a wearable device called BioRadio. Our study is one of the first that trained a RNN for resting conditions, but tested it on signals collected during walking or biking to result in accurate BP prediction.

Related Publications
  • S. Ghosh, A. Banerjee, N. Ray, P. Wood, P. Boulanger, R. Padwal, “Using Accelerometric and gyroscopic data to improve blood pressue prediction from pulse transit time using recurrent neural network,” ICASSP 2018.
  • S. Ghosh, A. Banerjee, N. Ray, P.W. Wood, P. Boulanger, R. Padwal, “Continuous blood pressure prediction from pulse transit time using ECG and PPG signals,” 2016 IEEE Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT).
  • S. Ghosh, A. Banerjee, N. Ray, P. W. Wood, P. Boulanger, R. Padwal, “Non-Invasive and Continuous Blood Pressure Prediction from Pulse Transit Time Using ECG and PPG Signals,” Poster presented at: Canadian Hypertension Congress Hypertension Canada; October 2016; Montreal, Quebec.
  • S. Ghosh, N. Ray, P. W. Wood, P. Boulanger, R. Padwal, “Pulse Transit Time Computation Using Signal Sparsity for Continuous Blood Pressure Prediction,” Poster presented at: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; August 2016; Orlando, Florida.