MoDTRAP: Improved heart rate tracking and preprocessing of motion-corrupted photoplethysmographic data for personalized healthcare

Abstract

ObjectiveA new joint framework for heart rate (HR) tracking and preprocessing of motion artifact (MA) corrupted photoplethysmogram (PPG) data is presented. MethodsTwo popular signal decomposition techniques, viz., variational mode decomposition (VMD) to track HR, and ensemble empirical mode decomposition (EEMD) in conjunction with an artificial neural network (NN) model was utilized for preprocessing. The HR frequency was estimated from the signal strengths of VMD decomposed mode functions and tracked with an indigenous algorithm utilizing the fact that the PPG signal normally comprises base HR frequency along with its harmonics. For MA reduction, The HR synchronous signal component was extracted from the appropriate mode functions of VMD decomposition and reconstructed to minimize MA using a template matching technique utilizing an autoencoder and pre-trained multilayer feedforward neural network model. A combination of EEMD followed by a LSTM binary classifier was used to generate and update the reference PPG template beat for the above purpose. ResultsWith 2015 IEEE signal processing cup challenge database, an average absolute error and percent absolute error of 1.02 and 0.86 respectively compared to the ground truth HR were obtained for all 22 subjects. For MA reduction, we used wrist PPG data collected from 30 human subjects (normal and cardiovascular patients), achieving an average root mean squared error (RMSE) of 0.31 and SNR improvement of 21.23 dB. ConclusionProposed MoDTRAP technique provides noticeable improvements, separately, over existing methods on HR tracking and MA reduction. SignificanceThe proposed technique can be utilized for ambulatory healthcare monitoring.