Improving Photoplethysmographic Measurements Under Motion Artifacts Using Artificial Neural Network for Personal Healthcare

Photoplethysmographic (PPG) measurements are susceptible to motion artifacts (MA) due to movement of the peripheral body parts. In this paper, we present a new approach to identify the MA corrupted PPG beats and then rectify the beat morphology using artificial neural network (ANN). Initially, beat quality assessment was done to identify the clean PPG beats by a pretrained feedback ANN to generate a reference beat template for each person. The PPG data were decomposed using principal component analysis (PCA) and reconstructed using fixed energy retention. A weight coefficient was assigned for each PPG sample in such a way that when they are multiplied, the modified beat morphology matches the reference template. A particle swarm optimization-based technique was utilized to select the best weight vector coefficients to tune another feedback ANN, fed with a set of significant features generated by an auto-encoder from PCA reconstructed data. For real-time implementation, this pretrained ANN was operated in feed-forward mode to directly generate the weight vectors for any subsequent measurements of PPG. The method was validated with PPG data collected from 55 human subjects. An average root-mean-square error of 0.28 and signal-to-noise ratio improvement of 14.54 dB were obtained, with an average improvement of 36% and 47% measurement accuracies on crest time and systolic to diastolic peak height ratio, respectively. With the IEEE Signal Processing Cup 2015 challenge database, Pearson’s correlation coefficient between PPG-estimated and ECG-derived heart rates was 0.990. The proposed method can be useful for personal health monitoring applications.

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