Improving Photoplethysmographic Measurements under Motion Artifacts using Artificial Neural Network for Personal Healthcare

TL;DR

This paper introduces a neural network-based method to improve PPG signal quality under motion artifacts, enhancing measurement accuracy for personal health monitoring.

Contribution

A novel neural network approach combined with PCA and PSO techniques for real-time correction of motion artifacts in PPG signals.

Findings

Achieved an average RMSE of 0.28 in PPG signal reconstruction.

Improved SNR by 14.54 dB on average.

Enhanced measurement accuracy by 36-47% for key PPG features.

Abstract

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 pre-trained feedback ANN to generate a reference beat template for each person. The PPG data was decomposed using principal component analysis (PCA) and reconstructed using fixed energy retention. A weight coefficient was assigned for each PPG samples in such a way that when they are multiplied , the modified beat morphology matches the reference template. A particle swarm optimization (PSO) based technique was utilized to select the best weight 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 pre-trained 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 RMSE of 0.28 and SNR improvement of 14.54 dB was obtained, with an average improvement of 36% and 47% measurement accuracy on crest time and systolic to diastolic peak height ratio respectively. With IEEE Signal Processing Cup 2015 Challenge database, Pearson's correlation coefficient between PPG estimated and ECG derived heart rate was 0.990. The proposed method can be useful for personal health monitoring applications.