A Novel Strategy for Signal Reconstruction of Noisy Segments in Photoplethysmographic Recordings

Motion artifacts (MAs) is a major issue in photo-plethysmography (PPG), complicating health monitoring. While many algorithms focus on detecting MAs, little is known on how MA segments could be further utilized. This work proposes an algorithm for electrocardiograph-independent (ECG) PPGs re-construction. MAs on PPGs are detected by spectral analysis and HR on the clean segments is calculated. For MAs shorter than 20 seconds, reconstruction is performed by the characteristic pulse of the clean segments surrounding MAs, using the heart rate (HR) of the closest-to-MAs pulses and the HR and amplitude variability. Thirty eight-minute PPG and ECG recordings at 125 Hz sampling frequency of the BIDMC database were employed for validation, after upsampling to 250 Hz. HR was calculated in reconstructed PPGs and the corresponding ECGs and compared with Pearson correlation (PC). Mean absolute error (MAE) on HR estimation was also calculated. Pulse transit time (PTT) was computed for the reconstructed segment, as the difference in time between the ECGs R-peak and the peak of PPG pulses. PTT was also calculated for the clean segments before $(\text{PTT}_{b})$ and after $(\text{PTT}_{a})$ the reconstructed signal and compared via PC. Median HR in PPGs: 87.5 bpm, in ECGs: 88.01 bpm (MAE: 1.59 bpm). ECG-PPG HR correlation: 99.31% $(p$ < 0.0001). Median PTT: 142.1 ms, $\text{PTT}_{b}$: 95.58 ms and $\text{PTT}_{a}$: 99.08 ms. $\text{PTT-PTT} _{b}$ correlation: 81.56% (p < 0.0001). $\text{PTT-PTT}_{a}$ correlation: 78.56% $(p$ < 0.0001). $\text{PTT}_{b}{\text{-PTT}_{a}}$ correlation: 92.56% $(p$ < 0.0001). The method shows outstanding performance for HR estimation during noise and can be used for remote HR monitoring. The non-perfect correlation of the two clean segments stresses the difficulty of a high performance on PTT calculation, implying that the method could also be used for PTT estimation.