A novel approach framework based on statistics for reconstruction and heartrate estimation from PPG with heavy motion artifacts
One of the most important applications of photoplethysmography (PPG) signal is heartrate (HR) estimation. For its applications in wearable devices, motion artifact (MA) may be the most serious challenge for randomness both in format and temporal distribution. This paper proposes an advanced time-frequency analysis framework based on empirical mode decomposition (EMD) to select specific time slices for signal reconstruction. This framework operates with a type of pre-processing called variance characterization series (VCS), EMD, singular value decomposition (SVD), and a precise and adaptive 2-D filtration reported first. This filtration is based on Harr wavelet transform (HWT) and 3rd order cumulant analysis, to make it have resolution in both the time domain and different components. The simulation results show that the proposed method gains 1.07 in absolute average error (AAE) and 1.87 in standard deviation (SD); AAEs’ 1st and 3rd quartiles are 0.12 and 1.41, respectively. This framework is tested by the PhysioBank MIMIC II waveform database.
Keywordsphotoplethysmography (PPG) motion artifact empirical mode decomposition (EMD) singular value decomposition discrete wavelet transform higher-order statistics
This work was supported by National Natural Science Foundation of China (Grant Nos. 61634006, 61372060, 61335010, 61474107, 81300803), National Key Technologies R&D Program (Grant Nos. 2016YFB0401303, 2016YFB0402405), Basic Research Project of Shanghai Science and Technology Commission (Grant No. 16JC1400101), and Key Research Program of Frontier Science, Chinese Academy of Sciences (Grant No. QYZDY-SSW-JSC004).
- 3.Lee J, Jung W. Design the filter to reject motion artifact of pulse oximetry. Elsevier Comput Sci, 2003, 26: 241–249Google Scholar
- 8.Sung P, Syed Z, Guttag J. Quantifying morphology changes in time series data with skew. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, Taipei, 2009. 477–480Google Scholar
- 10.Motin M A, Karmakar C, Palaniswami M. Ensemble empirical mode decomposition with principal component analysis: a novel approach for extracting respiratory rate and heart rate from photoplethysmographic signal. IEEE J Biomed Health Inf, 2017. doi: 10.1109/JBHI.2017.2679108Google Scholar
- 13.Li P, Liu M, Zhang X, et al. A low-complexity ECG processing algorithm based on the Haar wavelet transform for portable health-care devices. Sci China Inf Sci, 2014, 57: 122303Google Scholar