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.