A novel approach framework based on statistics for reconstruction and heartrate estimation from PPG with heavy motion artifacts  

作　　者：Bo PANG Ming LIU Xu ZHANG Peng LI Hongda CHEN 

机构地区：[1]State Key Laboratory on Integrated Optoelectronics,Institute of Semiconductors,Chinese Academy of Sciences,Beijing 100083,China [2]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Science China(Information Sciences)》2018年第2期178-189,共12页中国科学（信息科学）（英文版）

基　　金：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);Key Research Program of Frontier Science,Chinese Academy of Sciences(Grant No.QYZDY-SSW-JSC004)

摘　　要：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 3 rd 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＇ 1 st and 3 rd quartiles are 0.12 and 1.41, respectively. This framework is tested by the Physio Bank MIMIC II waveform database.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 3 rd 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＇ 1 st and 3 rd quartiles are 0.12 and 1.41, respectively. This framework is tested by the Physio Bank MIMIC II waveform database.

关 键 词：photoplethysmography（PPG） motion artifact empirical mode decomposition（EMD） singular value decomposition discrete wavelet transform higher-order statistics 

分 类 号：R318.6[医药卫生—生物医学工程] TN911.7[医药卫生—基础医学]