PPG signal motion artifacts correction algorithm based on feature estimation
Authors:
- Bin SunBSBin Sun
- Chengchao WangCWChengchao Wang
- Xiaohui ChenXCXiaohui Chen
- Yongfang ZhangYZYongfang Zhang
- Hanyu ShaoHSHanyu Shao
Abstract
No abstract availableKeywords
This publication has 22 references indexed in Scilit:
- Non-invasive prediction of hemoglobin level using machine learning techniques with the PPG signal's characteristics featuresApplied Soft Computing, 2015
- Body Sensor Network for Mobile Health Monitoring, a Diagnosis and Anticipating SystemIEEE Sensors Journal, 2015
- VLSI Wavelet Based Denoising of PPG SignalProcedia Computer Science, 2015
- Noninvasive cuffless blood pressure estimation using pulse transit time and Hilbert–Huang transformComputers and Electrical Engineering, 2013
- A Wireless Reflectance Pulse Oximeter With Digital Baseline Control for Unfiltered PhotoplethysmogramsIEEE Transactions on Biomedical Circuits and Systems, 2011
- Analysis of human PPG, ECG and EEG signals by eigenvector methodsDigital Signal Processing, 2010
- Independent component analysis of pulse oximetry signals2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2007
- Motion Artifact Reduction in Photoplethysmography Using Independent Component AnalysisIEEE Transactions on Biomedical Engineering, 2006
- Design of filter to reject motion artifact of pulse oximetryComputer Standards & Interfaces, 2004
- Adaptive reduction of motion artifact from photoplethysmographic recordings using a variable step-size LMS filter2003