Fetal ECG Extraction and QRS Detection using Independent Component Analysis

2020 ◽  
Author(s):  
Sarfaraj Mirza ◽  
Kalyani Bhole ◽  
Prateek Singh
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Qrs Detection ◽  
Fetal Ecg

Fetal Electrocardiogram Extraction Based on Modified Robust Independent Component Analysis

2013 ◽  
Vol 749 ◽  
pp. 250-253 ◽  
Author(s):  
Wen Po Yao ◽  
Jun Chang Zhao ◽  
Zheng Zhong Zheng ◽  
Tie Bing Liu ◽  
Hong Xing Liu ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Wavelet Decomposition ◽  
Minimum Mean Square Error ◽  
Component Analysis ◽  
Independent Component ◽  
Mean Square ◽  
Ecg Monitoring ◽  
Fetal Electrocardiogram ◽  
Fetal Ecg ◽  
Computing Speed

Fetal electrocardiogram (FECG) separation gets widely attention due to its clinical significance. In the paper, we proposed an improved robust independent component analysis for fetal ECG separation. Firstly, wavelet decomposition was applied to fetal ECG to get the relevant parameters. Then, the RobustICA was used to separate the mixed signals. Compared to robust independent component analysis, computing speed of the improved algorithm increased by an average of 15 percent while minimum mean square error fluctuations 0.0008, which indicated that this algorithm could be effectively used in clinical fetal ECG monitoring.

Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

2020 ◽  
Vol 2020 (14) ◽  
pp. 357-1-357-6
Author(s):  
Luisa F. Polanía ◽  
Raja Bala ◽  
Ankur Purwar ◽  
Paul Matts ◽  
Martin Maltz
Keyword(s):  
Independent Component Analysis ◽  
Spectral Reflectance ◽  
Source Separation ◽  
Principal Component ◽  
Component Analysis ◽  
Previous Method ◽  
Independent Component ◽  
Light Transport ◽  
Camera Model ◽  
Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

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