Wearable ECG(ElectroCardioGram) measurement systems have increasingly been developing for people who suffer from CVD(CardioVascular Disease) and have very active lifestyles. Especially, in the case of female CVD patients, several abnormal CVD symptoms are accompanied with CVDs. Therefore, monitoring women’s ECG signal is a significant diagnostic method to prevent from sudden heart attack. The E-bra ECG measurement system from our previous work provides more convenient option for women than Holter monitor system. The e-bra system was developed with a motion artifact removal algorithm by using an adaptive filter with LMS(least mean square) and a wandering noise baseline detection algorithm. In this paper, ICA(independent component analysis) algorithms are suggested to remove motion artifact factor for the e-bra system. Firstly, the ICA algorithms are developed with two kinds of statistical theories: Kurtosis, Endropy and evaluated by performing simulations with a ECG signal created by sgolayfilt function of MATLAB, a noise signal including 0.4Hz, 1.1Hz and 1.9Hz, and a weighed vector W estimated by kurtosis or entropy. A correlation value is shown as the degree of similarity between the created ECG signal and the estimated new ECG signal. In the real time E-Bra system, two pseudo signals are extracted by multiplying with a random weighted vector W, the measured ECG signal from E-bra system, and the noise component signal by noise extraction algorithm from our previous work. The suggested ICA algorithm basing on kurtosis or entropy is used to estimate the new ECG signal Y without noise component.