Fetal Electrocardiogram Extraction Using Let Techniques

Abstract

Fetal Electrocardiogram Extraction (FECG) identifies the congenital heart problems at the earlier stage. The major problem in the non invasive procedure is the extraction of FECG from Maternal ECG (MECG) and many interferences. The proposed methods (i) Combination of Adaptive Neuro Fuzzy Inference (ANFIS) and Fractional spline wavelet (ii) Combination of Fractional spline wavelet and ANFIS (iii) Combination of ANFIS and SURE-LET and (iv) Combination of SURE-LET and ANFIS remove the unwanted noises present in the FECG more effectively. This new approach extracts FECG by removing the noisy Abdominal ECG (AECG) and subsequently cancels the MECG. The pure thoracic ECG (TECG) or maternal ECG was used to remove noisy MECG present in the signal from abdomen signal and thereby the required noiseless FECG is extracted by means of the new approach. The excellence of the LET techniques are evaluated using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The result of combination of ANFIS and SURELET gives the best result and the closest match to the simulated FECG with high PSNR and low MSE among all the proposed methods.