SEPARATION AND CLASSIFICATION OF FETAL ECG SIGNAL BY ENHANCED BLIND SOURCE SEPARATION TECHNIQUE AND NEURAL NETWORK

Abstract

The separation of Fetal ElectroCardioGram (FECG) from mother's Abdominal ECG (AECG) signal is complicated and very important in medical diagnosis during pregnancy. In this study, the separation and classification of FECG signal from AECG signals is presented by a novel method. It uses MULTI-COMBI based Blind Source Separation (BSS) technique to separate the FECG signals. The separated FECG signals have different features, which are extracted using the morphological feature extraction method. These extracted features are used for classification by using Feed Forward Neural Network (FFNN). This classifier classifies the FECG into five different classes. The entire work is implemented in MATLAB. Results show that FFNN gives the classification accuracy of 77.1%, sensitivity of 75.3%, and specificity of 76.7%.

Keywords

• blind source separation, fetal ecg, multi-combi, feed forward neural network.