Blind Source Separation (BSS) of Mixed Maternal and Fetal Electrocardiogram (ECG) Signal: A comparative Study

Abstract Electrocardiogram (ECG) test is very important for fetus condition inspection so as to avoid stillbirth and neonatal death during pregnancy. It is well known and widely used as a medical tool as it is convenient and non-invasive. Nevertheless, analysing fetal ECG (FECG) signal by using naked eye is tedious as the observed signal is a mixed signal which consists of weak FECG, maternal ECG (MECG) and also other signals including mother’s respiratory noise. Hence, in this paper, Blind Source Separation (BSS) is used to extract the estimated desired signal i.e. FECG from the mixed signal. BSS is a well-known separation method that is able to extract desired signal without knowing any information of the source signal. The aim of this study is to elucidate the performance of BSS algorithms i.e. Fast Fixed-Point for Independent Component Analysis (FastICA), Joint Approximate Diagonalization of Eigenmatrix (JADE) and Principal Component Analysis (PCA) for FECG extraction. We integrate R-peak detection algorithm as a post-separation process to the BSS system in order to distinguish the estimated FECG and MECG for easier analysis process. Estimated signals are evaluated based on waveform characteristics observation and Signal-to Interference Ratio (SIR) parameter. We can conclude that JADE performance performs better in term of accuracy while FastICA is good in term of the computational time. However, FastICA manages to get comparable result as JADE after many fine-tuning steps and it is more flexible compared to JADE as it is not sensitive to low quality input signal.