Fetal Monitoring: Multi-Channel Fetal ECG Denoising Based on Artificial Intelligence Approach

Abstract

Continuous electronic fetal monitoring using cardiotocography (CTG) represents the standard of evaluating the health status of the fetus and the risk of the pregnancy, in developed countries. However, the CTG has many limitations: high false positive rates, cannot be used for long term monitoring, poor sensitivity, it offers just the fetal heart rate and its variability etc. In this context, the fetal electrocardiogram (fECG) signal is used to obtain additional diagnostic information. On the other hand, the standard in clinical practice for obtaining the fECG is invasive, can pose a risk for both mother and fetus, can only be used during birth (very limited time window). An alternative is the abdominal fECG, that is recorded using a matrix of electrodes placed on the maternal abdomen. This approach is noninvasive and can be used for long term monitoring. The main drawback is the small signal to noise ratio for the abdominal fECG. Thus, the challenge is to isolate the fECG signal from other types of noise that are recorded by the abdominal electrodes: the maternal electrocardiogram (mECG), the electromyogram (EMG), the electrohysterogram (EHG), power line interference (PLI) etc. In this paper the author proposes an algorithm based on artificial neural network approach to extract the fECG signal waveform from abdominal recorded signals (ADS). The performance evaluation of proposed approach is realized on a database with simulated abdominal signals. A comparison is introduced, with other approaches described in literature for fECG denoising from abdominal signals.