Robust fetal ECG extraction and detection from abdominal leads

fetal ECG derived from abdominal leads provides an alternative to standard means of fetal monitoring. Furthermore, it permits long-term and ambulant recordings, which expands the range diagnostic possibilities for evaluating the fetal health state. However, due to the temporal and spectral overlap of maternal and fetal signals, the usage of abdominal leads imposes the need for elaborated signal processing routines. In this work a modular combination of processing techniques is presented. Its core consists of two maternal ECG estimation techniques, namely the extended Kalman smoother (EKS) and template adaption (TA) in combination with an innovative detection algorithm. Our detection method employs principles of evolutionary computing to detect fetal peaks by considering the periodicity and morphological characteristics of the fetal signal. In a postprocessing phase, single channel detections are combined by means of kernel density estimation and heart rate correction. The described methodology was presented during the Computing in Cardiology Challenge 2013. The entry was the winner of the closed-source events with average scores for events 4/5 with 15.1/3.32 (TA) and 69.5/4.58 (EKS) on training set-A and 20.4/4.57 (TA) and 219/7.69 (EKS) on test set-B, respectively. Using our own clinical data (24 subjects each 20 min recordings) and statistical measures beyond the Challenge's scoring system, we further validated the proposed method. For our clinical data we obtained an average detection rate of 82.8% (TA) and 83.4% (EKS). The achieved results show that the proposed methods are able produce reliable fetal heart rate estimates from a restricted number of abdominal leads.