Automatic Recognition of Complete Atrioventricular Activity in Fetal Pulsed-Wave Doppler Signals.

Echocardiography is the gold standard for antenatal cardiological assessment. However, the adoption of this technique is challenging, since it is intrinsically operator-dependent and because of the different confounding factors related to the fetal heart size, the fetal movements and the ultrasound artifacts. Among the different options, fetal echocardiography is widely used, concurring to an early diagnosis of several cardiac pathologies. In this work, a neural network-based algorithm targeted at the identification of the most important features of Doppler fetal echocardiography videos is presented and evaluated on real signals. Compared to other approaches, the proposed algorithm works on a couple of ID signals, representing the pulse-wave Doppler envelope extracted from the video, thus preserving a Iightweight approach. For the validation, a small dataset was created, including recordings from five voluntary pregnant women 21 to 27 gestational week), for a total of 20 records, 10 seconds each. The dataset was annotated by an expert cardiologist in order to identify the epochs of the signal where a complete readable cardiac cycle could be identified. The performance of the method was evaluated through a 5-fold cross-validation. An average accuracy up to 88% was obtained, confirming the validity of the proposed approach and paving the way to future improvements of the technique.