Impact of pulsed-wave-Doppler velocity-envelope tracing techniques on classification of complete fetal cardiac cycles.

Fetal echocardiography is an operator-dependent examination technique requiring a high level of expertise. Pulsed-wave Doppler (PWD) is often used as a reference for the mechanical activity of the heart, from which several quantitative parameters can be extracted. These aspects suggest the development of software tools that can reliably identify complete and clinically meaningful fetal cardiac cycles that can enable their automatic measurement. Several scientific works have addressed the tracing of the PWD velocity envelope. In this work, we assess the different steps involved in the signal processing chains that enable PWD envelope tracing. We apply a supervised classifier trained on envelopes traced by different signal processing chains for distinguishing complete and measurable PWD heartbeats from incomplete or malformed ones, which makes it possible to determine the impact of each of the different processing steps on the detection accuracy. In this study, we collected 43 images and labeled 174,319 PWD segments from 25 pregnant women volunteers. By considering seven envelope tracing techniques and the 23 different processing steps involved in their implementation, the results of our study reveal that, compared to the steps investigated in most other works, those that achieve binarisation and envelope extraction are significantly more important (p < 0.05). The best approaches among those studied enabled greater than 98% accuracy on our large manually annotated dataset.

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 21st to 27th 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.