Automatic detection of complete and measurable cardiac cycles in antenatal pulsed-wave Doppler signals

Sulas, Eleonora; Urru, Monica; Tumbarello, Roberto; Raffo, Luigi; Pani, Danilo

doi:10.1016/j.cmpb.2020.105336

Cited by 12 publications

(8 citation statements)

References 19 publications

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“…As the dataset was not conceived for foetal heart rate (fHR) variability studies (typically requiring at least five minutes of continuous recording), some of the records are relatively short and not recommended for signal processing algorithms requiring a long training period. Some of the cases for this database were previously used for studies on PWD [27][28][29] and fECG [30][31][32][33] .…”

Section: Background and Summarymentioning

confidence: 99%

A non-invasive multimodal foetal ECG–Doppler dataset for antenatal cardiology research

et al. 2021

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Non-invasive foetal electrocardiography (fECG) continues to be an open topic for research. The development of standard algorithms for the extraction of the fECG from the maternal electrophysiological interference is limited by the lack of publicly available reference datasets that could be used to benchmark different algorithms while providing a ground truth for foetal heart activity when an invasive scalp lead is unavailable. In this work, we present the Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research (NInFEA), the first open-access multimodal early-pregnancy dataset in the field that features simultaneous non-invasive electrophysiological recordings and foetal pulsed-wave Doppler (PWD). The dataset is mainly conceived for researchers working on fECG signal processing algorithms. The dataset includes 60 entries from 39 pregnant women, between the 21st and 27th week of gestation. Each dataset entry comprises 27 electrophysiological channels (2048 Hz, 22 bits), a maternal respiration signal, synchronised foetal trans-abdominal PWD and clinical annotations provided by expert clinicians during signal acquisition. MATLAB snippets for data processing are also provided.

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Section: Background and Summarymentioning

confidence: 99%

A non-invasive multimodal foetal ECG–Doppler dataset for antenatal cardiology research

et al. 2021

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“…Despite the possibility of obtaining such a large amount of information from this signal, the fetal PWD procedure is challenging for cardiologists to perform and, like any other antenatal ultrasound examination, it is strongly operator-dependent and requires a high level of expertise. To support ultrasound operators with limited skills to perform fetal echocardiography, beyond tele-mentoring sessions [14], or for the automatic analysis of long traces, it is important to develop automatic tools for the detection of complete and clinically meaningful fetal heartbeats in the PWD trace [15]. Once identified, these heartbeats can be analyzed for extraction of the aforementioned clinical parameters and quantification of the cardiac function.…”

Section: Plos Onementioning

confidence: 99%

“…Recent works have addressed this important issue using ad-hoc algorithms and machine learning [15][16][17]. However, to the best of our knowledge, there has been no systematic analysis of the steps comprising the signal processing chain for tracing the PWD velocity envelope.…”

Section: Plos Onementioning

confidence: 99%

“…The automatic and unsupervised detection of meaningful fetal cardiac cycles in Doppler ultrasound videos was originally presented in [17], and subsequent development of the original technique is presented in [15,16]. To the best of our knowledge, no other works have focused on the automatic detection of complete and clinically measurable fetal cardiac cycles in a Doppler ultrasound trace.…”

Section: Related Workmentioning

confidence: 99%

“…The data were collected from the fetal echocardiographic examination of 25 low-risk pregnant volunteers at gestational weeks ranging from the 21 st to the 27 th , from which we obtained a total of 43 PWD traces. An expert pediatric cardiologist labeled all the complete and measurable fetal cardiac cycles using a custom MATLAB graphical interface, as described in [15]. This MATLAB application enables the cardiologist to scroll through the traces and label fetal heartbeats frame-by-frame.…”

Section: Plos Onementioning

confidence: 99%

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

Sulas

Ortu

Urru³

et al. 2021

PLoS ONE

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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.

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Deep Learning Based Cardiac Phase Detection Using Echocardiography Imaging

Farhad

Masud

Beg

2022

Lecture Notes in Computer Science

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Automatic detection of complete and measurable cardiac cycles in antenatal pulsed-wave Doppler signals

Cited by 12 publications

References 19 publications

A non-invasive multimodal foetal ECG–Doppler dataset for antenatal cardiology research

A non-invasive multimodal foetal ECG–Doppler dataset for antenatal cardiology research

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

Deep Learning Based Cardiac Phase Detection Using Echocardiography Imaging

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