Estrazione non invasiva del segnale elettrocardiografico fetale da registrazioni con elettrodi posti sull’addome della gestante (Non-invasive extraction of the fetal electrocardiogram from abdominal recordings by positioning electrodes on the pregnant woman’s abdomen)

openIl cuore è il primo organo che si sviluppa nel feto, particolarmente nelle primissime settimane di gestazione. Rispetto al cuore adulto, quello fetale ha una fisiologia ed un’anatomia significativamente differenti, a causa della differente circolazione cardiovascolare. Il benessere fetale si valuta monitorando l’attività cardiaca mediante elettrocardiografia fetale (ECGf). L’ECGf invasivo (acquisito posizionando elettrodi allo scalpo fetale) è considerato il gold standard, ma l’invasività che lo caratterizza ne limita la sua applicabilità. Al contrario, l’uso clinico dell’ECGf non invasivo (acquisito posizionando elettrodi sull’addome della gestante) è limitato dalla scarsa qualità del segnale risultante. L’ECGf non invasivo si estrae da registrazioni addominali, che sono corrotte da differenti tipi di rumore, fra i quali l’interferenza primaria è rappresentata dall’ECG materno. Il Segmented-Beat Modulation Method (SBMM) è stato da me recentemente proposto come una nuova procedura di filtraggio basata sul calcolo del template del battito cardiaco. SBMM fornisce una stima ripulita dell’ECG estratto da registrazioni rumorose, preservando la fisiologica variabilità ECG del segnale originale. Questa caratteristica è ottenuta grazie alla segmentazione di ogni battito cardiaco per indentificare i segmenti QRS e TUP, seguito dal processo di modulazione/demodulazione (che include strecciamento e compressione) del segmento TUP, per aggiustarlo in modo adattativo alla morfologia e alla durata di ogni battito originario. Dapprima applicato all’ECG adulto al fine di dimostrare la sua robustezza al rumore, l’SBMM è stato poi applicato al caso fetale. Particolarmente significativi sono i risultati relativi alle applicazioni su ECGf non invasivo, dove l’SBMM fornisce segnali caratterizzati da un rapporto segnale-rumore comparabile a quello caratterizzante l’ECGf invasivo. Tuttavia, l’SBMM può contribuire alla diffusione dell’ECGf non invasiva nella pratica clinica.The heart is the first organ that develops in the fetus, particularly in the very early stages of pregnancy. Compared to the adult heart, the physiology and anatomy of the fetal heart exhibit some significant differences. These differences originate from the fact that the fetal cardiovascular circulation is different from the adult circulation. Fetal well-being evaluation may be accomplished by monitoring cardiac activity through fetal electrocardiography (fECG). Invasive fECG (acquired through scalp electrodes) is the gold standard but its invasiveness limits its clinical applicability. Instead, clinical use of non-invasive fECG (acquired through abdominal electrodes) has so far been limited by its poor signal quality. Non-invasive fECG is extracted from the abdominal recording and is corrupted by different kind of noise, among which maternal ECG is the main interference. The Segmented-Beat Modulation Method (SBMM) was recently proposed by myself as a new template-based filtering procedure able to provide a clean ECG estimation from a noisy recording by preserving physiological ECG variability of the original signal. The former feature is achieved thanks to a segmentation procedure applied to each cardiac beat in order to identify the QRS and TUP segments, followed by a modulation/demodulation process (involving stretching and compression) of the TUP segments to adaptively adjust each estimated cardiac beat to the original beat morphology and duration. SBMM was first applied to adult ECG applications, in order to demonstrate its robustness to noise, and then to fECG applications. Particularly significant are the results relative to the non-invasive applications, where SBMM provided fECG signals characterized by a signal-to-noise ratio comparable to that characterizing invasive fECG. Thus, SBMM may contribute to the spread of this noninvasive fECG technique in the clinical practice.INGEGNERIA DELL'INFORMAZIONEAgostinelli, AngelaAgostinelli, Angel

Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease

INTRODUCTION: This study aims to investigate non-invasive electrocardiography as a method for the detection of congenital heart disease (CHD) with the help of artificial intelligence.MATERIAL AND METHODS: An artificial neural network was trained for the identification of CHD using non-invasively obtained fetal electrocardiograms. With the help of a Bayesian updating rule, multiple electrocardiographs were used to increase the algorithm's performance.RESULTS: Using 122 measurements containing 65 healthy and 57 CHD cases, the accuracy, sensitivity, and specificity were found to be 71%, 63%, and 77%, respectively. The sensitivity was however 75% and 69% for CHD cases requiring an intervention in the neonatal period and first year of life, respectively. Furthermore, a positive effect of measurement length on the detection performance was observed, reaching optimal performance when using 14 electrocardiography segments (37.5 min) or more. A small negative trend between gestational age and accuracy was found.CONCLUSIONS: The proposed method combining recent advances in obtaining non-invasive fetal electrocardiography with artificial intelligence for the automatic detection of CHD achieved a detection rate of 63% for all CHD and 75% for critical CHD. This feasibility study shows that detection rates of CHD might improve by using electrocardiography-based screening complementary to the standard ultrasound-based screening. More research is required to improve performance and determine the benefits to clinical practice.</p

Implementation of the combined use of non-invasive fetal electrocardiography and electrohysterography during labor:A prospective clinical study

INTRODUCTION: Fetal electrocardiography (NI-fECG) and electrohysterography (EHG) have been proven more accurate and reliable than conventional non-invasive methods (doppler ultrasound and tocodynamometry) and are less affected by maternal obesity. It is still unknown whether NI-fECG and EHG will eliminate the need for invasive methods, such as the intrauterine pressure catheter and fetal scalp electrode. We studied whether NI-fECG and EHG can be successfully used during labor.MATERIAL AND METHODS: A prospective clinical pilot study was performed in a tertiary care teaching hospital. A total of 50 women were included with a singleton pregnancy with a gestational age between 36 +0 and 42 +0  weeks and had an indication for continuous intrapartum monitoring. The primary study outcome was the percentage of women with NI-fECG and EHG monitoring throughout the whole delivery. Secondary study outcomes were reason and timing of a switch to conventional monitoring methods (i.e., tocodynamometry and fetal scalp electrode or doppler ultrasound), repositioning of the abdominal electrode patch, success rates (i.e., the percentage of time with signal output), and obstetric and neonatal outcomes. CLINICAL TRIAL REGISTRATION: Dutch trial register (NL8024).RESULTS: In 45 women (90%), NI-fECG and EHG monitoring was used throughout the whole delivery. In the other five women (10%), there was a switch to conventional methods: in two women because of insufficient registration quality of uterine contractions and in three women because of insufficient registration quality of the fetal heart rate. In three out of five cases, the switch was after full dilation was reached. Repositioning of the abdominal electrode patch occurred in two women. The overall success rate was 94.5%. In 16% (n = 8) of women, a cesarean delivery was performed due to non-progressing dilation (n = 7) and due to suspicion of fetal distress (n = 1). Neonatal metabolic acidosis did not occur. Two neonates (4%) were admitted to the neonatal intensive care unit for complications not related to intrapartum monitoring.CONCLUSIONS: NI-fECG and EHG can be successfully used during labor in 90% of women. Future research is needed to conclude whether implementation of electrophysiological monitoring can improve obstetric and neonatal outcomes.</p

Hybrid methods based on empirical mode decomposition for non-invasive fetal heart rate monitoring

This study focuses on fetal electrocardiogram (fECG) processing using hybrid methods that combine two or more individual methods. Combinations of independent component analysis (ICA), wavelet transform (WT), recursive least squares (RLS), and empirical mode decomposition (EMD) were used to create the individual hybrid methods. Following four hybrid methods were compared and evaluated in this study: ICA-EMD, ICA-EMD-WT, EMD-WT, and ICA-RLS-EMD. The methods were tested on two databases, the ADFECGDB database and the PhysioNet Challenge 2013 database. Extraction evaluation is based on fetal heart rate (fHR) determination. Statistical evaluation is based on determination of correct detection (ACC), sensitivity (Se), positive predictive value (PPV), and harmonic mean between Se and PPV (F1). In this study, the best results were achieved by means of the ICA-RLS-EMD hybrid method, which achieved accuracy(ACC) > 80% at 9 out of 12 recordings when tested on the ADFECGDB database, reaching an average value of ACC > 84%, Se > 87%, PPV > 92%, and F1 > 90%. When tested on the Physionet Challenge 2013 database, ACC > 80% was achieved at 12 out of 25 recordings with an average value of ACC > 64%, Se > 69%, PPV > 79%, and F1 > 72%.Web of Science8512185120

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

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

Quality predictors of abdominal fetal electrocardiography recording in antenatal ambulatory and bedside settings

Background: Fetal electrocardiography using an abdominal monitor (Monica AN24™) could increase the diagnostic use of fetal heart rate (fHR) variability measurements. However, signal quality may depend on factors such as maternal physical activity, posture, and bedside versus ambulatory setting. Methods: Sixty-three healthy women wore the monitor at home and 42 women during a hospital stay. All women underwent a posture experiment, and all home and 13 hospital participants wore the monitor during daytime and nighttime. The success rate (SR) of fHR detection was analyzed in relation to maternal physical activity, posture, daytime versus nighttime, and other maternal and fetal predictors. Results: Ambulatorily, the SR was 86.8% for nighttime and 40.2% for daytime. The low daytime SR was largely due to effects of maternal physical activity and posture. The in-hospital SR was lower during nighttime (71.1%) and similar during daytime (43.3%). SR was related to gestational age, but not affected by pre-pregnancy and current body mass index or fetal growth restriction. Conclusions: The success of beat-to-beat fHR detection strongly depends on the home/hospital setting and predictors such as time of recording, activity levels, and maternal posture. Its clinical utility may be limited in periods of unsupervised recording with physical activity or posture shifts