Extracting fetal heart beats from maternal abdominal recordings: Selection of the optimal principal components

This study presents a systematic comparison of different approaches to the automated selection of the principal components (PC) which optimise the detection of maternal and fetal heart beats from non-invasive maternal abdominal recordings. A public database of 75 4-channel non-invasive maternal abdominal recordings was used for training the algorithm. Four methods were developed and assessed to determine the optimal PC: (1) power spectral distribution, (2) root mean square, (3) sample entropy, and (4) QRS template. The sensitivity of the performance of the algorithm to large-amplitude noise removal (by wavelet de-noising) and maternal beat cancellation methods were also assessed. The accuracy of maternal and fetal beat detection was assessed against reference annotations and quantified using the detection accuracy score F1 [2*PPV*Se / (PPV + Se)], sensitivity (Se), and positive predictive value (PPV). The best performing implementation was assessed on a test dataset of 100 recordings and the agreement between the computed and the reference fetal heart rate (fHR) and fetal RR (fRR) time series quantified. The best performance for detecting maternal beats (F1 99.3%, Se 99.0%, PPV 99.7%) was obtained when using the QRS template method to select the optimal maternal PC and applying wavelet de-noising. The best performance for detecting fetal beats (F1 89.8%, Se 89.3%, PPV 90.5%) was obtained when the optimal fetal PC was selected using the sample entropy method and utilising a fixed-length time window for the cancellation of the maternal beats. The performance on the test dataset was 142.7 beats2/min2 for fHR and 19.9 ms for fRR, ranking respectively 14 and 17 (out of 29) when compared to the other algorithms presented at the Physionet Challenge 2013

Fetal ECG extraction from maternal abdominal ECG using neural network

FECG (Fetal ECG) signal contains potentially precise information that could assist clinicians in making more appro-priate and timely decisions during pregnancy and labor. The extraction and detection of the FECG signal from com-posite maternal abdominal signals with powerful and advance methodologies is becoming a very important requirement in fetal monitoring. The purpose of this paper is to illustrate the developed algorithms on FECG signal extraction from the abdominal ECG signal using Neural Network approach to provide efficient and effective ways of separating and understanding the FECG signal and its nature. The FECG signal was isolated from the abdominal signal by neural network approach with different learning constant value and momentum as well so that acceptable signal can be con-sidered. According to the output it can be said that the algorithm is working satisfactory on high learning rate and low momentum value. The method appears to be exceedingly robust, correctly isolate the FECG signal from abdominal ECG

Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography

Introduction: Wearable monitoring systems for non-invasive multi-channel fetal electrocardiography (fECG) can support fetal surveillance and diagnosis during pregnancy, thus enabling prompt treatment. In these embedded systems, power saving is the key to long-term monitoring. In this regard, the computational burden of signal processing methods implemented for the fECG extraction from the multi-channel trans-abdominal recordings plays a non-negligible role. In this work, a supervised machine-learning approach for the automatic selection of the most informative raw abdominal recordings in terms of fECG content, i.e., those potentially leading to good-quality, non-invasive fECG signals from a low number of channels, is presented and evaluated.Methods: For this purpose, several signal quality indexes from the scientific literature were adopted as features to train an ensemble tree classifier, which was asked to perform a binary classification between informative and non-informative abdominal channels. To reduce the dimensionality of the classification problem, and to improve the performance, a feature selection approach was also implemented for the identification of a subset of optimal features. 10336 5-s long signal segments derived from a real dataset of multi-channel trans-abdominal recordings acquired from 55 voluntary pregnant women between the 21st and the 27th week of gestation, with healthy fetuses, were adopted to train and test the classification approach in a stratified 10-time 10-fold cross-validation scheme. Abdominal recordings were firstly pre-processed and then labeled as informative or non-informative, according to the signal-to-noise ratio exhibited by the extracted fECG, thus producing a balanced dataset of bad and good quality abdominal channels.Results and Discussion: Classification performance revealed an accuracy above 86%, and more than 88% of those channels labeled as informative were correctly identified. Furthermore, by applying the proposed method to 50 annotated 24-channel recordings from the NInFEA dataset, a significant improvement was observed in fetal QRS detection when only the channels selected by the proposed approach were considered, compared with the use of all the available channels. As such, our findings support the hypothesis that performing a channel selection by looking directly at the raw abdominal signals, regardless of the fetal presentation, can produce a reliable measurement of fetal heart rate with a lower computational burden

Энтропия перестановок сердечного ритма плода при изъятии ударов сердца матери

Розробка і застосування методів ідентифікації фізіологічних станів для матері та плоду, заснованих на неінвазивному моніторингу серцевої діяльності, має велике клінічне значення під час вагітності. У даній роботі досліджуються нові можливості застосування ентропії перестановок (ЕП) – одного з нелінійних методів часового аналізу серцевих скорочень плода. ЕП використовується для описання ритмограм плоду з метою отримання нових даних щодо характеристик серцевого ритму плода. Використовується нова методика виділення фетальної електрокардіограми (фЕКГ), заснована на фільтрації у вейвлет-просторі та реконструкції фЕКГ з використанням коефіцієнтів деталізації. Ентропія перестановок застосовується для отримання числових значень та часових залежностей ЕП для серцевого ритму у випадку необроблених ритмограм плоду та ритмограм, отриманих з виділеними ударами серця матері. Припущення про необхідність видаляти удари серця матері із початкової ритмограми підтверджується різницею у значеннях ЕП для двох випадків.Development and application of maternal and fetal physiological states identification techniques based on the noninvasive electrical heart activity monitoring is of great clinical importance during pregnancy. In this paper, new possibilities of applying one of nonlinear measures of time series behavior analysis to the fetal heart rates are explored, and permutation entropy (PE) characteristics of fetal rhythmograms are used to get new insight on the fetal heart rhythm parameters. The new technique of fetal electrocardiogram (fECG) extraction is used, based on filtration in wavelet domain and reconstruction of fECG using detalization coefficients. Permutation entropy analysis is applied to obtain PE values and trends for the case of raw fetal rhythmograms and those obtained with excluded maternal heart beats. The assumption about the need to extract maternal heartbeats from initial rhythmogram is proven by the difference in PE values for two cases.Разработка и применение методов идентификации физиологических состояний матери и плода, основанных на неинвазивном мониторинге сердечной деятельности, имеет большое клиническое значение при беременности. В данной работе исследуются новые возможности применения энтропии перестановок (ЭП) – одного из нелинейных методов временного анализа сердечных сокращений плода. ЭП используется для описания ритмограмм плода с целью получения новых данных о характеристиках сердечного ритма плода. Используется новая методика выделения фетальной электрокардиограммы (фЭКГ), основанная на фильтрации в вейвлет-пространстве и реконструкции фЭКГ с использованием коэффициентов детализации. Энтропия перестановок применяется для получения числовых значений и временных зависимостей ЭП для сердечного ритма в случае необработанных ритмограмм плода и ритмограмм, полученных с выделенными ударами сердца матери. Предположение о необходимости удалять удары сердца матери из начальной ритмограммы подтверждается разницей в значениях ЭП для двух случаев

Generalized recursive algorithm for fetal electrocardiogram isolation from non-invasive maternal electrocardiogram

Non-invasive maternal electrocardiogram recording is the least unpleasant method to record a weak fetal electrocardiogram signal. The importance of this recording lies in the fact that it reveals crucial information about the fetal health state, especially during the last four weeks of pregnancy. This paper will be concerned with a new adaptive algorithm, namely the generalized recursive algorithm, to isolate and get the fetal electrocardiogram from the abdominal maternal electrocardiogram. This is achieved using a non-invasive method for bi-channel maternal electrocardiogram recordings i.e., with the thoracic maternal electrocardiogram as a reference signal, and the abdominal maternal electrocardiogram as a primary signal. Prior to this procedure, the discrete wavelet transform (DWT) method is applied to the abdominal electrocardiogram signal to clean it from any additive noise and the baseline wandering that is generally present on the raw recordings. The proposed new adaptive filter is shown to deliver improved characteristics through simulations. These simulations were performed on both synthetic and actual signals. This work was compared with the normalized least mean square algorithm