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

Efficient fetal-maternal ECG signal separation from two channel maternal abdominal ECG via diffusion-based channel selection

There is a need for affordable, widely deployable maternal-fetal ECG monitors to improve maternal and fetal health during pregnancy and delivery. Based on the diffusion-based channel selection, here we present the mathematical formalism and clinical validation of an algorithm capable of accurate separation of maternal and fetal ECG from a two channel signal acquired over maternal abdomen

Improving Maternal and Fetal Cardiac Monitoring Using Artificial Intelligence

Early diagnosis of possible risks in the physiological status of fetus and mother during pregnancy and delivery is critical and can reduce mortality and morbidity. For example, early detection of life-threatening congenital heart disease may increase survival rate and reduce morbidity while allowing parents to make informed decisions. To study cardiac function, a variety of signals are required to be collected. In practice, several heart monitoring methods, such as electrocardiogram (ECG) and photoplethysmography (PPG), are commonly performed. Although there are several methods for monitoring fetal and maternal health, research is currently underway to enhance the mobility, accuracy, automation, and noise resistance of these methods to be used extensively, even at home. Artificial Intelligence (AI) can help to design a precise and convenient monitoring system. To achieve the goals, the following objectives are defined in this research: The first step for a signal acquisition system is to obtain high-quality signals. As the first objective, a signal processing scheme is explored to improve the signal-to-noise ratio (SNR) of signals and extract the desired signal from a noisy one with negative SNR (i.e., power of noise is greater than signal). It is worth mentioning that ECG and PPG signals are sensitive to noise from a variety of sources, increasing the risk of misunderstanding and interfering with the diagnostic process. The noises typically arise from power line interference, white noise, electrode contact noise, muscle contraction, baseline wandering, instrument noise, motion artifacts, electrosurgical noise. Even a slight variation in the obtained ECG waveform can impair the understanding of the patient's heart condition and affect the treatment procedure. Recent solutions, such as adaptive and blind source separation (BSS) algorithms, still have drawbacks, such as the need for noise or desired signal model, tuning and calibration, and inefficiency when dealing with excessively noisy signals. Therefore, the final goal of this step is to develop a robust algorithm that can estimate noise, even when SNR is negative, using the BSS method and remove it based on an adaptive filter. The second objective is defined for monitoring maternal and fetal ECG. Previous methods that were non-invasive used maternal abdominal ECG (MECG) for extracting fetal ECG (FECG). These methods need to be calibrated to generalize well. In other words, for each new subject, a calibration with a trustable device is required, which makes it difficult and time-consuming. The calibration is also susceptible to errors. We explore deep learning (DL) models for domain mapping, such as Cycle-Consistent Adversarial Networks, to map MECG to fetal ECG (FECG) and vice versa. The advantages of the proposed DL method over state-of-the-art approaches, such as adaptive filters or blind source separation, are that the proposed method is generalized well on unseen subjects. Moreover, it does not need calibration and is not sensitive to the heart rate variability of mother and fetal; it can also handle low signal-to-noise ratio (SNR) conditions. Thirdly, AI-based system that can measure continuous systolic blood pressure (SBP) and diastolic blood pressure (DBP) with minimum electrode requirements is explored. The most common method of measuring blood pressure is using cuff-based equipment, which cannot monitor blood pressure continuously, requires calibration, and is difficult to use. Other solutions use a synchronized ECG and PPG combination, which is still inconvenient and challenging to synchronize. The proposed method overcomes those issues and only uses PPG signal, comparing to other solutions. Using only PPG for blood pressure is more convenient since it is only one electrode on the finger where its acquisition is more resilient against error due to movement. The fourth objective is to detect anomalies on FECG data. The requirement of thousands of manually annotated samples is a concern for state-of-the-art detection systems, especially for fetal ECG (FECG), where there are few publicly available FECG datasets annotated for each FECG beat. Therefore, we will utilize active learning and transfer-learning concept to train a FECG anomaly detection system with the least training samples and high accuracy. In this part, a model is trained for detecting ECG anomalies in adults. Later this model is trained to detect anomalies on FECG. We only select more influential samples from the training set for training, which leads to training with the least effort. Because of physician shortages and rural geography, pregnant women's ability to get prenatal care might be improved through remote monitoring, especially when access to prenatal care is limited. Increased compliance with prenatal treatment and linked care amongst various providers are two possible benefits of remote monitoring. If recorded signals are transmitted correctly, maternal and fetal remote monitoring can be effective. Therefore, the last objective is to design a compression algorithm that can compress signals (like ECG) with a higher ratio than state-of-the-art and perform decompression fast without distortion. The proposed compression is fast thanks to the time domain B-Spline approach, and compressed data can be used for visualization and monitoring without decompression owing to the B-spline properties. Moreover, the stochastic optimization is designed to retain the signal quality and does not distort signal for diagnosis purposes while having a high compression ratio. In summary, components for creating an end-to-end system for day-to-day maternal and fetal cardiac monitoring can be envisioned as a mix of all tasks listed above. PPG and ECG recorded from the mother can be denoised using deconvolution strategy. Then, compression can be employed for transmitting signal. The trained CycleGAN model can be used for extracting FECG from MECG. Then, trained model using active transfer learning can detect anomaly on both MECG and FECG. Simultaneously, maternal BP is retrieved from the PPG signal. This information can be used for monitoring the cardiac status of mother and fetus, and also can be used for filling reports such as partogram

BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring

Fetal monitoring may help with possible recognition of problems in the fetus. This research work focuses on the design of the Back-propagation Neural Network (BPNN) and Adaptive Linear Neural Network (ADALINE) to extract the Fetal Electrocardiogram (FECG) from the Abdominal ECG (AECG). FECG is extracted to assess the fetus well-being during the pregnancy period of a mother to overcome some existing difficulties regarding the fetal heart rate (FHR) monitoring system. Different sets of ECG signal has been tested to validate the algorithm performance. The accuracy of the QRS detection using the designed algorithm is 99%. This research work further made a comparison study between various methods' performance and accuracy and found that the developed algorithm gives the highest accuracy. This paper opens up a passage to biomedical scientists, researchers, and end users to advocate to extract the FECG signal from the AECG signal for FHR monitoring system by providing valuable information to help them for developing more dominant, flexible and resourceful applications.Muhammad Asraful Hasan and Md Mamu

Fetal ECG Extraction from Maternal ECG using Attention-based CycleGAN

Non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of FECG, the overlap of R waves, and the potential exposure to noise from different sources. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration, such as modeling the noise or desired signal. to map MECG to FECG efficiently. The high correlation between maternal and fetal ECG parts decreases the performance of convolution layers. Therefore, the masking region of interest using the attention mechanism is performed for improving signal generators' precision. The sine activation function is also used since it could retain more details when converting two signal domains. Three available datasets from the Physionet, including A&D FECG, NI-FECG, and NI-FECG challenge, and one synthetic dataset using FECGSYN toolbox, are used to evaluate the performance. The proposed method could map abdominal MECG to scalp FECG with an average 98% R-Square [CI 95%: 97%, 99%] as the goodness of fit on A&D FECG dataset. Moreover, it achieved 99.7 % F1-score [CI 95%: 97.8-99.9], 99.6% F1-score [CI 95%: 98.2%, 99.9%] and 99.3% F1-score [CI 95%: 95.3%, 99.9%] for fetal QRS detection on, A&D FECG, NI-FECG and NI-FECG challenge datasets, respectively. These results are comparable to the state-of-the-art; thus, the proposed algorithm has the potential of being used for high-performance signal-to-signal conversion

Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG

[EN] Objective: This study proposes a reference database, composed of a large number of simulated ECG signals in atrial fibrillation (AF), for investigating the performance of methods for extraction of atrial fibrillatory waves (f -waves). Approach: The simulated signals are produced using a recently published and validated model of 12-lead ECGs in AF. The database is composed of eight signal sets together accounting for a wide range of characteristics known to represent major challenges in f -wave extraction, including high heart rates, high morphological QRST variability, and the presence of ventricular premature beats. Each set contains 30 5 min signals with different f -wave amplitudes. The database is used for the purpose of investigating the statistical association between different indices, designed for use with either real or simulated signals. Main results: Using the database, available at the PhysioNet repository of physiological signals, the performance indices unnormalized ventricular residue (uVR), designed for real signals, and the root mean square error, designed for simulated signals, were found to exhibit the strongest association, leading to the recommendation that uVR should be used when characterizing performance in real signals. Significance: The proposed database facilitates comparison of the performance of different f -wave extraction methods and makes it possible to express performance in terms of the error between simulated and extracted f -wave signals.This work was supported by project DPI2017-83952-C3 of the Spanish Ministry of Economy, Industry and Competitiveness, project SBPLY/17/180501/000411 of the Junta de Comunidades de Castilla-La Mancha, Grant 'Jose Castillejo' (CAS17/00436) from the Spanish Ministry of Education, Culture and Sport, Grant No. BEST/2017/028 from the Education, Research, Culture and Sports Department of Generalitat Valenciana, European Regional Development Fund, and Grant No. 03382/2016 from the Swedish Research Council.Alcaraz, R.; Sornmo, L.; Rieta, JJ. (2019). Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG. Physiological Measurement. 40(7):1-11. https://doi.org/10.1088/1361-6579/ab2b17S111407Chugh, S. 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Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Non-invasive fetal electrocardiogram extraction based on novel hybrid method for intrapartum ST segment analysis

This study focuses on non-invasive fetal electrocardiogram extraction based on a novel hybrid method, which combines the advantages of non-adaptive and adaptive approaches for non-invasive fetal electrocardiogram morphological analysis. Besides estimating fetal heart rate, which is the main parameter used in the clinical practice, this study provides non-invasive ST segment analysis on data from Abdominal and Direct Fetal Electrocardiogram Database consisting of simultaneous traditional - gold standard invasive fetal scalp electrode and non-invasive fetal electrocardiogram recorded during delivery. This innovative approach utilizing the combination of independent component analysis and recursive least squares algorithms has the potential to extract valuable information from non-invasive fetal electrocardiogram in order to identify eventual sign of fetal distress. This was a prospective observational study of non-invasive fetal electrocardiogram, using 4 abdominally sited electrodes, against the traditional fetal scalp electrode on 8 patients. In terms of fetal heart rate estimation, the accuracy was high for all 8 tested patients with average value equaled 0.20 beats per minute and average value of 1.96 standard deviation equaled 5.80 beats per minute. In 7 patients, it was possible to perform the ST segment analysis with high accuracy in determining T/QRS in comparison with the reference fetal scalp electrode signal with average values and 1.96 standard deviation equaled 0.008 and 0.031 respectively. This study thus demonstrates that ST segment analysis is feasible using non-invasive fECG using the proposed hybrid method.Web of Science9286312860

Machine Learning in Fetal Cardiology: What to Expect

In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approaches are currently used to evaluate fetal cardiac structure and function, including conventional 2-D imaging and M-mode and tissue Doppler imaging among others. However, assessment of the fetal heart is still challenging mainly due to involuntary movements of the fetus, the small size of the heart, and the lack of expertise in fetal echocardiography of some sonographers. Therefore, the use of new technologies to improve the primary acquired images, to help extract measurements, or to aid in the diagnosis of cardiac abnormalities is of great importance for optimal assessment of the fetal heart. Machine leaning (ML) is a computer science discipline focused on teaching a computer to perform tasks with specific goals without explicitly programming the rules on how to perform this task. In this review we provide a brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of fetal cardiac remodeling and abnormalities

Artificial Neural Networks as Approach for Fetal Electrocardiogram Extraction and R-peak Detection

Tato diplomová práce se zabývá extrakcí fetálního plodového elektrokardiogramu (fEKG) pomocí metod využívající umělé neuronové sítě (ANN). Po prostudování problematiky zpracování neinvazivního fEKG (NI-fEKG) signálu byla provedena rešerše současných metod využívající ANN pro extrakci fEKG signálu z abdominálního signálu (aEKG). Na základě provedené rešerše byly vybrány metody využívající lineární adaptivní neuron (ADALINE), adaptivní neuro-fuzzy inferenční systém (ANFIS) a rekurentní sítě (RNN) tzv. Echo state sítě. Tyto metody byly také využity v kombinaci s dopřednou vícevrstvou ANN (ANN-ADALINE, ANN-ANFIS, ANN-ESN). Testování vybraných metod bylo provedeno na reálných datech z databáze Labour dataset a Pregnancy dataset. Pro vyhodnocení extrakce a stanovení plodové srdeční frekvence (fHR) byly detekovány R-kmity pomocí dvou detektorů. První detektor byl založen na spojité vlnkové transformaci (CWT), druhý detektor byl založen na dopředné vícevrstvé ANN. Pro zhodnocení byla stanovena celková pravděpodobnost správné detekce (ACC), senzitivita (SE), pozitivní prediktivní hodnota (PPV) a jako harmonický průměr SE a PPV byl stanoven parametr F1. Funkčnost metod byla ověřena vůči referenčním anotacím. Ve srovnání s metodami ADALINE, ANFIS, ANN-ADALINE, ANN-ANFIS a ANN-ESN, dosáhla metoda ESN nejlepších výsledků. Pro data z databáze Labour dataset dosahovala metoda hodnoty ACC 78,65 %, pro data z databáze Pregnancy dataset byla hodnota ACC přes 80 %. Pro zpracování, analýzu a vyhodnocení bylo vytvořeno grafické uživatelské rozhraní (GUI) v programu MATLAB.This thesis deals with the extraction of fetal electrocardiogram (fECG) through methods that use Artificial Neural Networks (ANN). After careful examination of non-invasive fECG (NI-fECG) signal processing, a search of current methods using ANN for extraction of fECG signal was performed. Based on the search, methods using a Linear Adaptive Neuron (ADALINE), an Adaptive Neuro-fuzzy Inference System (ANFIS) and a Recurrent Network (RNN), the so-called Echo State Network (ESN), were selected. These methods were also used in combination with Multilayer Feedforward ANN (ANN-ADALINE, ANN-ANFIS, ANN-ESN). Testing of the chosen methods was performed on real data from the Labour dataset and Pregnancy dataset databases. R-peaks were detected using two detectors to evaluate extraction and fetal heart rate (fHR). The first detector was based on continuous wavelet transform (CWT), the second detector was based on Multilayer Feedforward ANN. For evaluation the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and the harmonic mean of SE and PPV (F1) were determined. The functionality of chosen methods was verified by comparison to reference anotations. In comparison to methods ADALINE, ANFIS, ANN-ADALINE, ANN-ANFIS a ANN-ESN, the ESN method achieved the best results. For data from the Labor dataset, the ACC value reached 78.65 %, for data from the Pregnancy dataset, the ACC value was over 80 %. A graphical user interface (GUI) was created for processing, analysis and evaluation in MATLAB.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn