Novel hybrid extraction systems for fetal heart rate variability monitoring based on non-invasive fetal electrocardiogram

This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.Web of Science713178413175

Detection and Processing Techniques of FECG Signal for Fetal Monitoring

Fetal electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies are becoming very important requirements in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and developed algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature for fetal monitoring. A comparative study has been carried out to show the performance and accuracy of various methods of FECG signal analysis for fetal monitoring. Finally, this paper further focused some of the hardware implementations using electrical signals for monitoring the fetal heart rate. This paper opens up a passage for researchers, physicians, and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system

Techniques of FECG signal analysis: detection and processing for fetal monitoring

Fetal heart rate monitoring is a technique for obtaining important information about the condition of a fetus during pregnancy and labor, by detecting the FECG signal generated by the heart of the fetus. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies is becoming a very important requirement in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature. A comparative study has been carried out to show the performance of various methods. This paper opens up a passage to biomedical researchers, physicians and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system by providing valuable information to help them in developing more dominant, flexible and resourceful application

Fetal Electrocardiogram Signal Extraction by ANFIS Trained with PSO Method

Studies indicate that the primary source of distress in pregnent mothers is their concerns about fetus’s condition and health. One way to know about condition of fetus is non-invasive fetal electrocardiogram signal extraction through which the components of fetal electrocardiogram signal are extracted from a signal recorded at abdominal area of mother which is a combination of fetal and maternal electrocardiogram signal and noise source components. The purpose of this study is to propose an algorithm to boost this extraction. To this end, we decomposed electrocardiogram signal to its Intrinsic Mode Functions (IMFs) thruogh Empirical Mode Decomposition algorithm; then, we removed the last and collected the other IMFs to reconstruct electrocardiogram signal without Baseline. Afterwards, we used Particle Swarm Optimization to train and adjust the parameters of Adaptive Neuro-Fuzzy Inference System to model the path that maternal electrocardiogram signal travel to reach abdominal area. Accordingly, we were able to distinguish and remove maternal electrocardiogram signal components from the recorded signal and hence we obtained a good approximation of fetal electrocardiogram signal. We implemented our algorithm and other algorithms on simulated and real signals and found out that, in most cases, the proposed algorithm improved the extraction of fetal electrocardiogram signal.DOI:http://dx.doi.org/10.11591/ijece.v2i2.23

Efficient Blind Source Separation Algorithms with Applications in Speech and Biomedical Signal Processing

Blind source separation/extraction (BSS/BSE) is a powerful signal processing method and has been applied extensively in many fields such as biomedical sciences and speech signal processing, to extract a set of unknown input sources from a set of observations. Different algorithms of BSS were proposed in the literature, that need more investigations, related to the extraction approach, computational complexity, convergence speed, type of domain (time or frequency), mixture properties, and extraction performances. This work presents a three new BSS/BSE algorithms based on computing new transformation matrices used to extract the unknown signals. Type of signals considered in this dissertation are speech, Gaussian, and ECG signals. The first algorithm, named as the BSE-parallel linear predictor filter (BSE-PLP), computes a transformation matrix from the the covariance matrix of the whitened data. Then, use the matrix as an input to linear predictor filters whose coefficients being the unknown sources. The algorithm has very fast convergence in two iterations. Simulation results, using speech, Gaussian, and ECG signals, show that the model is capable of extracting the unknown source signals and removing noise when the input signal to noise ratio is varied from -20 dB to 80 dB. The second algorithm, named as the BSE-idempotent transformation matrix (BSE-ITM), computes its transformation matrix in iterative form, with less computational complexity. The proposed method is tested using speech, Gaussian, and ECG signals. Simulation results show that the proposed algorithm significantly separate the source signals with better performance measures as compared with other approaches used in the dissertation. The third algorithm, named null space idempotent transformation matrix (NSITM) has been designed using the principle of null space of the ITM, to separate the unknown sources. Simulation results show that the method is successfully separating speech, Gaussian, and ECG signals from their mixture. The algorithm has been used also to estimate average FECG heart rate. Results indicated considerable improvement in estimating the peaks over other algorithms used in this work

Wavelet denoising as a post-processing enhancement method for non-invasive foetal electrocardiography

Background and Objective: The detection of a clean and undistorted foetal electrocardiogram (fECG) from non-invasive abdominal recordings is an open research issue. Several physiological and instrumental noise sources hamper this process, even after that powerful fECG extraction algorithms have been used. Wavelet denoising is widely used for the improvement of the SNR in biomedical signal processing. This work aims to systematically assess conventional and unconventional wavelet denoising approaches for the post-processing of fECG signals by providing evidence of their effectiveness in improving fECG SNR while preserving the morphology of the signal of interest. Methods: The stationary wavelet transform (SWT) and the stationary wavelet packet transform (SWPT) were considered, due to their different granularity in the sub-band decomposition of the signal. Three thresholds from the literature, either conventional (Minimax and Universal) and unconventional, were selected. To this aim, the unconventional one was adapted for the first time to SWPT by trying different approaches. The decomposition depth was studied in relation to the characteristics of the fECG signal. Synthetic and real datasets, publicly available for benchmarking and research, were used for quantitative analysis in terms of noise reduction, foetal QRS detection performance and preservation of fECG morphology. Results: The adoption of wavelet denoising approaches generally improved the SNR. Interestingly, the SWT methods outperformed the SWPT ones in morphology preservation (p<0.04) and SNR (p<0.0003), despite their coarser granularity in the sub-band analysis. Remarkably, the Han et al. threshold, adopted for the first time for fECG processing, provided the best quality improvement (p<0.003). Conclusions: The findings of our systematic analysis suggest that particular care must be taken when selecting and using wavelet denoising for non-invasive fECG signal post-processing. In particular, despite the general noise reduction capability, signal morphology can be significantly altered on the basis of the parameterization of the wavelet methods. Remarkably, the adoption of a finer sub-band decomposition provided by the wavelet packet was not able to improve the quality of the processing

Development of a Novel Dataset and Tools for Non-Invasive Fetal Electrocardiography Research

This PhD thesis presents the development of a novel open multi-modal dataset for advanced studies on fetal cardiological assessment, along with a set of signal processing tools for its exploitation. The Non-Invasive Fetal Electrocardiography (ECG) Analysis (NInFEA) dataset features multi-channel electrophysiological recordings characterized by high sampling frequency and digital resolution, maternal respiration signal, synchronized fetal trans-abdominal pulsed-wave Doppler (PWD) recordings and clinical annotations provided by expert clinicians at the time of the signal collection. To the best of our knowledge, there are no similar dataset available. The signal processing tools targeted both the PWD and the non-invasive fetal ECG, exploiting the recorded dataset. About the former, the study focuses on the processing aimed at the preparation of the signal for the automatic measurement of relevant morphological features, already adopted in the clinical practice for cardiac assessment. To this aim, a relevant step is the automatic identification of the complete and measurable cardiac cycles in the PWD videos: a rigorous methodology was deployed for the analysis of the different processing steps involved in the automatic delineation of the PWD envelope, then implementing different approaches for the supervised classification of the cardiac cycles, discriminating between complete and measurable vs. malformed or incomplete ones. Finally, preliminary measurement algorithms were also developed in order to extract clinically relevant parameters from the PWD. About the fetal ECG, this thesis concentrated on the systematic analysis of the adaptive filters performance for non-invasive fetal ECG extraction processing, identified as the reference tool throughout the thesis. Then, two studies are reported: one on the wavelet-based denoising of the extracted fetal ECG and another one on the fetal ECG quality assessment from the analysis of the raw abdominal recordings. Overall, the thesis represents an important milestone in the field, by promoting the open-data approach and introducing automated analysis tools that could be easily integrated in future medical devices

Fetal electrocardiogram extraction and analysis

Diagnosis of mother’s and child’s heart beat is very necessary during pregnancy and hence we use Fetal electrocardiogram (FECG) extraction for the same. The signal contains precise informations that can help doctors during pregnancy and labor. In this thesis, an easy-to use method has been implemented using adaptive noise canceller (ANC). Using the ANC, an effective algorithm has been proposed. The algorithm uses ANC, Least Mean Square (LMS) method and a Simulink model for the extraction of FECG. The FECG extraction method has been implemented using an algorithm implemented on MATLAB using Simulink models. The extracted FECG signal is a noise free signal. The QRS complex has been detected using another algorithm that counts the R-R peaks. The simulation result shows that heart rate of the FECG signal can be counted using the detection algorithm. This project evokes a complete model of the FECG extraction with the implementation of effective algorithms and adaptive filters and finally gives the heart rate of the FECG signal

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