Non-invasive fetal monitoring: a maternal surface ECG electrode placement-based novel approach for optimization of adaptive filter control parameters using the LMS and RLS algorithms

This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size mu and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.Web of Science175art. no. 115

Preprocessing of Fetal Electrocardiogram

Tato práce se zabývá klasifikací a popisem vybraných filtračních technik, určených k potlačení nejčastějších typů rušení plodového elektrokardiogramu. Účelem snímání plodového elektrokardiogramu je dosažení plnohodnotného záznamu pro zhodnocení zdravotního stavu plodu a převážně pro správnou diagnostiku hypoxických stavů. Cílem práce je tedy návrh a realizace softwaru, který bude využitelný pro předzpracování a umožní další analýzu signálu. K řešení byly použity filtry typu FIR, IIR a vlnková transformace. Návrh softwaru je popsán v praktické části práce. K ovládání programu bylo vytvořeno grafické uživatelské rozhraní, kde je možné simulovat základní typy rušení, vybrat filtr a nastavit jednotlivé parametry pro filtraci.This thesis is focused on classification and description of selected filtration techniques which are designed to suppress the most frequent types of fetal electrocardiogram interference. The purpose of fetal electrocardiogram recording is to achieve a full-fledged record for assessment of fetal health and mostly for correct diagnosis of hypoxic states. The aim of this study therefore is to design and realize a software which would be usable for preprocessing and would enable further signal analysis. The FIR and IIR filters and wavelet transform were used as a solution. The design of the software is described in the practical part of this thesis. For operating the program, a graphical user interface was created. There, it is possible to simulate basic types of interference, to choose a filter, and to set individual parameters for the filtering.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Fetal ECG Extraction using Adaptive Linear Neural Network

Tato bakalářská práce se zabývá extrakcí plodového (fetálního) signálu EKG (fEKG) pomocí adaptivního lineárního neuronu (ADALINE). Plodový EKG záznam obsahuje velice důležité informace týkající se zdravotního stavu plodu nejen při porodu, ale i v průběhu těhotenství. První část této práce se zabývá současným stavem dané problematiky, klasifikací metody adaptivního lineárního neuronu a zabývá se jejím matematickým popisem. Dále je zde obsažen návrh a realizace adaptivního systému pro extrakci signálu fEKG ze zarušeného signálu břišního (abdominálního) EKG (aEKG) pomocí metody ADALINE. Mimo jiné tato práce dále zahrnuje tvorbu grafického uživatelského rozhraní (GUI – z angl. Graphical User Interface) v programu Matlab a ověření funkčnosti navrhnutého adaptivního systému nejen na syntetických, ale také na reálných datech z klinické praxe.This bachelor thesis deals with the extraction of the fetal (fetal) ECG signal (fECG) using Adaptive Linear Neuron (ADALINE). The fetal ECG record contains very important information about the health of the fetus not only during labor but also during pregnancy. The first part of this thesis deals with the current state of the given issue, contains the classification of the method of adaptive linear neuron and its mathematical description. Additionally, the design and implementation of the adaptive system for extracting the fECG signal from the abdominal ECG signal (aECG) using the ADALINE method is included. This work also contains the creation of a Graphical User Interface (GUI) in Matlab and the verification of the proposed adaptive system not only on synthetic but also on real data from clinical practice.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Adaptive Signal Processing Techniques for Extracting Abdominal Fetal Electrocardiogram

Import 03/11/2016Tato diplomová práce se zabývá problematikou snímání plodového elektrokardiogramu z transabdominálního záznamu. Ten by se v budoucnu mohl stát velmi účinným a nezbytným nástrojem v monitorování a diagnostice ohrožených plodů v průběhu těhotenství a během porodu. Největším problémem, se kterým se tento způsob monitorace potýká, je velké množství nežádoucích složek, které jsou snímány společně s užitečným signálem, zejména pak mateřský elektrokardiogram. Autorka se zaměřuje zejména na využití adaptivních metod pro extrakci plodového elektrokardiogramu z takto zarušeného transabdominálního záznamu. Tato práce obsahuje mimo jiné také obsáhlé shrnutí této poměrně nové problematiky, klasifikaci a popis vybraných adaptivních metod a zejména návrh a realizaci adaptivního systému pro potlačování „nežádoucího“ mateřského elektrokardiogramu. Ověření funkčnosti tohoto systému bylo provedeno na syntetických i reálných datech.This thesis focuses on the fetal electrocardiogram recorded transabdominally. This method could become very efficient and essential tool in monitoring and diagnosing endangered fetuses during the pregnancy and the delivery. The greatest challenge connected with this kind of monitoring is the amount of noise that is recorded within the desired signal. This thesis aims at the use of adaptive methods for extracting fetal electrocardiogram from such abdominal signal. This thesis includes among others an extensive summary of this relatively new issue, classification and description of selected linear adaptive methods, and in particular, the design and the implementation of adaptive system for suppressing the ‚undesirable‘ maternal electrocardiogram.450 - Katedra kybernetiky a biomedicínského inženýrstvívelmi dobř

Comparative Study of Adaptive Methods as a Part of Hybrid System for Fetal ECG Signal Extraction

Hlavním cílem této diplomové práce je extrakce plodového EKG signálu výhradně z abdominálních signálů (aEKG) snímaných neinvazivně, transabdominálně, pomocí elektrod umístěných na břiše matky. Signál aEKG v sobě obsahuje nejen plodovou, ale také mateřskou komponentu EKG (mEKG) a další rušivé signály. Teoretická část práce se věnuje současnému stavu této problematiky, popisu metod využívajících se k monitoraci plodu v klinické praxi a klasifikací vybraných metod a jejich matematickému rozboru. Dále se práce zaměřuje na návrh a realizaci hybridního systému, který k extrakci fEKG signálu využívá kombinace neadaptivních a adaptivních metod. Hodnocení kvality filtrace je provedeno na základě statistických parametrů ACC a F1 a také na základě stanovení variability tepové frekvence plodu (fHR). Ověření funkčnosti navrženého algoritmu bylo provedeno na reálných datech z klinické praxe a také na datech měřených přímo v laboratoři.The main goal of this diploma thesis is the extraction of the fetal ECG signal exclusively from abdominal signals (aECG) scanned non-invasively by transabdominal method, using electrodes placed on the mother's abdomen. The aECG signal contains not only the fetal but also the maternal component of the ECG (mECG) and other interfering signals. The theoretical part of the work deals with the current state of this issue, a description of methods used to monitor the fetus in clinical practice and the classification of selected methods and their mathematical analysis. Furthermore, the work focuses on the design and implementation of a hybrid system that uses a combination of non-adaptive and adaptive methods to extract the fECG signal. Evaluation of filtration quality is performed on the basis of statistical parameters ACC and F1 and also on the basis of determination of fetal heart rate variability (fHR). Verification of the functionality of the proposed algorithm was performed on real data from clinical practice and also on data measured directly in the laboratory.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Extraction and Detection of Fetal Electrocardiograms from Abdominal Recordings

The non-invasive fetal ECG (NIFECG), derived from abdominal surface electrodes, offers novel diagnostic possibilities for prenatal medicine. Despite its straightforward applicability, NIFECG signals are usually corrupted by many interfering sources. Most significantly, by the maternal ECG (MECG), whose amplitude usually exceeds that of the fetal ECG (FECG) by multiple times. The presence of additional noise sources (e.g. muscular/uterine noise, electrode motion, etc.) further affects the signal-to-noise ratio (SNR) of the FECG. These interfering sources, which typically show a strong non-stationary behavior, render the FECG extraction and fetal QRS (FQRS) detection demanding signal processing tasks. In this thesis, several of the challenges regarding NIFECG signal analysis were addressed. In order to improve NIFECG extraction, the dynamic model of a Kalman filter approach was extended, thus, providing a more adequate representation of the mixture of FECG, MECG, and noise. In addition, aiming at the FECG signal quality assessment, novel metrics were proposed and evaluated. Further, these quality metrics were applied in improving FQRS detection and fetal heart rate estimation based on an innovative evolutionary algorithm and Kalman filtering signal fusion, respectively. The elaborated methods were characterized in depth using both simulated and clinical data, produced throughout this thesis. To stress-test extraction algorithms under ideal circumstances, a comprehensive benchmark protocol was created and contributed to an extensively improved NIFECG simulation toolbox. The developed toolbox and a large simulated dataset were released under an open-source license, allowing researchers to compare results in a reproducible manner. Furthermore, to validate the developed approaches under more realistic and challenging situations, a clinical trial was performed in collaboration with the University Hospital of Leipzig. Aside from serving as a test set for the developed algorithms, the clinical trial enabled an exploratory research. This enables a better understanding about the pathophysiological variables and measurement setup configurations that lead to changes in the abdominal signal's SNR. With such broad scope, this dissertation addresses many of the current aspects of NIFECG analysis and provides future suggestions to establish NIFECG in clinical settings.:Abstract Acknowledgment Contents List of Figures List of Tables List of Abbreviations List of Symbols (1)Introduction 1.1)Background and Motivation 1.2)Aim of this Work 1.3)Dissertation Outline 1.4)Collaborators and Conflicts of Interest (2)Clinical Background 2.1)Physiology 2.1.1)Changes in the maternal circulatory system 2.1.2)Intrauterine structures and feto-maternal connection 2.1.3)Fetal growth and presentation 2.1.4)Fetal circulatory system 2.1.5)Fetal autonomic nervous system 2.1.6)Fetal heart activity and underlying factors 2.2)Pathology 2.2.1)Premature rupture of membrane 2.2.2)Intrauterine growth restriction 2.2.3)Fetal anemia 2.3)Interpretation of Fetal Heart Activity 2.3.1)Summary of clinical studies on FHR/FHRV 2.3.2)Summary of studies on heart conduction 2.4)Chapter Summary (3)Technical State of the Art 3.1)Prenatal Diagnostic and Measuring Technique 3.1.1)Fetal heart monitoring 3.1.2)Related metrics 3.2)Non-Invasive Fetal ECG Acquisition 3.2.1)Overview 3.2.2)Commercial equipment 3.2.3)Electrode configurations 3.2.4)Available NIFECG databases 3.2.5)Validity and usability of the non-invasive fetal ECG 3.3)Non-Invasive Fetal ECG Extraction Methods 3.3.1)Overview on the non-invasive fetal ECG extraction methods 3.3.2)Kalman filtering basics 3.3.3)Nonlinear Kalman filtering 3.3.4)Extended Kalman filter for FECG estimation 3.4)Fetal QRS Detection 3.4.1)Merging multichannel fetal QRS detections 3.4.2)Detection performance 3.5)Fetal Heart Rate Estimation 3.5.1)Preprocessing the fetal heart rate 3.5.2)Fetal heart rate statistics 3.6)Fetal ECG Morphological Analysis 3.7)Problem Description 3.8)Chapter Summary (4)Novel Approaches for Fetal ECG Analysis 4.1)Preliminary Considerations 4.2)Fetal ECG Extraction by means of Kalman Filtering 4.2.1)Optimized Gaussian approximation 4.2.2)Time-varying covariance matrices 4.2.3)Extended Kalman filter with unknown inputs 4.2.4)Filter calibration 4.3)Accurate Fetal QRS and Heart Rate Detection 4.3.1)Multichannel evolutionary QRS correction 4.3.2)Multichannel fetal heart rate estimation using Kalman filters 4.4)Chapter Summary (5)Data Material 5.1)Simulated Data 5.1.1)The FECG Synthetic Generator (FECGSYN) 5.1.2)The FECG Synthetic Database (FECGSYNDB) 5.2)Clinical Data 5.2.1)Clinical NIFECG recording 5.2.2)Scope and limitations of this study 5.2.3)Data annotation: signal quality and fetal amplitude 5.2.4)Data annotation: fetal QRS annotation 5.3)Chapter Summary (6)Results for Data Analysis 6.1)Simulated Data 6.1.1)Fetal QRS detection 6.1.2)Morphological analysis 6.2)Own Clinical Data 6.2.1)FQRS correction using the evolutionary algorithm 6.2.2)FHR correction by means of Kalman filtering (7)Discussion and Prospective 7.1)Data Availability 7.1.1)New measurement protocol 7.2)Signal Quality 7.3)Extraction Methods 7.4)FQRS and FHR Correction Algorithms (8)Conclusion References (A)Appendix A - Signal Quality Annotation (B)Appendix B - Fetal QRS Annotation (C)Appendix C - Data Recording GU