An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects

<p>Abstract</p> <p>Background</p> <p>Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia.</p> <p>Methods</p> <p>In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm.</p> <p>Results</p> <p>A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%.</p> <p>Conclusions</p> <p>The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians.</p

A method for context-based adaptive QRS clustering in real-time

Continuous follow-up of heart condition through long-term electrocardiogram monitoring is an invaluable tool for diagnosing some cardiac arrhythmias. In such context, providing tools for fast locating alterations of normal conduction patterns is mandatory and still remains an open issue. This work presents a real-time method for adaptive clustering QRS complexes from multilead ECG signals that provides the set of QRS morphologies that appear during an ECG recording. The method processes the QRS complexes sequentially, grouping them into a dynamic set of clusters based on the information content of the temporal context. The clusters are represented by templates which evolve over time and adapt to the QRS morphology changes. Rules to create, merge and remove clusters are defined along with techniques for noise detection in order to avoid their proliferation. To cope with beat misalignment, Derivative Dynamic Time Warping is used. The proposed method has been validated against the MIT-BIH Arrhythmia Database and the AHA ECG Database showing a global purity of 98.56% and 99.56%, respectively. Results show that our proposal not only provides better results than previous offline solutions but also fulfills real-time requirements.Comment: 12 pages, 6 figure

Mechanism and Prediction of Post-Operative Atrial Fibrillation Based on Atrial Electrograms

La fibrillation auriculaire (FA) est une arythmie touchant les oreillettes. En FA, la contraction auriculaire est rapide et irrégulière. Le remplissage des ventricules devient incomplet, ce qui réduit le débit cardiaque. La FA peut entraîner des palpitations, des évanouissements, des douleurs thoraciques ou l’insuffisance cardiaque. Elle augmente aussi le risque d'accident vasculaire. Le pontage coronarien est une intervention chirurgicale réalisée pour restaurer le flux sanguin dans les cas de maladie coronarienne sévère. 10% à 65% des patients qui n'ont jamais subi de FA, en sont victime le plus souvent lors du deuxième ou troisième jour postopératoire. La FA est particulièrement fréquente après une chirurgie de la valve mitrale, survenant alors dans environ 64% des patients. L'apparition de la FA postopératoire est associée à une augmentation de la morbidité, de la durée et des coûts d'hospitalisation. Les mécanismes responsables de la FA postopératoire ne sont pas bien compris. L'identification des patients à haut risque de FA après un pontage coronarien serait utile pour sa prévention. Le présent projet est basé sur l'analyse d’électrogrammes cardiaques enregistrées chez les patients après pontage un aorte-coronaire. Le premier objectif de la recherche est d'étudier si les enregistrements affichent des changements typiques avant l'apparition de la FA. Le deuxième objectif est d'identifier des facteurs prédictifs permettant d’identifier les patients qui vont développer une FA. Les enregistrements ont été réalisés par l'équipe du Dr Pierre Pagé sur 137 patients traités par pontage coronarien. Trois électrodes unipolaires ont été suturées sur l'épicarde des oreillettes pour enregistrer en continu pendant les 4 premiers jours postopératoires. La première tâche était de développer un algorithme pour détecter et distinguer les activations auriculaires et ventriculaires sur chaque canal, et pour combiner les activations des trois canaux appartenant à un même événement cardiaque. L'algorithme a été développé et optimisé sur un premier ensemble de marqueurs, et sa performance évaluée sur un second ensemble. Un logiciel de validation a été développé pour préparer ces deux ensembles et pour corriger les détections sur tous les enregistrements qui ont été utilisés plus tard dans les analyses. Il a été complété par des outils pour former, étiqueter et valider les battements sinusaux normaux, les activations auriculaires et ventriculaires prématurées (PAA, PVA), ainsi que les épisodes d'arythmie. Les données cliniques préopératoires ont ensuite été analysées pour établir le risque préopératoire de FA. L’âge, le niveau de créatinine sérique et un diagnostic d'infarctus du myocarde se sont révélés être les plus importants facteurs de prédiction. Bien que le niveau du risque préopératoire puisse dans une certaine mesure prédire qui développera la FA, il n'était pas corrélé avec le temps de l'apparition de la FA postopératoire. Pour l'ensemble des patients ayant eu au moins un épisode de FA d’une durée de 10 minutes ou plus, les deux heures précédant la première FA prolongée ont été analysées. Cette première FA prolongée était toujours déclenchée par un PAA dont l’origine était le plus souvent sur l'oreillette gauche. Cependant, au cours des deux heures pré-FA, la distribution des PAA et de la fraction de ceux-ci provenant de l'oreillette gauche était large et inhomogène parmi les patients. Le nombre de PAA, la durée des arythmies transitoires, le rythme cardiaque sinusal, la portion basse fréquence de la variabilité du rythme cardiaque (LF portion) montraient des changements significatifs dans la dernière heure avant le début de la FA. La dernière étape consistait à comparer les patients avec et sans FA prolongée pour trouver des facteurs permettant de discriminer les deux groupes. Cinq types de modèles de régression logistique ont été comparés. Ils avaient une sensibilité, une spécificité et une courbe opérateur-receveur similaires, et tous avaient un niveau de prédiction des patients sans FA très faible. Une méthode de moyenne glissante a été proposée pour améliorer la discrimination, surtout pour les patients sans FA. Deux modèles ont été retenus, sélectionnés sur les critères de robustesse, de précision, et d’applicabilité. Autour 70% patients sans FA et 75% de patients avec FA ont été correctement identifiés dans la dernière heure avant la FA. Le taux de PAA, la fraction des PAA initiés dans l'oreillette gauche, le pNN50, le temps de conduction auriculo-ventriculaire, et la corrélation entre ce dernier et le rythme cardiaque étaient les variables de prédiction communes à ces deux modèles.Atrial fibrillation (AF) is an abnormal heart rhythm (cardiac arrhythmia). In AF, the atrial contraction is rapid and irregular, and the filling of the ventricles becomes incomplete, leading to reduce cardiac output. Atrial fibrillation may result in symptoms of palpitations, fainting, chest pain, or even heart failure. AF is an also an important risk factor for stroke. Coronary artery bypass graft surgery (CABG) is a surgical procedure to restore the perfusion of the cardiac tissue in case of severe coronary heart disease. 10% to 65% of patients who never had a history of AF develop AF on the second or third post CABG surgery day. The occurrence of postoperative AF is associated with worse morbidity and longer and more expensive intensive-care hospitalization. The fundamental mechanism responsible of AF, especially for post-surgery patients, is not well understood. Identification of patients at high risk of AF after CABG would be helpful in prevention of postoperative AF. The present project is based on the analysis of cardiac electrograms recorded in patients after CABG surgery. The first aim of the research is to investigate whether the recordings display typical changes prior to the onset of AF. A second aim is to identify predictors that can discriminate the patients that will develop AF. Recordings were made by the team of Dr. Pierre Pagé on 137 patients treated with CABG surgery. Three unipolar electrodes were sutured on the epicardium of the atria to record continuously during the first 4 post-surgery days. As a first stage of the research, an automatic and unsupervised algorithm was developed to detect and distinguish atrial and ventricular activations on each channel, and join together the activation of the different channels belonging to the same cardiac event. The algorithm was developed and optimized on a training set, and its performance assessed on a test set. Validation software was developed to prepare these two sets and to correct the detections over all recordings that were later used in the analyses. It was complemented with tools to detect, label and validate normal sinus beats, atrial and ventricular premature activations (PAA, PVC) as well as episodes of arrhythmia. Pre-CABG clinical data were then analyzed to establish the preoperative risk of AF. Age, serum creatinine and prior myocardial infarct were found to be the most important predictors. While the preoperative risk score could to a certain extent predict who will develop AF, it was not correlated with the post-operative time of AF onset. Then the set of AF patients was analyzed, considering the last two hours before the onset of the first AF lasting for more than 10 minutes. This prolonged AF was found to be usually triggered by a premature atrial PAA most often originating from the left atrium. However, along the two pre-AF hours, the distribution of PAA and of the fraction of these coming from the left atrium was wide and inhomogeneous among the patients. PAA rate, duration of transient atrial arrhythmia, sinus heart rate, and low frequency portion of heart rate variability (LF portion) showed significant changes in last hour before the onset of AF. Comparing all other PAA, the triggering PAA were characterized by their prematurity, the small value of the maximum derivative of the electrogram nearest to the site of origin, as well as the presence of transient arrhythmia and increase LF portion of the sinus heart rate variation prior to the onset of the arrhythmia. The final step was to compare AF and Non-AF patients to find predictors to discriminate the two groups. Five types of logistic regression models were compared, achieving similar sensitivity, specificity, and ROC curve area, but very low prediction accuracy for Non-AF patients. A weighted moving average method was proposed to design to improve the accuracy for Non-AF patient. Two models were favoured, selected on the criteria of robustness, accuracy, and practicability. Around 70% Non-AF patients were correctly classified, and around 75% of AF patients in the last hour before AF. The PAA rate, the fraction of PAA initiated in the left atrium, pNN50, the atrio-ventricular conduction time, and the correlation between the latter and the heart rhythm were common predictors of these two models

Automated Cardiac Rhythm Diagnosis for Electrophysiological Studies, an Enhanced Classifier Approach

INTRODUCTION Heart function can be impaired by rhythm disturbances (cardiac arrhythmia), illustrated by electrocardiogram (ECG) recordings. Computerised arrhythmia diagnosis is well established for ECG’s but less for intracardiac electrophysiological (EP) testing. Accurate diagnosis is pre-requisite for delivering appropriate treatment to patients however existing algorithms misdiagnose a proportion of arrhythmias. Studies suggested artificial intelligence (AI) classifiers are accurate using ECG and intracardiac electrogram features and reviews suggested new features might augment diagnosis. This study aimed to develop an accurate cardiac rhythm diagnostic algorithm for electrophysiological (EP) studies with potential application as a generic rhythm classifier. METHOD An ethically approved prospective clinical study collected clinical history, right atrial and right ventricular intracardiac electrograms, beat-to-beat cardiac stroke volume, body motion and body temperature data during EP studies. An iterative system development life-cycle was used, including knowledge management and classifier development sub-processes. Domain expert knowledge and clinical arrhythmia diagnosis were modelled, synthesised as AI classifiers and used to classify cardiac rhythms. RESULTS Data collected from 65 patients was pre-processed into instances for classifier inputs. Decision tree, naïve Bayes, neural network, support vector machine and inference engine classifiers developed using Matlab showed good performance and were combined as a production system in a mixture-of-experts multi-classifier system. 18 different rhythms were classified, with the naïve Bayes classifier used to classify 11 rhythms, decision tree 4 rhythms, neural network and support vector machine one each, unclassified instances by the inference engine classifier and final class allocation using decision rule. Production system showed overall correct clasification rate 0.960; error 0.040; mean sensitivity 0.855; mean specificity 0.977; mean κ 0.767; mean positive predictive value 0.792; mean negative predictive value 0.975; mean Pearson’s phi 0.787, with P 0.9 for sinus node dysfunction and atrio-ventricular nodal/ junctional tachycardias. Temperature, accelerometry and QT interval were assessed as features by a comparison of algorithm performances with each feature removed and found not to affect classification performance. An evaluation showed 10 beat analysis performed better than 5 beat analysis. CONCLUSIONS Modelling of the clinical diagnosis process produced an AI based mixture-of-experts multi-classifier system, which accurately diagnosed different 18 cardiac rhythms. The naïve Bayes classifier performed best and classified 11 rhythms. Features for clinical symptoms and predisposing factors, atrial electrogram morphology and changes in stroke volume were found to influence rhythm classification. High performances encourage further development and potential future improvements include: a larger sample dataset; inclusion of His and coronary sinus electrograms; data mining for unknown features with significant influence on diagnosis; binary classification. The aim to classify rhythm using artificial intelligence suitable for use during EP studies was satisfied and the research hypothesis that it outperformed current algorithms was accepted. The system was likely to be able to accept updates but needs conversion as a precursor to use in a live clinical environment

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Implementação de um sistema de análise automática do ECG para identificação de episódios de fibrilação atrial utilizando uma plataforma de aquisição BITalino® e um smartphone Android™

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáAs arritmias cardíacas são distúrbios que afetam a frequência e/ou o ritmo dos batimentos cardíacos. O diagnóstico da maioria das arritmias é feito através da análise do eletrocardiograma (ECG), o qual consiste na representação gráfica da atividade elétrica do coração. A fibrilação atrial (AF) é um tipo de arritmia cardíaca, sendo a mais presente na população mundial. Se não identificada nos estágios iniciais, aumenta as chances de ocorrência de paragens cardíacas e acidente vascular cerebral, que constituem uma das maiores causas de morte no mundo. Uma das principais características presentes no sinal de ECG de indivíduos com AF é a irregularidade no ritmo cardíaco, ou seja, variação no intervalo entre dois picos R consecutivos. Pelo fato da AF muitas vezes se apresentar de forma assintomática, o uso de sistemas computacionais para a análise automática do sinal de ECG se apresenta como uma alternativa interessante para auxiliar o profissional de saúde no diagnóstico dessa arritmia. Nesse contexto, o presente trabalho trata da implementação de um sistema de análise automática do sinal de ECG para identificação de episódios de AF. O sistema consiste em uma etapa de aquisição do sinal realizada por um sensor de ECG BITalino conectado à plataforma BITalino (r)evolution Core, ambos desenvolvidos pela PLUX – Wireless Biosignals S.A. O sinal adquirido é transmitido via comunicação bluetooth para um smartphone com sistema operacional Android™. O processamento do sinal é feito através de um aplicativo desenvolvido através da IDE Android™ Studio. O sistema de análise foi desenvolvido através do software MATLAB® e, posteriormente, implementado no aplicativo com o auxílio da aplicação MATLAB Coder™ e da interface JNI. Em linhas gerais, o sistema de análise é composto por um algoritmo para detecção dos picos da onda R do sinal de ECG, seguido de uma etapa de extração de características, e outra de classificação. A característica utilizada na entrada do modelo de classificação foi o intervalo entre picos R consecutivos. O modelo de classificação utilizado é baseado em redes neurais do tipo LSTM (Long Short-Term Memory). Quando validado sobre os sinais do banco de dados MIT-BIH Atrial Fibrillation, o algoritmo de detecção dos picos da onda R apresentou valores médios de sensibilidade (Se) e preditividade positiva (P+) de 98,99% e 95,95%, respectivamente. O modelo de classificação utilizado apresentou exatidão média de 94,94% na identificação de episódios de AF.Cardiac arrhythmias are disorders that affect the rate and/or rhythm of the heartbeats. The diagnosis of most arrhythmias is made through the analysis of the electrocardiogram (ECG), which consists of a graphic representation of the electrical activity of the heart. Atrial fibrillation (AF) is a type of cardiac arrhythmia, being the most present in the world population. If not identified in the early stages, it increases the chances of cardiac arrest and stroke, which are one of the biggest causes of death in the world. One of the main characteristics present in the ECG signal of individuals with AF is the irregularity in the cardiac rhythm, that is, variation in the interval between two consecutive R peaks. Since AF is often asymptomatic, the use of computer systems for the automatic analysis of the ECG signal is an interesting alternative to assist health professionals in diagnosing this arrhythmia. In this context, this work deals with the implementation of an automatic ECG signal analysis system to identify AF episodes. The system consists of a signal acquisition step performed by a BITalino ECG sensor connected to the BITalino (r)evolution Core platform, both developed by PLUX – Wireless Biosignals SA. The acquired signal is transmitted via bluetooth communication to a smartphone with Android™ operating system. The signal processing is done through an application developed using the IDE Android™ Studio. The analysis system was developed using the MATLAB® software and later implemented in the application with the help of the MATLAB Coder™ application and the JNI interface. In general terms, the analysis system is composed of an algorithm for detecting the peaks of the R wave of the ECG signal, followed by a feature extraction step, and a classification step. The feature used in the entry of the classification model was the interval between consecutive R peaks (RRi). The classification model used is based on a LSTM neural network. When validated over the signals from the MIT-BIH Atrial Fibrillation database, the R-wave peak detection algorithm showed mean values of sensitivity (Se) and positive predictivity (P+) of 98.99% and 95.95%, respectively. The classification model used had an average accuracy of 94.94% in identifying AF episodes

Intelligent monitoring and interpretation of preterm physiological signals using machine learning

Every year, more than one in ten babies are born prematurely. In Ireland of the 70000 babies delivered every year, 4500 are born too early. Premature babies are at a higher risk of complications, which may lead to both short-term and long-term adverse health outcomes. The neonatal population is especially vulnerable and a delay in the identification of medical conditions, as well as delays in the initiating the correct treatment, may be fatal. After birth, preterms are admitted to the neonatal intensive care unit (NICU), where a continuous flow of information in the form of physiological signals is available. Physiological signals can assist clinicians in decision making related to the diagnosis and treatment of various diseases. This information, however, can be highly complex, and usually requires expert analysis which may not be available at all times. The work conducted in this thesis develops a decision support systems for the intelligent monitoring of preterms in the NICU. This will allow for an accurate estimation of the current health status of the preterm neonate as well as the prediction of possible long-term complications. This thesis is comprised of three main work packages (WP), each addressing health complication of preterm on three different stages of life. At the first 12 hours of life the health status is quantified using the clinical risk index for babies (CRIB). This is followed by the assessment of the preterm’s well-being at discharge from the NICU using the clinical course score (CCS). Finally, the long-term neurodevelopmental follow-up is assessed using the Bayley III scales of development at two years. This is schematically represented in Figure 1 along with the main findings and contributions. Low blood pressure (BP) or hypotension is a recognised problem in preterm infants particularly during the first 72 hours of life. Hypotension may cause decreased cerebral perfusion, resulting in deprived oxygen delivery to the brain. Deciding when and whether to treat hypotension relies on our understanding of the relation between BP, oxygenation and brain activity. The electroencephalogram (EEG) is the most commonly used technology to assess the ‘brain health’ of a newborn. The first WP investigates the relationship between short-term dynamics in BP and EEG energy in the preterm on a large dataset of continuous multi-channel unedited EEG recordings in the context of the health status measured by the CRIB score. The obtained results indicate that a higher risk of mortality for the preterm is associated with a lower level of nonlinear interaction between EEG and BP. The level of coupling between these two systems can potentially serve as an additional source of information when deciding whether or not to intervene in the preterm. The electrocardiogram (ECG) is also routinely recorded in preterm infants. Analysis of heart rate variability (HRV) provides a non-invasive assessment of both the sympathetic and parasympathetic control of the heart rate. A novel automated objective decision support tool for the prediction of the short-term outcome (CCS) in preterm neonates who may have low BP is proposed in the second WP. Combining multiple HRV features extracted during hypotensive episodes, the classifier achieved an AUC of 0.97 for the task of short-term outcome prediction, using a leave-one-patient-out performance assessment. The developed system is based on the boosted decision tree classifier and allows for the continuous monitoring of the preterm. The proposed system is validated on a large clinically collected dataset of multimodal recordings from preterm neonates. If the correct treatment is initiated promptly after diagnosis, it can potentially improve the neurodevelopmental outcome of the preterm infant. The third WP presents a pilot study investigating the predictive capability of the early EEG recorded at discharge from the NICU with respect to the 2-year neurodevelopmental outcome using machine learning techniques. Two methods are used: 1) classical feature-based classifier, and 2) end-to-end deep learning. This is a fundamental study in this area, especially in the context of applying end-to-end learning to the preterm EEG for the problem of long-term outcome prediction. It is shown that for the available labelled dataset of 37 preterm neonates, the classical feature-based approach outperformed the end-to-end deep learning technique. A discussion of the obtained result as well as a section highlighting the possible limitations and areas that need to be investigated in the future are provided

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