Patient Specific Congestive Heart Failure Detection From Raw ECG signal

In this study; in order to diagnose congestive heart failure (CHF) patients, non-linear second-order difference plot (SODP) obtained from raw 256 Hz sampled frequency and windowed record with different time of ECG records are used. All of the data rows are labelled with their belongings to classify much more realistically. SODPs are divided into different radius of quadrant regions and numbers of the points fall in the quadrants are computed in order to extract feature vectors. Fisher's linear discriminant, Naive Bayes, Radial basis function, and artificial neural network are used as classifier. The results are considered in two step validation methods as general k-fold cross-validation and patient based cross-validation. As a result, it is shown that using neural network classifier with features obtained from SODP, the constructed system could distinguish normal and CHF patients with 100% accuracy rate. KeywordsComment: Congestive heart failure, ECG, Second-Order Difference Plot, classification, patient based cross-validatio

BEAT CLASSIFICATION USING HYBRID WAVELET TRANSFORM BASED FEATURES AND SUPERVISED LEARNING APPROACH

This paper describes an automatic heartbeat recognition based on QRS detection, feature extraction and classification. In this paper five different type of ECG beats of MIT BIH arrhythmia database are automatically classified. The proposed method involves QRS complex detection based on the differences and approximation derivation, inversion and threshold method. The computation of combined Discrete Wavelet Transform (DWT) and Dual Tree Complex Wavelet Transform (DTCWT) of hybrid features coefficients are obtained from the QRS segmented beat from ECG signal which are then used as a feature vector. Then the feature vectors are given to Extreme Learning Machine (ELM) and k- Nearest Neighbor (kNN) classifier for automatic classification of heartbeat. The performance of the proposed system is measured by sensitivity, specificity and accuracy measures

ECG-Based Arrhythmia Classification using Recurrent Neural Networks in Embedded Systems

Cardiac arrhythmia is one of the most important cardiovascular diseases (CVDs), causing million deaths every year. Moreover it is difficult to diagnose because it occurs intermittently and as such requires the analysis of large amount of data, collected during the daily life of patients. An important tool for CVD diagnosis is the analysis of electrocardiogram (ECG), because of its non-invasive nature and simplicity of acquisition. In this work we propose a classification algorithm for arrhythmia based on recurrent neural networks (RNNs) that operate directly on ECG data, exploring the effectiveness and efficiency of several variations of the general RNN, in particular using different types of layers implementing the network memory. We use the MIT-BIH arrhythmia database and the evaluation protocol recommended by the Association for the Advancement of Medical Instrumentation (AAMI). After designing and testing the effectiveness of the different networks, we then test its porting to an embedded platform, namely the STM32 microcontroller architecture from ST, using a specific framework to port a pre-built RNN to the embedded hardware, convert it to optimized code for the platform and evaluate its performance in terms of resource usage. Both in binary and multiclass classification, the basic RNN model outperforms the other architectures in terms of memory storage (∼117 KB), number of parameters (∼5 k) and inference time (∼150 ms), while the RNN LSTM-based achieved the best accuracy (∼90%)

Machine learning and soft computing approaches to microarray differential expression analysis and feature selection.

Differential expression analysis and feature selection is central to gene expression microarray data analysis. Standard approaches are flawed with the arbitrary assignment of cut-off parameters and the inability to adapt to the particular data set under analysis. Presented in this thesis are three novel approaches to microarray data feature selection and differential expression analysis based on various machine learning and soft computing paradigms. The first approach uses a Separability Index to select ranked genes, making gene selection less arbitrary and more data intrinsic. The second approach is a novel gene ranking system, the Fuzzy Gene Filter, which provides a more holistic and adaptive approach to ranking genes. The third approach is based on a Stochastic Search paradigm and uses the Population Based Incremental Learning algorithm to identify an optimal gene set with maximum inter-class distinction. All three approaches were implemented and tested on a number of data sets and the results compared to those of standard approaches. The Separability Index approach attained a K-Nearest Neighbour classification accuracy of 92%, outperforming the standard approach which attained an accuracy of 89.6%. The gene list identified also displayed significant functional enrichment. The Fuzzy Gene Filter also outperformed standard approaches, attaining significantly higher accuracies for all of the classifiers tested, on both data sets (p < 0.0231 for the prostate data set and p < 0.1888 for the lymphoma data set). Population Based Incremental Learning outperformed Genetic Algorithm, identifying a maximum Separability Index of 97.04% (as opposed to 96.39%). Future developments include incorporating biological knowledge when ranking genes using the Fuzzy Gene Filter as well as incorporating a functional enrichment assessment in the fitness function of the Population Based Incremental Learning algorithm

ECG Classification with an Adaptive Neuro-Fuzzy Inference System

Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificial neural network (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning

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

Mitochondrial Morphology Dynamics during Apoptosis - An integrative modeling approach

Mitochondria are central to many important cellular functions. The entire mitochondrial population is in constant flux, driven by continuous fusion and division of mitochondria. Defects in mitochondrial dynamics can cause deficits in mitochondrial respiration, morphology and motility leading to apoptosis under extreme conditions. An important and still unresolved question is how the heterogeneity of mitochondrial morphology, distribution and function are mechanistically realized in the cell. Importantly, to what extent is mitochondrial morphology dependent or affects cell fate decisions. Despite the intense focus on unraveling connections between mitochondrial morphology and severe human pathologies, the analysis and systematic description of mitochondrial phenotypes remains an open challenge. Current approaches to study mitochondrial morphology are limited by low data sampling coupled to manual identification and simplistic classification of complex morphological phenotypes. The overall goal of this work was to elucidate the nature of the relationship between mitochondria morphology and apoptotic events. Diverse apoptotic triggers were systematically tested and data concerning mitochondrial phenotypes and injury was collected to infer cause and consequence relationships. Therefore, high-resolution fluorescence imaging was employed to extract high-content information essential to identify and quantify spatial and conformational events in the single cell. These included monitoring of mitochondrial membrane permeability and quantification of Bax activation under matched conditions to assess mitochondrial stress. Experimentally, mitochondrial morphological transitions were followed in human breast carcinoma MCF-7 cells by tagging a mitochondrial inner membrane protein with a fluorescent probe. We made use of apoptotic conditions that have been previously reported to cause mitochondrial fragmentation or swelling. Wide field microscopy allowed for the collection of images containing cells with mostly networked, fragmented or swollen mitochondria. Next, image analysis was performed to extract several mitochondrial features that better characterize each class. These were grouped and used to build a decision tree-based classifier that automatically classifies individual mitochondria into the correspondent phenotypic class. Our population-based classifier accounts for intracellular sub-classes, intermediate mitochondrial stages and reproduces intercellular variances with high accuracy. Our results show that distinct apoptotic stimuli lead to subtle but significant differences in mitochondrial morphology within cell population that can be specific to a particular insult. Interestingly, there was no direct relation between the induced-mitochondrial classes and the analyzed apoptotic steps. In fact, some apoptotic drugs, which are known to cause similar mitochondrial damage, showed distinct mitochondrial morphology. Therefore, the observed heterogeneous response of mitochondria to stress strongly suggests that more complex, non-linear interactions exist. Here, we propose an integrated mechanistic and data-driven modeling approach to analyze heterogeneously quantified datasets and infer hierarchical interactions between mitochondrial morphology and apoptotic events. Our modeling results suggest that Bax activation leads to mitochondrial fragmentation, which is strongly associated with mitochondrial membrane depolarization events. In turn, the loss of mitochondrial membrane potential is closely related to mitochondrial swelling. Our model predictions are in accordance with previous published results and thereby validate our modeling approach that can now be easily extended to include new datasets. Surprisingly, mitochondrial fragmentation was not the most prominent phenotype, even under conditions where Bax activation was considerably high. Instead, swollen-mitochondria seem to be closer related to mitochondrial-associated death pathways. Next steps include the extension of our pipeline in a time-resolved manner and combined datasets acquisition in order to further investigate this hypothesis. In summary, we have established and validated a platform for mitochondrial morphological and functional analysis that offers results in an unbiased, systematic and statistically relevant manner. We believe the developed platform is suitable to be extensively used in the investigation of specific molecular targets. Possible applications include RNAi screens (e.g. morphology proteins) or extended compound libraries in a high-throughput mode. Importantly, it can now be further adjusted to other studies relevant to mitochondrial programmed cell death that will hopefully lead into the better understanding of mitochondrial role in physiology and disease progression

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

Decoding atrial fibrillation:Personalized identification and quantification of electropathology

Electrophysiological mapping-guided ablation strategies targeting atrial fibrillation (AF) have improved considerably over the past few years. However, it remains a major challenge to design effective strategies for particularly persistent AF. This can be partially explained by the inadequate understanding of the mechanisms and electropathological substrate underlying AF. Progression of AF is accompanied by structural and electrical remodeling, resulting in complex electrical conduction disorders, which is defined as electropathology. The severity of electropathology thus defines the stage of AF and is a major determinant of the effectiveness of AF therapy. In this thesis, features of electrophysiological properties of atrial tissue have been explored, developed and quantified during normal sinus rhythm, programmed electrical stimulation and AF. In addition, inter- and intra-individual variation in these quantified parameters has been examined in patients with and without prior episodes of AF. The most suitable objective parameters will aid in the identification of patients at risk for early onset or progression of AF. Part I of this thesis focusses on quantified electrogram features related to electropathology. In part II, abnormalities in wavefront propagation due to heterogeneous conduction properties were explored. Part III focusses on identification of post-operative AF and the relation with electropathology. In part IV of this thesis, some clinical implications of high-resolution mapping during cardiac surgery and application of quantified electrophysiological features are discussed