Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76

P Wave Detection in Pathological ECG Signals

Důležitou součástí hodnocení elektrokardiogramu (EKG) a následné detekce srdečních patologií, zejména v dlouhodobém monitorování, je detekce vln P. Výsledky detekce vln P umožňují získat ze záznamu EKG více informací o srdeční činnosti. Podle správně detekovaných pozic vln P je možné detekovat a odlišit patologie, které současné programy používané v medicínské praxi identifikovat neumožňují (např. atrioventrikulární blok 1., 2. a 3. stupně, cestující pacemaker, Wolffův-Parkinsonův-Whiteův syndrom). Tato dizertační práce představuje novou metodu detekce vln P v záznamech EKG během fyziologické a zejména patologické srdeční činnosti. Metoda je založena na fázorové transformaci, inovativních pravidlech detekce a identifikaci možných patologií zpřesňující detekci vln P. Dalším důležitým výsledkem práce je vytvoření dvou veřejně dostupných databází záznamů EKG s obsahem patologií a anotovanými vlnami P. Dizertační práce je rozdělena na teoretickou část a soubor publikací představující příspěvek autora v oblasti detekce vlny P.Accurate software for the P wave detection, mainly in long-term monitoring, is an important part of electrocardiogram (ECG) evaluation and subsequent cardiac pathological events detection. The results of P wave detection allow us to obtain more information from the ECG records. According to the correct P wave detection, it is possible to detect and distinguish cardiac pathologies which are nowadays automatically undetectable by commonly used software in medical practice (events e.g. atrioventricular block 1st, 2nd and 3rd degree, WPW syndrome, wandering pacemaker, etc.). This thesis introduces a new method for P wave detection in ECG signals during both physiological and pathological heart function. This novel method is based on a phasor transform, innovative rules, and identification of possible pathologies that improve P wave detection. An equally important part of the work is the creation of two publicly available databases of physiological and pathological ECG records with annotated P waves. The dissertation is divided into theoretical analysis and a set of publications representing the contribution of the author in the area of P wave detection.

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