Analysis of experimental ECG

Tato práce se zabývá analýzou experimentálních elektrogramů získaných z izolovaných králičích srdcí. V teoretické části práce jsou popsány principy elektrokardiografie, projevy patologické srdeční činnosti v EKG záznamu, často používané metody automatické klasifikace EKG cyklů a také informace o experimentálním výzkumu. První část praktické práce se zabývá manuální klasifikací elektrogramů a jednotlivých patologických událostí v nich zaznamenaných. Výsledky klasifikace budou použity ve veřejně dostupné databázi experimentálních elektrogramů, která nyní vzniká na UBMI VUT v Brně. Klasifikace záznamů byla konzultována s odborníky. Dále je popsán výskyt patologií v průběhu fází experimentů a dle toho zhodnocen vliv opakované ischemie na jejich vznik. Nakonec je realizována automatická klasifikace čtyř typů patologických cyklů čtyřmi klasifikačními metodami (diskriminační analýza, naivní Bayesův klasifikátor, metoda podpůrných vektorů a metoda k-nejbližších sousedů). Pro reprezentaci cyklů při klasifikaci jsou použity morfologické parametry. Celkem je z každého cyklu odvozeno 71 morfologických parametrů. Z nich jsou za pomoci testů Kruskal-Wallis a Tukey-Kramer a také analýzy hlavních komponent určeny ty, které dokáží cykly reprezentovat nejlépe.This diploma thesis deals with the analysis of experimental electrograms (EG) recorded from isolated rabbit hearts. The theoretical part is focused on the basic principles of electrocardiography, pathological events in ECGs, automatic classification of ECG and experimental cardiological research. The practical part deals with manual classification of individual pathological events – these results will be presented in the database of EG records, which is under developing at the Department of Biomedical Engineering at BUT nowadays. Manual scoring of data was discussed with experts. After that, the presence of pathological events within particular experimental periods was described and influence of ischemia on heart electrical activity was reviewed. In the last part, morphological parameters calculated from EG beats were statistically analised with Kruskal-Wallis and Tukey-Kramer tests and also principal component analysis (PCA) and used as classification features to classify automatically four types of the beats. Classification was realized with four approaches such as discriminant function analysis, k-Nearest Neighbours, support vector machines, and naive Bayes classifier.

Single-Feature Method for Fast Atrial Fibrillation Detection in ECG Signals

Atrial fibrillation (AF) is the most common arrhthmia in adults and is associated with higher risk of heart failure or death. Here, we introduce simple and efficient method for automatic AF detection based on symbolic dynamics and Shannon entropy. This method comprises of three parts. Firstly, QRS complex detection is provided, than the raw RR sequence is transformed into a sequence of specific symbols and subsequently into a word sequence and finally, Shannon entropy of the word sequence is calculated. According to the value of Shannon entropy, it is decided, whether AF is present in the current cardiac beat. We achieved sensitivity Se=96.32% and specificity Sp=98.61 on MIT-BIH Atrial Fibrillation database, Se=91.30% and Sp=90.80% on MIT-BIH Arrhythmia database, Se=95.6% and Sp=80.27% for CinC Challenge database 2020. The achieved results of our one-feature method are comparable with other authors of more complicated and computationally expensive methods

A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression

The assessment of ECG signal quality after compression is an essential part of the compression process. Compression facilitates the signal archiving, speeds up signal transmission, and reduces the energy consumption. Conversely, lossy compression distorts the signals. Therefore, it is necessary to express the compression performance through both compression efficiency and signal quality. This paper provides an overview of objective algorithms for the assessment of both ECG signal quality after compression and compression efficiency. In this area, there is a lack of standardization, and there is no extensive review as such. 40 methods were tested in terms of their suitability for quality assessment. For this purpose, the whole CSE database was used. The tested signals were compressed using an algorithm based on SPIHT with varying efficiency. As a reference, compressed signals were manually assessed by two experts and classified into three quality groups. Owing to the experts’ classification, we determined corresponding ranges of selected quality evaluation methods’ values. The suitability of the methods for quality assessment was evaluated based on five criteria. For the assessment of ECG signal quality after compression, we recommend to use a combination of these methods: PSim SDNN, QS, SNR1, MSE, PRDN1, MAX, STDERR, and WEDD SWT

Robust QRS Detection Using Combination of Three Independent Methods

QRS detection is a fundamental step in ECG analysis. Although there are many algorithms reporting results close to 100%, this problem is still not resolved. The reported numbers are influenced by the quality of the detector, the quality of annotations and also by the chosen method of testing. In this study, we proposed and properly tested robust QRS detection algorithm based on a combination of three independent principles. For enhancement of QRS complexes there were developed three independent approaches based on continuous wavelet transform, Stockwell transform and phasor transform which are followed by individual adaptive thresholding. Each method produces candidates for QRS complexes which are further processed by cluster analysis resulting in final QRS positions. The proposed detection algorithm was tested on three complete standard ECG databases: MIT-BIH Arrhythmia Database, European ST-T Database and QT Database without any change in algorithm setting. We utilized complete data from mentioned databases including all provided leads and used original (not adjusted) reference positions of QRS complexes. Summarized detection accuracy for all three databases was expressed by sensitivity 99.16% and positive predictive value 98.99%

Cardiac Pathologies Detection and Classification in 12-lead ECG

Background: Automatic detection and classification of cardiac abnormalities in ECG is one of the basic and often solved problems. The aim of this paper is to present a proposed algorithm for ECG classification into 19 classes. This algorithm was created within PhysioNet/CinC Challenge 2020, name of our team was HITTING. Methods: Our algorithm detects each pathology separately according to the extracted features and created rules. Signals from the 6 databases were used. Detector of QRS complexes, T-waves and P-waves including detection of their boundaries was designed. Then, the most common morphology of the QRS was found in each record. All these QRS were averaged. Features were extracted from the averaged QRS and from intervals between detected points. Appropriate features and rules were set using classification trees. Results: Our approach achieved a challenge validation score of 0.435, and full test score of 0.354, placing us 11 out of 41 in the official ranking. Conclusion: The advantage of our algorithm is easy interpretation. It is obvious according to which features algorithm decided and what thresholds were set

Rules for Determination of Expected P Wave Type in ECG Signals

The aim of this work is to improve existing possibilities of automatic detection of pathological P wave in electrocardiogram. For detection of different types of P wave is crucial to choose suitable algorithm. In this paper, establishing of rules for determining which P wave type is in given situation probably present is done. This rules are derived from positions of QRS complexes. Therefore, accurate QRS detector based on a signal filtering by bandpass filter and finding the maximum in a moving window is designed

Use of higher-order cumulants for ECG analysis

This work deals with using higher order cumulants for analysis ECG. In the first part of work is described principle of electrocardiography, followed by matemathical derivation of higher order cumulants, description of their properties and their use in current practice. In the next part of paper is described caltulation higher order statistic of ECG beats in Matlab programming environment. In the practical part of work are tested predicted properties. Distinctive properties are minimalization of amplitude and time shift and Gaussian noise. This properties of higher order cumulants enable lesser variance of beats in one class and easier clasification ECG. Calculation of cumulants from real ECG beats of various groups is then realized. Classification based on the original ECG cycles and cumulants is performed using artificial neural network. Results of these classification approaches are then compared and discussed

Selected Items from a Firm´s Accounting Statements and their Analysis

Diplomová práce je rozdělena na teoretickou a prakticou část. Teoretická část práce je členěna na čtyři podkapitoly, které se postupně zabývají jednotlivými směry v účetnictví, účetními výkazy, oceňováním v účetnictví a poslední kapitola této části se zabývá pohledávkami a závazky. V praktické části jsou pak analyzovány pohledávky a závazky konkrétní společnosti. Dále je rozebrána problematika kurzových rozdílů a jejich vliv na výsledek hospodaření