Automatic delineation of ECG signals

Tato dizertační práce se zabývá detekcí komplexů QRS a rozměřováním signálů EKG. V teoretické části práce jsou popsány základy elektrokardiografie, přístupy detekce komplexů QRS, přístupy rozměřování EKG, standardní databáze CSE a teorie vlnkové transformace. V praktické části práce jsou popsány navržené metody detekce komplexů QRS a rozměření signálů EKG. Navržené metody jsou založeny na spojité vlnkové transformaci, vhodných měřítcích, vhodné mateřské vlnce, shlukové analýze a transformaci svodů. Představené algoritmy byly otestovány na standardní databázi CSE. Dosažené výsledky jsou lepší, než přímo srovnatelné výsledky jiných metod a splňují stanovená kritéria databáze CSE. Robustnost algoritmů byla úspěšně otestována na signálech databáze CSE pozměněných kompresí a filtrací. Navržený rozměřovací algoritmus byl úspěšně využit jako nástroj pro stanovení míry diagnostického zkreslení signálů EKG pozměněných kompresí.This dissertation deals with QRS complex detection and ECG delineation. The theoretical part of the work describes basics of electrocardiography, QRS detection approaches, ECG delineation approaches, the standard CSE database and the wavelet transform theory. The practical part of the work describes designed methods of QRS complex detection and ECG delineation. The designed methods are based on a continuous wavelet transform, appropriate scales, appropriate mother wavelet, cluster analysis and leads transformation. The introduced algorithms were evaluated on the standard CSE database. The obtained results are better, than directly comparable results of other methods and accomplished given database criteria. The robustness of designed algorithms was successfully tested on CSE database signals modified by compression and filtering. The proposed ECG delineation algorithm was successfully used as a tool for evaluation of diagnostic distortion of ECG signals modified by compression.

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%

A meteorite-dropping superbolide from the catastrophically disrupted comet C1919Q2 Metcalf: a pathway for meteorites from Jupiter family comets

2 pages, 1 figure.-- Contributed to: 40th Lunar and Planetary Science Conference (The Woodlands, Texas ,Mar 23-27, 2009).It is widely accepted that cometary nuclei are composed of a mix of volatile ices and meteoritic materials. In a series of seminal papers F. L. Whipple tried to explain how the irregular internal structure of each nuclei would be able to explain the nongravitational forces, and how the continuous sublimation of the ice species would lead to explain the origin of meteoroid streams. Not essential progress was made until that the approach of a cruise of international spacecrafts to comet 1P/Halley allowed to achieve the first direct view of a cometary nucleus.Peer reviewe

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

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