Removal of pacing spikes from the electrocardiographic signal

Cílem této práce je detekce stimulačních hrotů v záznamu ultra vysokofrekvenčního EKG za účelem následného odstranění stimulačních hrotů a umožnění vyhodnocení vyšších frekvenčních složek komplexu QRS. Toto vyhodnocení je nemožné při přítomnosti stimulačních hrotů. Zvolený problém je vyřešen pomocí heuristického algoritmu, který využívá proložení signálu přímkou v oblasti, která není ovlivněna stimulačním impulzem. Následně dochází k prodloužení této přímky a pomocí rozdílů mezi přímkou a signálem, případně pomocí dalších pravidel, jsou detekovány okraje stimulačního hrotu. Samotný vrchol hrotu je detekován prahováním obálky první diference originálního signálu. V práci jsou testovány i další algoritmy. Je zde též navrženo několik metod odstranění stimulačního hrotu. Práce zmiňuje i tvorbu obálek vysokofrekvenčních složek, na základě jejichž analýzy jsou porovnány navržené metody odstranění stimulačních hrotů a dále je také vyhodnocena úspěšnost detekce.The goal of this thesis is to detect pacing pulses in ultra high-frequency ECG so as to remove these pacing pulses. It makes evaluation of higher frequency components of QRS complex possible. This evaluation is impossible while pacing pulses are present. Chosen issue is solved using heuristic algorithm. Algorithm uses spacing of signal by line in the area which is not influenced by pacing pulses. Subsequently this line is made longer and using differences between line and signal (or another rules) edges of pacing pulses are detected. The top of the stimulation tip is detected by thresholding envelope of original signal´s first difference. More algorithms are tested in this thesis. Several methods of removing pacing pulses are suggested in thesis. Envelopes of high-frequency components are created. Envelopes are analyzed subsequently and suggested methods of removing pacing pulses are compared on the basis of these analysis. Finally the detection efficiency is evaluated.

ECG signal classification based on SVM

Cardiovascular diseases nowadays represent the most common cause of death in Western countries. Long-term ECG recording is modern method, because it allows to detect sporadically occurring pathology. We designed an automatic classifier to detect five pathologies (AAMI standard) by SVM method. The classifier was tested on the entire MIT-BIH Arrhythmia Database with an accuracy of 99.17 %. We also compared the quality of parameters entering the classifier

Heart beat representation for classification

Výběr úseku EKG hraje významnou roli při návrhu klasifikátoru srdečních cyklů. Typ vybraných úseků ovlivňuje klasifikaci nejen z hlediska typu a maximálního počtu rozpoznávaných patologických skupin, ale ovlivňuje také složitost klasifikačního modelu, a tudíž nepřímo udává nároky na paměť používané výpočetní techniky a čas potřebný pro klasifikaci. Tato práce je zaměřena právě na porovnání úspěšnosti klasifikace cyklů EKG při různých vstupních úsecích. Byly použity vstupní úseky QRST, RST, ST-T, QRS a T. Signál EKG byl získán z izolovaných králičích srdcí a rozdělen na typy podle maximální výchylky vlny T a změn ST úseku. Signál dále vstupuje do umělé neuronové sítě, kde je klasifikován na předdefinované typy. Byla použita síť s dvaceti čtyřmi neurony ve skryté a jedním neuronem ve výstupní vrstvě. Úspěšnost klasifikace je shrnuta v závěru práce.Selection of ECG segment plays a significant role in design of a heart beat classifier. The type of selected segments influences the classification not only in regard to the type and maximum number of recognized pathological groups but also in regard to the complexity of classification model, which consequently creates indirect demands on the memory of the computer technology used as well as on the time needed for the classification. The thesis is focused on the comparison of success rates of the ECG heart beat classifications in different input segments. The input segments used were QRST, RST, ST-T, QRS, and T. The ECG signal was obtained from isolated rabbit hearts and divided into individual types according to the T-wave amplitude and changes in the ST segment. The signal subsequently enters the artificial neural network where it is classified into predefined types. The network used had twenty-four neurons in the first layer and one neuron in the second layer. Efficiency of the classification is in the conclusion of this thesis.

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 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

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

Comparing Ventricular Synchrony in Left Bundle Branch and Left Ventricular Septal Pacing in Pacemaker Patients

Background: Left bundle branch area pacing (LBBAP) has recently been introduced as a novel physiological pacing strategy. Within LBBAP, distinction is made between left bundle branch pacing (LBBP) and left ventricular septal pacing (LVSP, no left bundle capture). Objective: To investigate acute electrophysiological effects of LBBP and LVSP as compared to intrinsic ventricular conduction. Methods: Fifty patients with normal cardiac function and pacemaker indication for bradycardia underwent LBBAP. Electrocardiography (ECG) characteristics were evaluated during pacing at various depths within the septum: starting at the right ventricular (RV) side of the septum: the last position with QS morphology, the first position with r' morphology, LVSP and-in patients where left bundle branch (LBB) capture was achieved-LBBP. From the ECG's QRS duration and QRS morphology in lead V1, the stimulus- left ventricular activation time left ventricular activation time (LVAT) interval were measured. After conversion of the ECG into vectorcardiogram (VCG) (Kors conversion matrix), QRS area and QRS vector in transverse plane (Azimuth) were determined. Results: QRS area significantly decreased from 82 +/- 29 mu Vs during RV septal pacing (RVSP) to 46 +/- 12 mu Vs during LVSP. In the subgroup where LBB capture was achieved (n = 31), QRS area significantly decreased from 46 +/- 17 mu Vs during LVSP to 38 +/- 15 mu Vs during LBBP, while LVAT was not significantly different between LVSP and LBBP. In patients with normal ventricular activation and narrow QRS, QRS area during LBBP was not significantly different from that during intrinsic activation (37 +/- 16 vs. 35 +/- 19 mu Vs, respectively). The Azimuth significantly changed from RVSP (-46 +/- 33 degrees) to LVSP (19 +/- 16 degrees) and LBBP (-22 +/- 14 degrees). The Azimuth during both LVSP and LBBP were not significantly different from normal ventricular activation. QRS area and LVAT correlated moderately (Spearman's R = 0.58). Conclusions: ECG and VCG indices demonstrate that both LVSP and LBBP improve ventricular dyssynchrony considerably as compared to RVSP, to values close to normal ventricular activation. LBBP seems to result in a small, but significant, improvement in ventricular synchrony as compared to LVSP