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1	Potlačení driftu signálu EKG s využitím empirického rozkladu / ECG baseline wander correction based on the empirical mode decomposition Šlancar, Matěj January 2017 (has links) The aim of this thesis is to introduce with principle of Empirical Mode Decomposition method and possibility use for correction of baseline wander in ECG signals. The thesis describes the main components of the ECG signal, a selection of possible types of signal noise, its property and principles of chosen methods for filtration of ECG signals. In conclusion the evaluation of the effectiveness of the EMD method for filtering a baseline wander and it comparing with effectiveness of the linear filtration. Functionality of used algorithms has been tested on signals of CSE standard library.
2	Capacitively-Coupled, Pseudo Return-to-Zero Input, Latched-Bias Data Receiver Mathieu, Brandon Lee January 2018 (has links) No description available. Electrical Engineering Engineering Baseline Wander BiCMOS Integrated Circuits CMOS SOI Capacitive Coupling Digital Feedback Latch Pulse Reciever
3	Odstraňovaní kolísání izolinie v EKG pomocí empirické modální dekompozice / Removing baseline wander in ECG with empirical mode decomposition Procházka, Petr January 2015 (has links) In this semestral thesis, realizations of chosen linear filters for baseline wander are described. These filters are then used on artificial ECG signals from CSE database with added baseline wander. These methods are compared and results are evaluated. After that, literature search of Empirical mode decomposition method is utilized. Realization of designed filters in MATLAB programming language are described, then results are evaluated with respect to filtration success.
4	Odhad dechové frekvence z elektrokardiogramu a fotopletysmogramu / Breathing Rate Estimation from the Electrocardiogram and Photoplethysmogram Janáková, Jaroslava January 2021 (has links) The master thesis deals with the issue of gaining the respiratory rate from ECG and PPG signals, which are not only in clinical practice widely used measurable signals. The theoretical part of the work outlines the issue of obtaining a breath curve from these signals. The practical part of the work is focused on the implementation of five selected methods and their final evaluation and comparison.
5	A Fully Analog Motion Artifacts and Baseline Wander Elimination Circuit for Ambulatory ECG Recording Systems Nazari, Masoud 01 May 2023 (has links) No description available. Electrical Engineering
6	Atrial Fibrillation Detection Algorithm Evaluation and Implementation in Java / Utvärdering av algoritmer för detektion av förmaksflimmer samt implementation i Java Dizon, Lucas, Johansson, Martin January 2014 (has links) Atrial fibrillation is a common heart arrhythmia which is characterized by a missing or irregular contraction of the atria. The disease is a risk factor for other more serious diseases and the total medical costs in society are extensive. Therefore it would be beneficial to improve and optimize the prevention and detection of the disease. Pulse palpation and heart auscultation can facilitate the detection of atrial fibrillation clinically, but the diagnosis is generally confirmed by an ECG examination. Today there are several algorithms that detect atrial fibrillation by analysing an ECG. A common method is to study the heart rate variability (HRV) and by different types of statistical calculations find episodes of atrial fibrillation which deviates from normal sinus rhythm. Two algorithms for detection of atrial fibrillation have been evaluated in Matlab. One is based on the coefficient of variation and the other uses a logistic regression model. Training and testing of the algorithms were done with data from the Physionet MIT database. Several steps of signal processing were used to remove different types of noise and artefacts before the data could be used. When testing the algorithms, the CV algorithm performed with a sensitivity of 91,38%, a specificity of 93,93% and accuracy of 92,92%, and the results of the logistic regression algorithm was a sensitivity of 97,23%, specificity of 93,79% and accuracy of 95,39%. The logistic regression algorithm performed better and was chosen for implementation in Java, where it achieved a sensitivity of 97,31%, specificity of 93,47% and accuracy of 95,25%. / Förmaksflimmer är en vanlig hjärtrytmrubbning som kännetecknas av en avsaknad eller oregelbunden kontraktion av förmaken. Sjukdomen är en riskfaktor för andra allvarligare sjukdomar och de totala kostnaderna för samhället är betydande. Det skulle därför vara fördelaktigt att effektivisera och förbättra prevention samt diagnostisering av förmaksflimmer. Kliniskt diagnostiseras förmaksflimmer med hjälp av till exempel pulspalpation och auskultation av hjärtat, men diagnosen brukar fastställas med en EKG-undersökning. Det finns idag flertalet algoritmer för att detektera arytmin genom att analysera ett EKG. En av de vanligaste metoderna är att undersöka variabiliteten av hjärtrytmen (HRV) och utföra olika sorters statistiska beräkningar som kan upptäcka episoder av förmaksflimmer som avviker från en normal sinusrytm. I detta projekt har två metoder för att detektera förmaksflimmer utvärderats i Matlab, en baseras på beräkningar av variationskoefficienten och den andra använder sig av logistisk regression. EKG som kommer från databasen Physionet MIT används för att träna och testa modeller av algoritmerna. Innan EKG-signalen kan användas måste den behandlas för att ta bort olika typer av brus och artefakter. Vid test av algoritmen med variationskoefficienten blev resultatet en sensitivitet på 91,38%, en specificitet på 93,93% och en noggrannhet på 92,92%. För logistisk regression blev sensitiviteten 97,23%, specificiteten 93,79% och noggrannheten 95,39%. Algoritmen med logistisk regression presterade bättre och valdes därför för att implementeras i Java, där uppnåddes en sensitivitet på 91,31%, en specificitet på 93,47% och en noggrannhet på 95,25%. Atrial Fibrillation AF Detection algorithm AF detection Algorithm evaluation Matlab Java Electrocardiogram ECG Heart rate variability HRV Signal processing Pre processing Noise reduction Baseline wander powerline interference wavelet transform DWT R-peak detection Pan-tompkins HRV cleaning Feature selection U-test SDANN SDNN CV RMSSD PNN50 TINN LF HF Logistic regression CV algorithm Receiver operating characteristics ROC Classification Confusion matrix Leave-one-out cross validation Statgraphics Förmaksflimmer detekteringsalgoritm evaluering Matlab Java elektrokardiogram EKG Hjärtfrekvens variabilitet Signalbehandling Brusreducering Logistisk regression Variationskoefficient Klassificering Medical Engineering Medicinteknik

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