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Novel Approaches to ECG-Based Modeling and Characterization of Atrial Fibrillation

Sandberg, Frida LU (2010)
Abstract
This thesis deals with signal processing algorithms for analysis of the electrocardiogram (ECG) during atrial fibrillation (AF). Such analysis can be used for diagnosing patients, and for monitoring and predicting their response to various treatment. The thesis comprises an introduction and five papers describing methods for ECG-based modeling and characterization of AF. Paper I--IV deal with methods for characterization of the atrial activity, whereas Paper V deals with modeling of the ventricular response, both problems with the assumption that AF is present.



In Paper I, a number of measures characterizing the atrial activity in the ECG, obtained using time-frequency analysis as well as nonlinear methods, are... (More)
This thesis deals with signal processing algorithms for analysis of the electrocardiogram (ECG) during atrial fibrillation (AF). Such analysis can be used for diagnosing patients, and for monitoring and predicting their response to various treatment. The thesis comprises an introduction and five papers describing methods for ECG-based modeling and characterization of AF. Paper I--IV deal with methods for characterization of the atrial activity, whereas Paper V deals with modeling of the ventricular response, both problems with the assumption that AF is present.



In Paper I, a number of measures characterizing the atrial activity in the ECG, obtained using time-frequency analysis as well as nonlinear methods, are evaluated for their ability to predict spontaneous termination of AF. The AF frequency, i.e, the repetition rate of the atrial fibrillatory waves of the ECG, proved to be a significant factor for discrimination between terminating and non-terminating AF.



Noise is a common problem in ECG signals, particularly in long-term ambulatory recordings.

Hence, robust algorithms for analysis and characterization are required. In Paper II, a robust method for tracking the AF frequency in noisy signals is presented. The method is based on a hidden Markov model (HMM), which takes the harmonic pattern of the atrial activity into account. Using the HMM-based method, the average RMS error of the frequency estimates at high noise levels was significantly lower compared to existing methods.



In Paper III, the HMM-based method is employed for analysis of 24-h ambulatory ECG signals in order to explore circadian variation in AF frequency. Circadian variations reflect autonomic modulation; attenuation or absence of such variations may help to diagnose patients. Methods based on curve fitting, autocorrelation, and joint variation, respectively, are employed to quantify circadian variations, showing that it is present in most patients with long-standing persistent AF, although the short-term variation is considerable.



In Paper IV, 24-h ambulatory ECG recordings with paroxysmal and persistent AF are analyzed using an entropy-based method for characterization of the atrial activity. Short segments are classified based on these measures, showing that it is feasible to distinguish between patient with paroxysmal and persistent AF from 10-s ECGs; the average classification rate was above 95%.



The ventricular response during AF is mainly determined by the AV nodal blocking of atrial impulses. In Paper V, a new model-based approach for analysis of the ventricular response during AF is proposed. The model integrates physiological properties of the AV node and the atrial fibrillatory rate; the model parameters can be estimated from ECG signals. Results show that ventricular response is sufficiently represented by the estimated model in a majority of the recordings; in 85.7% of the analyzed 30-min segments the model fit was considered accurate, and that changes of AV nodal properties caused by autonomic modulation could be tracked through the estimated model parameters.



In summary, the work within this thesis contributes with new methods for non-invasive analysis of AF, which can be used to tailor and evaluate different strategies for AF treatment. (Less)
Abstract (Swedish)
Popular Abstract in Swedish

Förmaksflimmer är den vanligaste rytmrubbningen i hjärtat som kräver behandling. Risken att drabbas ökar med åldern och 8% av alla åttioåringar lider av förmaksflimmer. Förmaksflimmer är i sig inte livshotande, men den förhöjda risken för proppbildning i förmaken kan leda till allvarliga komplikationer såsom stroke. Det finns olika behandlingsstrategier för förmaksflimmer, t.ex. medicinering, elkonvertering och kateterablation, men läkarna vet idag inte vilken metod som fungerar bäst för den enskilda patienten.

Under förmaksflimmer är den elektriska aktiviteten i förmaken snabb och oregelbunden. De bakomliggande orsakerna till detta är inte fullständigt kartlagda. Ett enkelt och ofarligt... (More)
Popular Abstract in Swedish

Förmaksflimmer är den vanligaste rytmrubbningen i hjärtat som kräver behandling. Risken att drabbas ökar med åldern och 8% av alla åttioåringar lider av förmaksflimmer. Förmaksflimmer är i sig inte livshotande, men den förhöjda risken för proppbildning i förmaken kan leda till allvarliga komplikationer såsom stroke. Det finns olika behandlingsstrategier för förmaksflimmer, t.ex. medicinering, elkonvertering och kateterablation, men läkarna vet idag inte vilken metod som fungerar bäst för den enskilda patienten.

Under förmaksflimmer är den elektriska aktiviteten i förmaken snabb och oregelbunden. De bakomliggande orsakerna till detta är inte fullständigt kartlagda. Ett enkelt och ofarligt sätt att studera den elektriska aktiviteten i hjärtat är genom elektrokardiogram (EKG), som mäts på kroppsytan.



Denna avhandling handlar om metoder för analys av EKG-signaler under förmaksflimmer. Målet är att kunna hjälpa läkare att fatta rätt beslut om behandling för den enskilda patienten.

Egenskaper i EKG-signalen kan användas för att följa spontana förändringar i hjärtats elektriska aktivitet samt övervaka effekten av behandling. I vissa fall kan man även förutsäga effekten av en viss behandling för en enskild patient genom att analysera EKG-signalen.



I denna avhandling behandlas metoder för analys av förmakens aktivitet (artikel I--IV), och den elektriska kopplingen mellan förmak och kammare (artikel V) under förmaksflimmer.

Under denna arytmi består EKG-signalen av flimmervågor, som avspeglar den elektriska aktiviteten i förmaken, och QRST komplex, som avspeglar den elektriska aktiviteten i kamrarna.



I artikel I används mått som kvantifierar olika egenskaper hos flimmervågorna, såsom amplitud, vågform, frekvens och komplexitet, för att undersöka skillnader mellan förmaksflimmer som upphör spontant och som ej upphör spontant. Det visar sig att flimmerfrekvensen kan användas för att förutspå spontan konvertering.



En ny metod för robust flimmerfrekvensanalys av långtids-EKG presenteras i artikel II, som bygger på en ``hidden Markov modell" (HMM). Resultaten visar att frekvensestimaten är mer tillförlitliga än de som existerande metoder producerar vid höga brusnivåer.



I artikel III används HMM-metoden för att analysera variationer i flimmerfrekvens över dygnet. Sådana cirkadiska variationer kan användas för att sätta in behandling vid en tidpunkt på dygnet då den bedöms ge störst effekt. Resultaten visar att flimmerfrekvensen i de flesta fall var högst under eftermiddagen, även om korttidsvariationen var betydande.



I artikel IV presenteras en entropi-baserad metod för analys av flimmervågor. Metoden kan användas för att skilja paroxysmalt (spontant konverterande) från persistent förmaksflimmer genom analys av 10-sekunders segment av långtids-EKG.



All överledning av elektriska impulser från förmaken till kamrarna går via AV-knutan. I artikel V presenteras en ny stokastisk modell för AV-knutans funktion under förmaksflimmer. Modellens parametrar kan estimeras från EKG-signalen. På så sätt kan egenskaper hos AV-knutan och förändringar hos dessa egenskaper kartläggas från EKG-signalen. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Professor Meste, Olivier, University of Nice, France
organization
publishing date
type
Thesis
publication status
published
subject
keywords
HMM, ECG, Biomedical Signal Processing, Time-Frequency Analysis, Atrial Fibrillation, Sample Entropy, AV modeling
pages
220 pages
defense location
Lectur hall E:1406, Building E, Ole Römers väg 3, Lund University Faculty of Engineering
defense date
2010-11-26 10:15:00
ISBN
978-91-7473-045-6
language
English
LU publication?
yes
id
947f29f5-4295-4cb9-b443-13a122e54474 (old id 1711942)
date added to LUP
2016-04-04 09:17:36
date last changed
2018-11-21 20:52:06
@phdthesis{947f29f5-4295-4cb9-b443-13a122e54474,
  abstract     = {{This thesis deals with signal processing algorithms for analysis of the electrocardiogram (ECG) during atrial fibrillation (AF). Such analysis can be used for diagnosing patients, and for monitoring and predicting their response to various treatment. The thesis comprises an introduction and five papers describing methods for ECG-based modeling and characterization of AF. Paper I--IV deal with methods for characterization of the atrial activity, whereas Paper V deals with modeling of the ventricular response, both problems with the assumption that AF is present. <br/><br>
<br/><br>
In Paper I, a number of measures characterizing the atrial activity in the ECG, obtained using time-frequency analysis as well as nonlinear methods, are evaluated for their ability to predict spontaneous termination of AF. The AF frequency, i.e, the repetition rate of the atrial fibrillatory waves of the ECG, proved to be a significant factor for discrimination between terminating and non-terminating AF. <br/><br>
<br/><br>
Noise is a common problem in ECG signals, particularly in long-term ambulatory recordings.<br/><br>
Hence, robust algorithms for analysis and characterization are required. In Paper II, a robust method for tracking the AF frequency in noisy signals is presented. The method is based on a hidden Markov model (HMM), which takes the harmonic pattern of the atrial activity into account. Using the HMM-based method, the average RMS error of the frequency estimates at high noise levels was significantly lower compared to existing methods.<br/><br>
<br/><br>
In Paper III, the HMM-based method is employed for analysis of 24-h ambulatory ECG signals in order to explore circadian variation in AF frequency. Circadian variations reflect autonomic modulation; attenuation or absence of such variations may help to diagnose patients. Methods based on curve fitting, autocorrelation, and joint variation, respectively, are employed to quantify circadian variations, showing that it is present in most patients with long-standing persistent AF, although the short-term variation is considerable. <br/><br>
<br/><br>
In Paper IV, 24-h ambulatory ECG recordings with paroxysmal and persistent AF are analyzed using an entropy-based method for characterization of the atrial activity. Short segments are classified based on these measures, showing that it is feasible to distinguish between patient with paroxysmal and persistent AF from 10-s ECGs; the average classification rate was above 95%. <br/><br>
<br/><br>
The ventricular response during AF is mainly determined by the AV nodal blocking of atrial impulses. In Paper V, a new model-based approach for analysis of the ventricular response during AF is proposed. The model integrates physiological properties of the AV node and the atrial fibrillatory rate; the model parameters can be estimated from ECG signals. Results show that ventricular response is sufficiently represented by the estimated model in a majority of the recordings; in 85.7% of the analyzed 30-min segments the model fit was considered accurate, and that changes of AV nodal properties caused by autonomic modulation could be tracked through the estimated model parameters.<br/><br>
<br/><br>
In summary, the work within this thesis contributes with new methods for non-invasive analysis of AF, which can be used to tailor and evaluate different strategies for AF treatment.}},
  author       = {{Sandberg, Frida}},
  isbn         = {{978-91-7473-045-6}},
  keywords     = {{HMM; ECG; Biomedical Signal Processing; Time-Frequency Analysis; Atrial Fibrillation; Sample Entropy; AV modeling}},
  language     = {{eng}},
  school       = {{Lund University}},
  title        = {{Novel Approaches to ECG-Based Modeling and Characterization of Atrial Fibrillation}},
  url          = {{https://lup.lub.lu.se/search/files/5284598/1729381.pdf}},
  year         = {{2010}},
}