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

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

Offer för en mänsklig gärningsman? - En kvalitativ innehållsanalys av våldtäktsdomar

Abstract Authors: Frida Ljungsten, Sara Sandberg Titel: Victim of a human perpetrator – a qualitative content analysis of judicial decisions. Supervisor: Roberto Scaramuzzino Examiner: Anett Schenk It is not unusual today that one will find articles about crimes of rape in the Swedish media. These articles are often expressing a doubt against how the court of law sentences the offender or describe the victim, in their opinion in a negative way. This study has taken its shape from these discussions portrayed in the media, as well as in a speculation around how the newly changed law is handling the critique that has increased during the last year. On top of this we discovered that the area covering the presentation of the victim and the offender in a judicial decision is somewhat unexplored. The purpose of this study is therefore to understand the presentation made of the victim and the offender in the judicial decisions, before as well as after the change of law that occurred in July 2013. To do this we are using a qualitative content analysis, looking at sixteen cases, to study the court discussions in judicial decisions. We are using Nils Christie’s (2001) theory of ideal victims to describe and understand the presentation of the victim and the offender. On top of that we use theories about judicial discretion to understand the changes within our study. The study shows that the different judicial courts are presenting the offender and victim in a somewhat alike manner. The biggest change being that the offenders, and as a result the victims, are portrayed a bit more ideal after the law changed. We drew the conclusion that the judicial discretion must be weak in most cases, but alas allowing some room for different interpretation. In a few instances the discretional freedom appeared to be stronger, though, often where the laws were unspecified and left a big capacity for interpretation. In conclusion, our result does not show a big distinction from before and after the change

Respiratory Modulation in Permanent Atrial Fibrillation

Several studies have shown that the autonomic nervous system (ANS) can induce changes during atrial fibrillation (AF). There is currently a lack of methods for quantifying ANS induced variations during AF. The purpose of this study is to quantify respiratory induced modulation in the f-wave frequency trend. Following qrst-cancellation, the local f-wave frequency is estimated by fitting a harmonic f-wave model signal and a quality index (SQI) is computed based on the model fit. The resulting frequency trend is filtered using a narrow bandpass filter with a center frequency corresponding to the local respiration rate. The magnitude of the respiratory induced f-wave frequency modulation is estimated by the envelope of the filtered frequency trend. The performance of the method is validated using simulations and the method is applied to analyze ECG data from eight patients with permanent AF recorded during 0.125 Hz frequency controlled respiration before and after the full vagal blockade, respectively. Results from simulated data show the magnitude of the respiratory induced f-wave frequency modulation can be estimated with an error of less than = 0.005Hz if the SQI is above 0.45. The signal quality was sufficient for analysis in 7 out of 8 patients. In 4 patients the magnitude decreased and in 3 patients there was no change

Automatic Detection of Atrial Fibrillation Using Electrocardiomatrix and Convolutional Neural Network

Long-term electrocardiogram (ECG) monitoring is a standard clinical routine in cryptogenic stroke survivors to assess the presence of atrial fibrillation (AF). However, manual evaluation of such recordings is time consuming, in particular when brief episodes are of interest. The electrocardiomatrix (ECM) technique allows compact, two-dimensional representation of the ECG and facilitates its review. In this study, we present a convolutional neural network (CNN) approach for automatic detection of AF based on ECM images. ECG segments of only 10 beats were converted into ECM images. A CNN was implemented to classify the ECMs between non-AF and AF. The CNN was trained using the MIT-BIH-NSR and the MIT-BIH-LTAF, and tested on the MIT-BIH-AF. A total of 120088 non-AF and 108088 AF ECM images were classified with accuracy of 86.95%. This study suggests that a CNN allows automatic detection of AF episodes of only 10 beats when the ECG data is represented as an ECM image

ECG-based estimation of respiratory modulation of AV nodal conduction during atrial fibrillation

Information about autonomic nervous system (ANS) activity may be valuable for personalized atrial fibrillation (AF) treatment but is not easily accessible from the ECG. In this study, we propose a new approach for ECG-based assessment of respiratory modulation in AV nodal refractory period and conduction delay. A 1-dimensional convolutional neural network (1D-CNN) was trained to estimate respiratory modulation of AV nodal conduction properties from 1-minute segments of RR series, respiration signals, and atrial fibrillatory rates (AFR) using synthetic data that replicates clinical ECG-derived data. The synthetic data were generated using a network model of the AV node and 4 million unique model parameter sets. The 1D-CNN was then used to analyze respiratory modulation in clinical deep breathing test data of 28 patients in AF, where a ECG-derived respiration signal was extracted using a novel approach based on periodic component analysis. We demonstrated using synthetic data that the 1D-CNN can predict the respiratory modulation from RR series alone ($\rho$ = 0.805) and that the addition of either respiration signal ($\rho$ = 0.830), AFR ($\rho$ = 0.837), or both ($\rho$ = 0.855) improves the prediction. Results from analysis of clinical ECG data of 20 patients with sufficient signal quality suggest that respiratory modulation decreased in response to deep breathing for five patients, increased for five patients, and remained similar for ten patients, indicating a large inter-patient variability.Comment: 20 pages, 7 figures, 5 table

Frequency Tracking of Atrial Fibrillation using Hidden Markov Models

A Hidden Markov Model (HMM) is used to improve the robustness to noise when tracking the atrial fibrillation (AF) frequency in the ECG. Each frequency interval corresponds to a state in the HMM. Following QRST cancellation, a sequence of observed states is obtained from the residual ECG, using the short time Fourier transform. Based on the observed state sequence, the Viterbi algorithm, which uses a state transition matrix, an observation matrix and an initial state vector, is employed to obtain the optimal state sequence. The state transition matrix incorporates knowledge of intrinsic AF characteristics, e.g., frequency variability, while the observation matrix incorporates knowledge of the frequency estimation method and SNRs. An evaluation is performed using simulated AF signals where noise obtained from ECG recordings have been added at different SNR. The results show that the use of HMM considerably reduces the average RMS error associated with the frequency tracking: at 5 dB SNR the RMS error drops from 1.2 Hz to 0.2 Hz

An atrioventricular node model incorporating autonomic tone

The response to atrial fibrillation (AF) treatment is differing widely among patients, and a better understanding of the factors that contribute to these differences is needed. One important factor may be differences in the autonomic nervous system (ANS) activity. The atrioventricular (AV) node plays an important role during AF in modulating heart rate. To study the effect of the ANS-induced activity on the AV nodal function in AF, mathematical modelling is a valuable tool. In this study, we present an extended AV node model that incorporates changes in autonomic tone. The extension was guided by a distribution-based sensitivity analysis and incorporates the ANS-induced changes in the refractoriness and conduction delay. Simulated RR series from the extended model driven by atrial impulse series obtained from clinical tilt test data were qualitatively evaluated against clinical RR series in terms of heart rate, RR series variability and RR series irregularity. The changes to the RR series characteristics during head-down tilt were replicated by a 10% decrease in conduction delay, while the changes during head-up tilt were replicated by a 5% decrease in the refractory period and a 10% decrease in the conduction delay. We demonstrate that the model extension is needed to replicate ANS-induced changes during tilt, indicating that the changes in RR series characteristics could not be explained by changes in atrial activity alone

The environmental benefits and challenges of a composite car with structural battery materials

One way to reduce the environmental impact of an electric vehicle is to reduce the vehicle’s mass. This can be done by substitution of conventional materials such as steel, aluminium, and plastics with carbon fibre composites, or possibly even with structural battery composite materials. In the latter case, another consequence is that the size of the vehicle battery is reduced as the structural battery composite not only provides structural integrity, but also stores energy. This study assesses the change in life cycle environmental impacts related to transitioning from a conventional battery electric vehicle to a vehicle with components made from either carbon fibre composites or structural battery composites, with the aim of identifying environmental challenges and opportunities for cars with a high share of composite materials. Results show that a transition to carbon fibre composites and structural battery composite materials today would (in most cases) increase the total environmental impact due to the energy intensive materials production processes. The two major contributors to the environmental impacts for the structural battery composite materials are energy intensive structural battery material manufacturing process and carbon fibre production process, both of which can be expected to decrease their energy consumption as the technology maturity level increases and other production and manufacturing processes are developed. For future assessments, more effort needs to be put on collecting primary data for large-scale structural battery composites production and on assessing different technology development routes

ECG-derived respiratory rate in atrial fibrillation

Objective: The present study addresses the problem of estimating the respiratory rate from the morphological ECG variations in the presence of atrial fibrillatory waves (f-waves). The significance of performing f-wave suppression before respiratory rate estimation is investigated. Methods: The performance of a novel approach to ECG-derived respiration, named “slope range” (SR) and designed particularly for operation in atrial fibrillation (AF), is compared to that of two well-known methods based on either R-wave angle (RA) or QRS loop rotation angle (LA). A novel rule is proposed for spectral peak selection in respiratory rate estimation. The suppression of f-waves is accomplished using signal- and noise-dependent QRS weighted averaging. The performance evaluation embraces real as well as simulated ECG signals acquired from patients with persistent AF; the estimation error of the respiratory rate is determined for both types of signals. Results: Using real ECG signals and reference respiratory signals, rate estimation without f-wave suppression resulted in a median error of 0.015±0.021 Hz and 0.019±0.025 Hz for SR and RA, respectively, whereas LA with f-wave suppression resulted in 0.034±0.039 Hz. Using simulated signals, the results also demonstrate that f-wave suppression is superfluous for SR and RA, whereas it is essential for LA. Conclusion: The results show that SR offers the best performance as well as computational simplicity since f-wave suppression is not needed. Significance: The respiratory rate can be robustly estimated from the ECG in the presence of AF

Relationship between Atrial Oscillatory Acetylcholine Release Pattern and f-wave Frequency Modulation: a Computational and Experimental Study

The frequency of fibrillatory waves (f-waves), F f , exhibits significant variation over time, and previous studies suggest that some of this variation is related to respiratory modulation through the autonomic nervous system. In this study, we tested the hypothesis that this variation (ΔF f ) could be related to acetylcholine concentration ([ACh]) release pattern. Electrocardiograms were recorded from seven patients during controlled respiration before and after full vagal blockade, from which f-wave frequency modulation was characterized. Computational simulations in human atrial tissues were performed to assess the effects of [ACh] release pattern on F f and compared to experimental results in humans. A cross-stimulation protocol was applied onto the tissue to initiate a rotor while cyclically varying [ACh] following a sinusoidal waveform of frequency equal to 0.125 Hz. Different mean levels (0.05, 0.075μM/l) and peak-to-peak ranges (0.1, 0.05, 0.025 μM/l) of [ACh] variation were tested. In all patients, an f-wave frequency modulation could be observed. In 57% of the patients, this modulation was significantly reduced after vagal blockade. Simulations confirmed that rotor frequency variations followed the induced [ACh] patterns. Mean F f was dependent on mean [ACh] level, whileΔF f was dependent on [ACh] variation range