An electrocardiogram (ECG) is used to monitor the activity of the heart. The human heart beats seventy times on an average per minute. The rate at which a human heart beats can exhibit a periodic variation. This is known as heart rate variability (HRV). Heart rate variability is an important measurement that can predict the survival after a heart attack. Studies have shown that reduced HRV predicts sudden death in patients with Myocardial Infarction (MI). The time interval between each beat is called an RR interval, where the heart rate is given by the reciprocal of the RR interval expressed in beats per minute. For a deeper insight into the dynamics underlying the beat to beat RR variations and for understanding the overall variance in HRV, an accurate method of estimating the RR interval must be obtained. Before an HRV computation can be obtained the quality of the RR interval data obtained must be good and reliable. Most QRS detection algorithms can easily miss a QRS pulse producing unreliable RR interval values. Therefore it is necessary to estimate the RR interval in the presence of missing QRS beats. The approach in this thesis is to apply KALMAN estimation algorithm to the RR interval data calculated from the ECG. The goal is to improve the RR interval values obtained from missed beats of ECG data.

We studied Heart Rate Variability (HRV) evolution during therapeutic hypothermia in newborns with hypoxic ischemic encephalopathy (HIE) using spectral analysis. We hypothesized that HRV measures are predictive of neurological outcome in babies with HIE. Non-stationarity in the data causes inaccurate quantification of the spectral power. A modification was proposed to power spectral analysis approach to mitigate the effect of non-stationarity. The modified and the standard approaches were applied to cardiac beat-to-beat intervals of newborns receiving hypothermia treatment. The performance of the approaches in distinguishing the RRi dynamics of two groups of newborns was assessed using area under the receiver operating characteristic (ROC) curve. Our results showed that the modified spectral analysis distinguished the two groups of neonates better than the standard approach. These results may be useful in identifying the deteriorating physiology of the infants receiving hypothermia treatment early in time and strategize alternate interventions for them.

An recent finding shows that heart rate data possess self-similar property, which is characterized by a parameter H, as well as a long range dependent parameter d. We estimate H by the EBP(Embedded Branching Process) method to derive the fractional parameter d in the first part. The heart rate and R-R interval data are found to have high differencing parameter(d=0.8 ~0.9) and against the normality assumption. Thus the heart rate and R-R interval data are first fractionally differenced of order 0.5 to achieve stationarity. In the second part, we analyze the RR-interval data on the physionet and obtain the long range parameters. After fractionally differencing 0.5 order, the EBP method is adapted to estimate the long range parameter d. The EWMA and EWRMS control charts of the I(d) processes are constructed to monitor the heart rate mean level and variability, respectively for the 18 RR-interval data sets from the physionet. For the EWMA control chart the out of control percentages are chosen to the nominal probability. However, the out of control percentages are affected by the skewness and kurtosis of the process distribution for the EWRMS control carts. Generally speaking, the I(d)-EWMA and I(d)-EWRMS control charts provide a proper monitor system for heart rate mean level and variability.

Abstract The quantitation of the variability in cardiovascular signals provides information about the autonomic neural regulation of the heart and the circulatory system. Several factors have an indirect effect on these signals as well as artifacts and several types of noise are contained in the recorded signal. The dynamics of RR and QT interval time series have also been analyzed in order to predict a risk of adverse cardiac events and to diagnose them. An ambulatory measurement setting is an important and demanding condition for the recording and analysis of these signals. Sophisticated and robust signal analysis schemes are thus increasingly needed. In this thesis, essential points related to ambulatory data acquisition and analysis of cardiovascular signals are discussed including the accuracy and reproducibility of the variability measurement. The origin of artifacts in RR interval time series is discussed, and consequently their effects and possible correction procedures are concidered. The time series including intervals differing from a normal sinus rhythm which sometimes carry important information, but may not be as such suitable for an analysis performed by all approaches. A significant variation in the results in either intra- or intersubject analysis is unavoidable and should be kept in mind when interpreting the results. In addition to heart rate variability (HRV) measurement using RR intervals, the dy- namics of ventricular repolarization duration (VRD) is considered using the invasively obtained action potential duration (APD) and different estimates for a QT interval taken from a surface electrocardiogram (ECG). Estimating the low quantity of the VRD vari- ability involves obviously potential errors and more strict requirements. In this study, the accuracy of VRD measurement was improved by a better time resolution obtained through interpolating the ECG. Furthermore, RTmax interval was chosen as the best QT interval estimate using simulated noise tests. A computer program was developed for the time interval measurement from ambulatory ECGs. This thesis reviews the most commonly used analysis methods for cardiovascular vari- ability signals including time and frequency domain approaches. The estimation of the power spectrum is presented on the approach using an autoregressive model (AR) of time series, and a method for estimating the powers and the spectra of components is also presented. Time-frequency and time-variant spectral analysis schemes with applica- tions to HRV analysis are presented. As a novel approach, wavelet and wavelet packet transforms and the theory of signal denoising with several principles for the threshold selection is examined. The wavelet packet based noise removal approach made use of an optimized signal decomposition scheme called best tree structure. Wavelet and wavelet packet transforms are further used to test their effciency in removing simulated noise from the ECG. The power spectrum analysis is examined by means of wavelet transforms, which are then applied to estimate the nonstationary RR interval variability. Chaotic modelling is discussed with important questions related to HRV analysis.ciency in removing simulated noise from the ECG. The power spectrum analysis is examined by means of wavelet transforms, which are then applied to estimate the nonstationary RR interval variability. Chaotic modelling is discussed with important questions related to HRV analysis.

The thesis deals with the biological data averaging applied to a periodical and repetitive signal, specifically to an ECG signals. There were used signals from MIT-BIH Arrhythmia database and ÚBMI database. Averaging was realized with constant, floating and exponential Windows, where was used the method of addition of the filtered residue. This method is intended to capture the slow variations from the input to the output signal. The outcomes of these methods can be used as a basis for further work, or function as an example of principled methods. Methods and its outcomes were created in Matlab.

The master’s thesis is focused on the possibilities of measuring heart rate of ECG signal and its use in therapeutic game of ECG biofeedback. This thesis describes the way of measuring ECG with acquisition unit Biopac and analyzes signal processing for measurement of heart rate – algorithms of QRS detection, HRV analysis. Realisation of therapeutic is designed for applications in Matlab.

This thesis focuses on the issue of atrial fibrillation and the success of their detection in the ECG signal. It provides a description of electrical activity of the heart with the theoretical analysis of atrial fibrillation and methods for their detection. Additionally the work describes procedures for the implementation of three selected methods for the detection of atrial fibrillation in the MATLAB environment, presents the results of their tests on two atrial fibrillation signal databases and assesses the accuracy of each method.

Tolkningen av EKG är en viktig metod vid diagnostisering av onormala hjärttillstånd och kan användas i förebyggande syfte att upptäcka tidigare okända hjärtproblem. Att enkelt kunna mäta sitt EKG och få det analyserat och presenterat på ett pedagogiskt sätt utan att behöva rådfråga en läkare är något det finns ett konsumentbehov av. Denna rapport beskriver hur en EKG-signal behandlas med olika algoritmer och metoder i syfte att detektera hjärtslag och dess olika parametrar. Denna information används till att klassificera varje hjärtslag för sig och därmed avgöra om användaren har en normal eller onormal hjärtfunktion. För att nå dit har en mjukvaruprototyp utvecklats där algoritmerna implementerats. En enkätundersökning gjordes i syfte att undersöka hur utdata från mjukvaruprototypen skulle presenteras för en vanlig användare utan medicinsk utbildning. Sju filer med EKG-signaler från MIT-BIH Arrhythmia Database användes för testning av mjukvaruprototypen. Resultatet visade att prototypen kunde detektera en rad olika hjärtfel som låg till grund vid fastställning om hjärtat slog normalt eller onormalt. Resultatet presenterades på en mobilapp baserad på enkätundersökningen. / The interpretation of the ECG is an important method in the diagnosis of abnormal heart conditions and can be used proactively to discover previ-ously unknown heart problems. Being able to easily measure the ECG and get it analyzed and presented in a clear manner without having to consult a doctor is improtant to satisfy consumer needs. This report describes how an ECG signal is treated with different algo-rithms and methods to detect the heartbeat and its various parameters. This information is used to classify each heartbeat separately and thus determine whether the user has a normal or abnormal cardiac function. To achieve this a software prototype was developed in which the algorithms were implemented. A questionnaire survey was done in order to examine how the output of the software prototype should be presented for a user with no medical training. Seven ECG files from MIT-BIH Arrhythmia database were used for validation of the algorithms. The developed algorithms could detect of if any abnormality of heart function occurred and informed the users to consult a physician. The presentation of the heart function was based on the result from the questioner.

The aim of this thesis is to describe the variability of cardiac rhythm and familiarity with the methods of the analysis, ie by monitoring changes in heart rhythm electrogram signal recording and using the methods in the time domain using recurrent diagram. The work describes the quantification of the methods and possibilities of quantifiers in the evaluation of heart rate variability analysis. It also describes the clinical significance of heart rate variability and diagnostic capabilities changes of heart rate variability caused by ischemic heart disease. The practical part describes how to create applications in Matlab to calculate the quantifiers analysis of heart rate variability in the time domain using recurrent diagram. The calculation was made of the positions R wave elektrogram signal isolated rabbit hearts. The calculated values of quantifiers both methods were statistically evaluated and discussed.

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