Oscillometry is the most common measurement method used in automatic blood pressure (BP) monitors. However, most of the oscillometric algorithms are without physiological and theoretical foundation, and rely on empirically derived coefficients for systolic and diastolic pressure evaluation which affects the reliability of the technique. In this thesis, the oscillometric BP estimation problem is addressed using a comprehensive modeling approach, based on which coefficient-free estimation of BP becomes possible. A feature-based neural network approach is developed to find an implicit relationship between BP and the oscillometric waveform (OMW). The modeling approach is then extended by developing a mathematical model for the OMW as a function of the arterial blood pressure, cuff pressure, and cuff-arm-artery system parameters. Based on the developed model, the explicit relationship between the OMW and the systolic and diastolic pressures is found and a new coefficient-free oscillometric BP estimation method using the trust region reflective algorithm is proposed. In order to improve the reliability of BP estimates, the electrocardiogram signal is recorded simultaneously with the OMW, as another independent source of information. The electrocardiogram signal is used to identify the true oscillometric pulses and calculate the pulse transit time (PTT). By combining our developed model of oscillomtery with an existing model of the pulse wave velocity, a new mathematical model is derived for the PTT during the cuff deflation. The derived model is incorporated to study the PTT-cuff pressure dependence, based on which a new coefficient-free BP estimation method is proposed. In order to obtain accurate and robust estimates of BP, the proposed model-based BP estimation method sare fused by computing the weighted arithmetic mean of their estimates. With fusion of the proposed methods, it is observed that the mean absolute error (MAE) in estimation of systolic and diastolic pressures is 4.40 and 3.00 mmHg, respectively, relative to the Food and Drug Administration-approved Omron monitor. In addition, the proposed feature-based neural network was compared with auscultatory measurements by trained observers giving MAE of 6.28 and 5.73 mmHg in estimation of systolic and diastolic pressures, respectively. The proposed models thus show promise toward developing robust BP estimation methods.

Cette thèse de doctorat est consacrée à l'étude des problèmes directe et inverse en électrophysiologie cardiaque. Comme les équations qui décrivent l'activité électrique du coeur peuvent être très couteuses en temps de calcul, une attention particulière est apportée aux méthodes d'ordre réduit et à leur applications aux modèles de l'électrophysiologie.Dans un premier temps, nous introduisons les modèles mathématiques et numériques de l'électrophysiologie cardiaque. Ces modèles nous permettent de réaliser des simulations numériques que nous validons à l'aide de plusieurs critères qualitatifs et quantitatifs trouvés dans la littérature médicale. Comme notre modèle prend en compte les oreillettes et les ventricules, nous sommes capables de reproduire des cycles complets d'électrocardiogrammes (ECG) à la fois dans des conditions saines et dans des cas pathologiques.Ensuite, plusieurs méthodes d'ordre réduit sont étudiées pour la résolution des équations de l'électrophysiologie. La méthode Proper Orthogonal Decomposition (POD) est appliquée pour la discrétisation des équations de l'électrophysiologie dans plusieurs configurations, comme par exemple la simulation d'un infarctus du myocarde. De plus, cette méthode est utilisée pour résoudre quelques problèmes d'identification de paramètres comme localiser un infarctus à partir de mesures d'un électrocardiogramme ou simuler une courbe de restitution. Pour contourner les limitations de la POD, une nouvelle méthode basée sur des couples de Lax approchés (Approximated Lax Pairs, ALP) est utilisée. Cette méthode est appliquée aux problèmes directe et inverse. Pour finir, un nouvel algorithme, basé sur les méthodes ALP et l'interpolation empirique discrète, est proposé. Cette nouvelle approche améliore significativement l'efficacité de l'algorithme original ALP et nous permet de considérer des modèles plus complexes utilisés en électrophysiologie cardiaque. / This PhD thesis is dedicated to the investigation of the forward and the inverse problem of cardiac electrophysiology. Since the equations that describe the electrical activity of the heart can be very demanding from a computational point of view, a particular attention is paid to the reduced order methods and to their application to the electrophysiology models. First, we introduce the mathematical and numerical models of electrophysiology and we implement them to provide for simulations that are validated against various qualitative and quantitative criteria found in the medical literature. Since our model takes into account atria and ventricles, we are able to reproduce full cycle Electrocardiograms (ECG) in healthy configurations and also in the case of several pathologies. Then, several reduced order methods are investigated for the resolution of the electrophysiology equations. The Proper orthogonal Decomposition (POD) method is applied for the discretization of the electrophysiology equations in several configurations, as for instance the simulation of a myocardial infarction. Also, the method is used in order to solve some parameters identification problems such as the identification of an infarcted zone using the Electrocardiogram measures and for the efficient simulation of restitution curves. To circumvent some limitations of the POD method, a new reduced order method based on the Approximated Lax Pairs (ALP) is investigated. This method is applied to the forward and inverse problems. Finally, a new reduced order algorithm is proposed, based on the ALP and the Discrete Empirical Interpolation methods. This new approach significantly improves the efficiency of the original ALP algorithm and allow us to consider more complex models used in electrophysiology.

The aging population requires new and innovative approaches to monitor and supervise medical and physical conditions in residential environments. For this purpose, various sensor and hardware systems are being developed by researchers and industrial companies. One way to monitor health status is the electrocardiogram (ECG), which noninvasively measures heart activity on the body surface. These measurements provide a simple and easy way to monitor health on a continuous basis. However, the use of ECG measurements outside a confined clinical setting, beyond purely medical purposes, is associated with considerable disadvantages resulting from the given freedom of movement. In this work, a substantial noise source in mobile ECG is examined: Motion artifacts. We study the spectral characteristics of motion artifacts for a set of different motions representing everyday activities, namely: standing up, bending forward, walking, running, jumping, and climbing stairs. Furthermore, we investigate to what extent the reference sensors (accelerometer, gyroscope, and skin-electrode impedance) are able to characterize and remove the recorded motion artifacts from the measurements. Our results demonstrate that motion artifacts markedly change their characteristics with a change in motion. While lowintensity movements manifest in lower frequency bands, higher intensity exercises provoke motion artifacts that are much more complex in their composition. These characteristics are correspondingly reflected in the correlation between reference sensors and artifacts. To overcome the drawbacks of motion artifacts in mobile measurements, we propose the application of tensor decomposition using canonical polyadic decomposition (CPD) as an example. A significant advantage of tensor factorization is that it can decompose the data without artificial constraints, unlike matrix factorization. We use CPD along with measurements obtained from different reference sensors to remove the artifacts. Wavelet transformation is utilized to transform ECG and reference data from vector to matrix format. Subsequently, a tensor is constructed by combining the heterogeneous measurements into a three-dimensional tensor. In this way, it is possible to access temporal and spectral features within the data simultaneously. Subsequently, we propose a methodology to predict the decomposition rank based on statistical features in the ECG that quantify the signal quality. To evaluate the performance of the decomposition process, we combine isolated motion artifacts recorded at the back with ECG obtained in rest to generate artificially corrupted data. The results suggest that CPD successfully removes motion artifacts from the data for all reference sensors regarded.

The thesis deals with problems of automatic detection of premature ventricular contractions in ECG records. One detection method which uses a convolutional neural network and LSTM units is implemented in the Python language. Cardiac cycles extracted from one-lead ECG were used for detection. F1 score for binary classification (PVC and normal beat) on the test dataset reached 96,41 % and 81,76 % for three-class classification (PVC, normal beat and other arrhythmias). Lastly, the accuracy of the classification is evaluated and discussed, the achieved results for binary classification are comparable to the results of methods described in different papers.

The goal of this work is to study the principles of delineation of ECG signals, wavelet transformation and transformation approaches to increase the number of available leads. Consequently, the knowledge was used to create delineation algorithm in Matlab. The algorithm was tested on complete CSE database. The obtained results were compared with the criteria which are set for the CSE database. In this work were realized six transformation approaches to increase the number of available leads. All of them were analyzed by delineation algorithm. In the work was examined, whether the transformation increase the efficiency of detection.

This paper deals with the classification of ECG measured from isolated rabbit hearts during the experiment with repeated ischemia. Classification features were calculated using the methods of heart rate variability analysis. The results were statistically evaluated. Heart rate variability parameters were calculated using Kubios HRV, other calculations were performed in MATLAB. Artificial neural network was created to classify the analyzed parameters to specific groups.

This master‘s thesis deals with the use of higher order cumulants for classification of cardiac cycles. Second-, third-, and fourth-order cumulants were calculated from ECG recorded in isolated rabbit hearts during experiments with repeated ischemia. Cumulants properties useful for the subsequent classification were verified on ECG segments from control and ischemic group. The results were statistically analyzed. Cumulants are then used as feature vectors for classification of ECG segments by means of artificial neural network.

The thesis describes design, algorithmization and realization of graphical application for recording EKG and PPG signal using LabJack UE9 tool in Matlab program, it also describes subsequent deposition of recorded signals and their processing, such as optional selection, cropping and filtering. Furthermore there are described types of filters, methods for detecting basic parameters of EKG and PPG signals and methods for detecting R waves and Systolic peaks. Based on detection of those parameters, algorithms for computing average heart rate and finding arrhythmias were designed and tested. Last part of the thesis includes an evaulation which compares values detected by designed algorithms with values from public database which includes reference annotation.

Classifying the electrocardiogram is of clinical importance because classification can be used to diagnose patients with cardiac arrhythmias. Many industries utilize machine learning techniques that consist of feature extraction methods followed by Naive- Bayesian classification in order to detect faults within machinery. Machine learning techniques that analyze vibrational machine data in a mechanical application may be used to analyze electrical data in a physiological application. Three of the most common feature extraction methods used to prepare machine vibration data for Naive-Bayesian classification are the Fourier transform, the Hilbert transform, and the Wavelet Packet transform. Each machine learning technique consists of a different feature extraction method to prepare the data for Naive-Bayesian classification. The effectiveness of the different machine learning techniques, when applied to electrocardiogram, is assessed by measuring the sensitivity and specificity of the classifications. Comparing the sensitivity and specificity of each machine learning technique to the other techniques revealed that the Wavelet Packet transform, followed by Naïve-Bayesian classification, is the most effective machine learning technique.

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