Electrocardiography (ECG) monitor is a medical device for recording the electrical activities of the heart using electrodes placed on the body. There are many ECG monitors in the market but it is essential to find the accuracy with which they generate results. Accuracy depends on the processing of the ECG signal which contains several noises and the algorithms used for detecting peaks. Based on these peaks the abnormality in the functioning of the heart can be estimated. Hence this thesis characterizes the ECG signal which helps to detect the abnormalities and determine the accuracy of the system.

Physicians’ understanding of bio-signals, measured using medical instruments, becomes the foundation of their decisions and diagnoses of patients, as they rely strongly on what the instruments show. Thus, it is critical and very important to ensure that the instruments’ readings exactly reflect what is happening in the patient’s body so that the detected signal is the real one or at least as close to the real in-body signal as possible and carries all of the appropriate information. This is such an important issue that sometimes physicians use invasive measurements in order to obtain the real bio-signal. Generating an in-body signal from what a measurement device shows is called “signal purification” or “reconstruction,” and can be done only when we have adequate information about the interface between the body and the monitoring device. In this research, first, we present a device that we developed for electrocardiogram (ECG) acquisition and transfer to PC. In order to evaluate the performance of the device, we use it to measure ECG and apply conductive textile as our ECG electrode. Then, we evaluate ECG signals captured by different electrodes, specifically traditional gel Ag/AgCl and dry golden plate electrodes, and compare the results. Next, we propose a method to reconstruct the ECG signal from the signal we detected with our device with respect to the interface characteristics and their relation to the detected ECG. The interface in this study is the skin-electrode interface for conductive textiles. In the last stage of this work, we explore the effects of pressure on skin-electrode interface impedance and its parametrical variation.

Physicians’ understanding of bio-signals, measured using medical instruments, becomes the foundation of their decisions and diagnoses of patients, as they rely strongly on what the instruments show. Thus, it is critical and very important to ensure that the instruments’ readings exactly reflect what is happening in the patient’s body so that the detected signal is the real one or at least as close to the real in-body signal as possible and carries all of the appropriate information. This is such an important issue that sometimes physicians use invasive measurements in order to obtain the real bio-signal. Generating an in-body signal from what a measurement device shows is called “signal purification” or “reconstruction,” and can be done only when we have adequate information about the interface between the body and the monitoring device. In this research, first, we present a device that we developed for electrocardiogram (ECG) acquisition and transfer to PC. In order to evaluate the performance of the device, we use it to measure ECG and apply conductive textile as our ECG electrode. Then, we evaluate ECG signals captured by different electrodes, specifically traditional gel Ag/AgCl and dry golden plate electrodes, and compare the results. Next, we propose a method to reconstruct the ECG signal from the signal we detected with our device with respect to the interface characteristics and their relation to the detected ECG. The interface in this study is the skin-electrode interface for conductive textiles. In the last stage of this work, we explore the effects of pressure on skin-electrode interface impedance and its parametrical variation.

The Electrocardiogram (ECG) is a tool measuring the electrical activity of the heart, and it is extensively used for diagnosis and monitoring of heart diseases. The ECG signal reflects not only the heart activity but also many other physiological processes. The respiratory activity is a prominent process that affects the ECG signal due to the close proximity of the heart and the lungs. In this thesis, several methods for the extraction of respiratory process information from the ECG signal are presented. These methods allow an estimation of the lung volume and the lung pressure from the ECG signal. The potential benefit of this is to eliminate the corresponding sensors used to measure the respiration activity. A reduction of the number of sensors connected to patients will increase patients’ comfort and reduce the costs associated with healthcare. As a further result, the efficiency of diagnosing respirational disorders will increase since the respiration activity can be monitored with a common, widely available method. The developed methods can also improve the detection of respirational disorders that occur while patients are sleeping. Such disorders are commonly diagnosed in sleeping laboratories where the patients are connected to a number of different sensors. Any reduction of these sensors will result in a more natural sleeping environment for the patients and hence a higher sensitivity of the diagnosis.

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This thesis deals with issues of automatic quality estimation of ECG signals. The main aim of this thesis is to implement own algorithm for classifying ECG signals into three classes of quality. Theoretical part of the thesis contains mostly description of recording electrical activity of the heart, anatomy and physiology of the heart, electrocardiography, different types of ECG signals interference and two of the chosen methods for quality estimation. Implementation of the chosen methods is presented in the practical part. Result of this thesis are two implemented algorithms, which are based on methods described in the theoretical part. The first of two is based on detection of R-waves, validation of physiological assumptions and the subsequent calculation of the correlation coefficient between adaptive template and interfered signal. Second is based on calculation of a continuous SNR value over time, which is then thresholded. The robustness of the methods was verified on the three specified real ECG signals, which are all available on UBMI including annotation of specific signal parts. Those 24-hour long signals were recorded by Holter monitor, which is described in the theoretical part of the thesis. Achieved results of individual methods, including their comparison with annotation and statistical evaluation are presented in the conclusion of this thesis.

This masters thesis is focused on filtering the ECG signal for suppression of spurious frequency components of the network. The theoretical part is talking about electrocardiography, ECG signal interference and about principle different types of filtration. In practical part of this thesis are described linear filtering methods and wavelet transform methods with discrete time. The main topic of this work is recommended the best type of filtration.

This master thesis solves the problem of estimating the quality of ECG signals. The main objective of the work is to implement a self-assessment of the quality assessment method based on the studied methods for estimating the quality of the ECG signal. The theoretical part of the thesis contains mainly the description of the electrical activity of the heart, cardiac anatomy, and physiology, electrocardiography, various types of ECG signal interference and methods describing the estimation of ECG signal quality. The practical part deals with the application of individual methods for estimating the quality of ECG signals. The SNR (signal-to-noise ratio) calculation is used to continuously estimate the quality of the ECG. Signal quality can also be judged based on statistical functions, adaptive filtering, or by analyzing independent components. The proposed method is based on the calculation of the correlation coefficient between the adaptive template and the disturbed signal. The robustness of the method was verified on artificially created ECG signals with different noise levels and then on real signals from the MIT-BIH database.

The thesis provides an analysis of the ECG signal, focusing on the possible types of interference occuring in the signal. These types of interference are closely examined and their realization in the MATLAB programming environment is characterized. The SPIHT algorithm is introduced and the possibilities of its use are discussed. Futher, the application of this algorithm to the ECG signal containing individual types of interference generated by means of the designed interference generator is described and the impact of the SPIHT algorithm on the specific types of signal interference is analyzed. Finally, the obtained results are evaluated.

Separating unknown signal mixtures into their constituent parts is a difficult problem in signal processing called blind source separation. One of the benchmark problems in this area is the extraction of the fetal heartbeat from an electrocardiogram in which it is overshadowed by a strong maternal heartbeat. This thesis presents a study of a signal separation technique called independent component analysis (ICA), in order to assess its suitability for the maternal-fetal ECG separation problem. This includes an analysis of ICA on deterministic, stochastic, simulated and recorded ECG signals. The experiments presented in this thesis demonstrate that ICA is effective on linear mixtures of known simulated or recorded ECGs. The performance of ICA was measured using visual comparison, heart rate extraction, and energy, information theoretic, and fractal-based measures. ICA extraction of clinically recorded maternal-fetal ECGs mixtures, in which the source signals were unknown, were successful at recovering the fetal heart rate.