Tato práce se zabývá možností využití vlnkové transformace v aplikacích, které se zabývají potlačením šumu. Především se jedná o oblast filtrace signálu EKG. Úkolem je zhodnotit vliv různých parametrů nastavení samotné filtrace a zjistit jaký vliv má různé nastavení prahování wavelet koeficientů. Výsledkem práce je také stanovení hodnot prahů, stanovení nejlepšího způsobu rozkladu signálu a volba rekonstrukčních bank filtrů. Text obsahuje výsledky Wienerovy filtrace, při které byly testovány různé banky rozkladových a rekonstrukčních filtrů.Všechny popsané filtrační metody byly testovány na reálných záznamech EKG s aditivním myopotenciálním šumem. Algoritmy byly realizovány v prostředí MATLAB.

The aim of this thesis is segmentation of P waves in ECG signals. The theoretical part of the thesis describes the physiology of the heart and the basics of deep learning methods. Preprocessing of the signals is performed and neural network U-Net is implemented in the Python software environment in the practical part. Afterwards, optimization of network architecture is performed in order to reduce model complexity. Lastly the success rate of the model is evaluated.

The Thesis deals with the area of cardio stimulation. It describes the different stimulation modes and their specifics. It describes the creation of a virtual cardio stimulator based on R-waves, designed in the LabVIEW application. Part of the thesis is the software realization of the model and the verification of its functionality.

The Cardiovascular diseases may evocated the high percentual risk of sudden cardiac death in whole world. In several western countries is the number of death higher then number of cancer death. In this time is used a lot of methods for prediction of sudden cardiac death with focus on ECG T-wave alternance. The aim of the theses was to do stronger relation and cooperation with Internal Cardiac Clinic of Faculty Hospital Brno Bohunice on the risk analysis of sudden cardiac death. Secondly, we met the methods used for detection and quantification of simulated TWA. Last but not least was necessary to find TWA detection methods improvement and process the data on real signals obtained from Faculty Hospital Brno Bohunice. First part of the Thesis is focused on summary of pathologic artifacts in ECG signal, which are important for sudden cardiac risk stratification. There are described further known detection and quantification methods for TWA analysis. An interesting part for clinical practice is analysis of TWA trend in time and looking for the best method, which is able to catch and track the short TWA trend changes. Second part describes the new methods improvements, which were tested with interesting outputs. Further, there was developed method for TWA presence statement probability evaluation.

Estimation of the fetal heart rate (FHR) has gained interest in the last century; low heart rate variability has been studied to identify intrauterine growth restricted fetuses (prepartum), and abnormal FHR patterns have been associated with fetal distress during delivery (intrapartum). Several monitoring techniques have been proposed for FHR estimation, including auscultation and Doppler ultrasound. This thesis focuses on the extraction of the non-invasive fetal electrocardiogram (NI-FECG) recorded from a limited set of abdominal sensors. The main challenge with NI-FECG extraction techniques is the low signal-to-noise ratio of the FECG signal on the abdominal mixture signal which consists of a dominant maternal ECG component, FECG and noise. However the NI-FECG offers many advantages over the alternative fetal monitoring techniques, the most important one being the opportunity to enable morphological analysis of the FECG which is vital for determining whether an observed FHR event is normal or pathological. In order to advance the field of NI-FECG signal processing, the development of standardised public databases and benchmarking of a number of published and novel algorithms was necessary. Databases were created depending on the application: FHR estimation with or without maternal chest lead reference or directed toward FECG morphology analysis. Moreover, a FECG simulator was developed in order to account for pathological cases or rare events which are often under-represented (or completely missing) in the existing databases. This simulator also serves as a tool for studying NI-FECG signal processing algorithms aimed at morphological analysis (which require underlying ground truth annotations). An accurate technique for the automatic estimation of the signal quality level was also developed, optimised and thoroughly tested on pathological cases. Such a technique is mandatory for any clinical applications of FECG analysis as an external confidence index of both the input signals and the analysis outputs. Finally, a Bayesian filtering approach was implemented in order to address the NI-FECG morphology analysis problem. It was shown, for the first time, that the NI-FECG can allow accurate estimation of the fetal QT interval, which opens the way for new clinical studies on the development of the fetus during the pregnancy.

This project considers methods of construction QRS detectors. It focus in detection complexes of QRS single leads and space speed, which are calculated from three orthogonal leads. In theory was refer to various methods, which lead to design detector. It were designed two algoritms (constant and adaptive detecting threshold), which were implemented into detector and the signal was preprocessed by Hilbert transformation. Toward algoritms were completed by modification, which improved detection effectivity. Function of algoritms were tested in all signals of CSE (V2,V5,aVF).

The thesis deals with possibilities of using wavelet transform in applications dealing with noise reduction, primarily in the field of ECG signals denoising. We assess the impact of the various filtration parameters setting as the thresholding wavelet coefficients method, thresholds level setting and the selection of decomposition and reconstruction filter banks.. Our results are compared with the results of linear filtering. The results of wavelet Wieners filtration with pilot estimation are described below. Mainly, we tested a combination of decomposition and reconstruction filter banks. All the filtration methods described here are tested on real ECG records with additive myopotential noise character and are implemented in the Matlab environment.

This work deals with delineation of ECG signals. First we will become familiar with ECG and commonly used processing methods. Various transformation methods of ECG leads. Further we will describe methods of delineation ECG signals. Second part is dedicated to metod used in this work for delineation of ECG signals from CSE database. Particulary method using Dyadic Wavelet Transform. Work include, description of used program and results. Third part is dedicated to methods of ECG leads transformations and cluster analysis. In conclusion we will evaluate the results.

Technology development in the sub-field of older adult care has always been on the back-burner compared to other healthcare areas. But with increasing life expectancy, this is poised to change. With the increasing older adult population, the current older adult care facilities and personnel are struggling to keep up with demand. Research conducted in the Netherlands [1] found 33,000 older adults were awaiting admission into a home for the elderly showing that demand far exceeds availability. This huge demand for older adult care has resulted in a decrease in the quality of care being provided. A recent study involving older adults aged 65 and above [2] compared the quality of care given to older adults in nursing homes in the UK and found it to be inadequate. While it is true that giant strides have been made in the field of personal health and fitness [3], we have to acknowledge that these technologies have not found widespread adoption in the elderly communities for a number of reasons which include lack of education, cognitive impediments, low-income and techno-phobia [4]. We believe that older adult care technologies should be approached from a different perspective in order to maximize outcomes. Inventions in the health care space are a moving target and a significant degree of technical aptitude and interest is required to keep up with these changes. My research work will be focused on developing a distributed system infrastructure that will enable large-scale monitoring of vital signals and early detection of emergency situations in nursing homes and assisted living communities. This new approach will increase automation in nursing homes leading to a reduction in running cost and an increase in capacity

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-04-17T11:57:59Z No. of bitstreams: 1 Alfredo Costa Oliveira Júnior.pdf: 789446 bytes, checksum: c5c9858983f5e6384177bda8d1ae2a0a (MD5) / Made available in DSpace on 2017-04-17T11:57:59Z (GMT). No. of bitstreams: 1 Alfredo Costa Oliveira Júnior.pdf: 789446 bytes, checksum: c5c9858983f5e6384177bda8d1ae2a0a (MD5) Previous issue date: 2017-02-16 / Atrial fibrilation(AF) is one of the most common cardiac arrhythmias worldwide. Thus, there are ample efforts to implement AF diagnosis systems. The main noninvasive way to assess cardiac health is through electrocardiogram (ECG) signal analysis, which represents the electrical activity of the cardiac muscle, and has characteristic temporal markings: P, Q, R, S and T waves. Some authors use filtering techniques, statistical analysis and even neural networks for detecting AF based on the RR interval, that is given by the temporal difference between the peaks of the R wave. However, analises of the RR interval allows for evaluating changes occurring only in the R wave of the ECG signal, not allowing to assess, for example, variations in the P wave provoked by the AF. In face of that, we propose characterize the ECG signal amplitude aiming at classifying both healthy and AF patients. The ECG signal was analyzed in the proposed methodology through the following statistics: variance, asymmetry, and kurtosis. Herein, we use the MIT-BIH Atrial Fibrillation and MIT-BIH Normal Sinus Rhythm database signals to evaluate AF and normal heartbeat intervals. Our study shown that kurtosis outperfomed variance and asymmetry with respect to sensibility (Se = 100%), specificity (Sp = 88.33%) and accuracy (Ac = 91.33%). The results were expected since kurtosis is a non-Gaussian measure and the ECG signal has sparse distribution. The proposed methodology also requires a lower number of pre-processing stages, and its simplicity allows for implementations in imbedded systems supporting the clinical diagnosis. / A Fibrilação atrial (FA) é uma das arritmias cardíacas mais comuns em todo o mundo. Por isso, amplos são os esforços para implementar sistemas que apoiem o diagnóstico de FA. A principal forma não invasiva de avaliar a saúde cardíaca, é através da análise do sinal de eletrocardiograma (ECG), o qual representa a atividade elétrica do músculo cardíaco, e possui marcações temporais características: as ondas P, Q, R, S e T. Alguns autores utilizaram técnicas de filtragem, análise estatística e até redes neurais para detectar FA com base no intervalo RR, que é dado pela diferença temporal entre os picos da onda R. Entretanto, a análise do intervalo RR permite avaliar apenas as variações que ocorrem na onda R do sinal de ECG, não permitindo avaliar, por exemplo, as alterações na onda P, provocadas pela FA. Diante disso, propõe-se caracterizar a amplitude do sinal de ECG, a fim de classificar pacientes com FA e saudáveis. Na metodologia proposta, o sinal de ECG, foi analisado por meio das seguintes estatísticas: variância, assimetria e curtose. Para avaliar o classificador proposto, usou-se sinais obtidos das bases de dados MIT-BIH Atrial Fibrillation e MIT-BIH Normal Sinus Rhythm referentes aos pacientes com FA e com ritmo cardíaco normal, respectivamente. Dentre as estatísticas analidadas, a curtose foi a que apresentou resultados superiores em termos de sensibilidade (Se = 100%), especificidade (Sp = 88, 33%) e acurácia (Ac = 91, 33%). Esses resultados são de se esperar pelo fato de que a curtose é uma medida de não-gaussianidade e que o sinal de ECG possui distribuição esparsa. A metodologia proposta também requer um número menor de etapas de pré-processamento, e sua simplicidade permite implementações em sistemas embarcados que apoiarão o diagnóstico clínico.