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<p dir="ltr">Cardiac disease is one of the prominent reasons of deaths worldwide. The timely de-<br>tection of arrhythmias, one of the highly prevalent cardiac abnormalities, is very important<br>and promising for treatment. Electrocardiography (ECG) is well applied to probe the car-<br>diac dynamics, nevertheless, it is still challenging to robustly detect the arrhythmia with<br>automatic algorithms, especially when the noise may contaminate the signal to some extent.<br>In this research study, we have not only built and assessed different neural network models<br>to understand their capability in terms of ECE-based arrhythmia detection, but also com-<br>prehensively investigated the detection under different kinds of signal-to-noise ratio (SNR).<br>Both Long Short-Term Memory (LSTM) model and Multi-Layer Perception (MLP) model<br>have been developed in the study. Further, we have studied the necessity of fine-tuning<br>of the neural network models, which are pre-trained on other data and demonstrated that<br>it is very important to boost the performance when ECG is contaminated by noise. In<br>the experiments, the LSTM model achieves an accuracy of 99.0%, F1 score of 97.9%, and<br>high precision and recall, with the clean ECE signal. Further, in the high SNR scenario,<br>the LSTM maintains an attractive performance. With the low SNR scenario, though there<br>is some performance drop, the fine-tuning approach helps performance improvement criti-<br>cally. Overall, this study has built the neural network models, and investigated different<br>kinds of signal fidelity including clean, high-SNR, and low-SNR, towards robust arrhythmia<br>detection.</p>

This work deals with the possibilities of filtering the ECG signal, representing the first part, which is the basis for successful delineation and follow diagnosis of the ECG signal. Filtration in this case is mean to suppress interference from electrical grid, noise of electrical grid. The content of the work is description of filters realized trough wavelet transform and linear filtering as a means to successful filtration of interference. There are method of stationary wavelet transform - dyadic wavelet transform, wavelet packet transform and wavelet wiener filtering method. Linear filtering includes two narrow-band FIR filters. The objective of this work is to propose different methods of wavelet and linear filters in Matlab, filtering of ECG signals and compare the success of filtration methods. ECG signals used in this work are from the CSE database.

The aim of this master’s thesis is to create a multiband stop derived from Lynn filters for suppressing mains hum and baseline variation (drift). The first part of the thesis is focused on brief theoretical introduction to the distortion types affecting ECG signal and twelve lead connection. The following practical part describes free realizations of ECG filter and ECG signal filtration. The filter has been tested both on distorted and on non-distorted signal. Finally filters’ error rate was computed from CSE database signals.

Submitted by Cristiane Chim (cristiane.chim@ucpel.edu.br) on 2018-05-08T17:34:22Z No. of bitstreams: 1 Vagner Guidotti Furtado (1).pdf: 2942442 bytes, checksum: a43c18ecb28456284d4b6c622f11210d (MD5) / Made available in DSpace on 2018-05-08T17:34:22Z (GMT). No. of bitstreams: 1 Vagner Guidotti Furtado (1).pdf: 2942442 bytes, checksum: a43c18ecb28456284d4b6c622f11210d (MD5) Previous issue date: 2017-12-07 / The division operation in digital systems has its relevance because it is a necessary function in several applications, such as general purpose processors, digital signal processors and microcontrollers. The digital divider circuit is of great architectural complexity and may occupy a considerable area in the design of an integrated circuit, and as a consequence may have a great influence on the static and dynamic power dissipation of the circuit as a whole. In relation to the application of dividing circuits in circuits of the Digital Signal Processing (DSP) area, adaptive filters have a particular appeal, especially when using algorithms that perform a normalization in the input signals. In view of the above, this work focuses on the proposition of algorithms, techniques for reducing energy consumption and logical area, proposition and implementation of efficient dividing circuit architectures for use in adaptive filters. The Newton-Raphson and Goldschmidt iterative dividing circuits both operating at fixed-point were specifically addressed. The results of the synthesis of the implemented architectures of the divisors with the proposed algorithms and techniques showed considerable reduction of power and logical area of the circuits. In particular, the dividing circuits were applied in adaptive filter architectures based on the NLMS (Normalized least Mean Square) algorithm, seeking to add to these filters, characteristics of good convergence speed, combined with the improvement in energy efficiency. The adaptive filters implemented are used in the case study of harmonic cancellation on electrocardiogram signals / A operação de divisão em sistemas digitais tem sua relevância por se tratar de uma função necessária em diversas aplicações, tais como processadores de propósito geral, processadores digitais de sinais e microcontroladores. O circuito divisor digital é de grande complexidade arquitetural, podendo ocupar uma área considerável no projeto de um circuito integrado, e por consequência pode ter uma grande influência na dissipação de potência estática e dinâmica do circuito como um todo. Em relação à aplicação de circuitos divisores em circuitos da área DSP (Digital Signal Processing), os filtros adaptativos têm um particular apelo, principalmente quando são utilizados algoritmos que realizam uma normalização nos sinais de entrada. Diante do exposto, este trabalho foca na proposição de algoritmos, técnicas de redução de consumo de energia e área lógica, proposição e implementação de arquiteturas de circuitos divisores eficientes para utilização em filtros adaptativos. Foram abordados em específico os circuitos divisores iterativos Newton-Raphson e Goldschmidt ambos operando em ponto-fixo. Os resultados da síntese das arquiteturas implementadas dos divisores com os algoritmos e técnicas propostas mostraram considerável redução de potência e área lógica dos circuitos. Em particular, os circuitos divisores foram aplicados em arquiteturas de filtros adaptativos baseadas no algoritmo NLMS (Normalized least Mean Square), buscando agregar a esses filtros, características de boa velocidade de convergência, aliada à melhoria na eficiência energética. Os filtros adaptativos implementados são utilizados no estudo de caso de cancelamento de harmônicas em sinais de eletrocardiograma (ECG)

This thesis deals with the issue of automatic measurement of the duration of QRS complexes in ECG signals. Special emphasis is then placed on the possibility of automatic detection of QRS complexes while exciting cardiac tissue with a pacemaker. The content of this work is divided into four logical units, where the first part deals with the heart as an organ. It describes the origin and spread of excitement in the heart, its possible pathologies and their manifestations in ECG recording, it also deals with pacing and measuring ECG recording during simultaneous pacing. The second part of the thesis contains a brief introduction to the topic of machine and deep learning. The third part of the thesis contains a search of current approaches using methods based on deep learning to solve the detection of QRSd. The fourth part deals with the design and implementation of its own model of deep learning, able to detect the beginnings and ends of QRS complexes from ECG recordings. It describes the data preprocessing implemented in the MATLAB programming environment. The actual implementation of the model was performed in the Python using the PyTorch and NumPy moduls.

This thesis deals with the issue of automatic measurement of the duration of QRS complexes in ECG signals. Special emphasis is then placed on the possibility of automatic detection of QRS complexes while exciting cardiac tissue with a pacemaker. The content of this work is divided into four logical units, where the first part deals with the heart as an organ. It describes the origin and spread of excitement in the heart, its possible pathologies and their manifestations in ECG recording, it also deals with pacing and measuring ECG recording during simultaneous pacing. The second part of the thesis contains a brief introduction to the topic of machine and deep learning. The third part of the thesis contains a search of current approaches using methods based on deep learning to solve the detection of QRSd. The fourth part deals with the design and implementation of its own model of deep learning, able to detect the beginnings and ends of QRS complexes from ECG recordings. It describes the data preprocessing implemented in the MATLAB programming environment. The actual implementation of the model was performed in the Python using the PyTorch and NumPy moduls.

This diploma thesis deals with location optimal wavelet for detecton charakterics points of QRS complex in ECG signals. The first part of this thesis deals with description of heart, genesis of electric signals on heart and problem of noise. The second part describes the wavelet transform and the designed program and the third part evaluate detection results. The created program is working with 10 ECG signals from the CSE database and is testing 12 different mother wavelets. The program was developed in Matlab environment and is based on the finding zero-points in the transformed signal.

The main aim of this thesis is to introduce issue of averaging of biological signals. The first part of the thesis deals with the principles of individual averaging methods (constant, floating and exponential window) and describes their basic features. Moreover, the principle of filtered residue, detection of QRS complex, and stretching/shrinking the length of RR-interval to the standardized length are explicated. In the second part of the thesis the outcomes of practically realized (Matlab and GUI) methods of averaging (by final signal-to-noise ratio) are evaluated. Signals from MIT-BIH database are used.

This master's thesis deals with the optimization of numerical coefficients of the Wiener filter for muscle noise filtering in ECG signals. The theoretical part deals with ECG signal characteristic and muscle interference. It also contains a summary of the wavelet transform, wavelet Wiener's filtration, methods for calculating of the threshold and thresholding. In the last theoretical part the characteristic optimization techniques, the exhausive search and Nelder-Mead simplex method are mentioned, which were implemented in the practical part of this thesis in MATLAB. The functional verification and Wiener's filter optimization were tested on the standard electrocardiograms database CSE. By using the methods of exhausive search, the initial estimate for the solution method Nelder-Mead was obtained. The optimization method Nelder-Mead gives better results in the orders of hundredths or tenths than the method of exhausive search. The practical part is finished by the comparison of results of implemented algorithm with optimum coefficients, implemented in this thesis, with the results of other methods for filtering muscle interference in ECG signals.