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Comparison Of Hilbert Transform And Derivative Methods For Converting Ecg Data Into Cardioid Plots To Detect Heart Abnormalities, Robert George Goldie
Comparison Of Hilbert Transform And Derivative Methods For Converting Ecg Data Into Cardioid Plots To Detect Heart Abnormalities, Robert George Goldie
Master's Theses
Electrocardiogram (ECG) time-domain signals contain important information about the heart. Several techniques have been proposed for creating a two-dimensional visualization of an ECG, called a Cardioid, that can be used to detect heart abnormalities with computer algorithms. The derivative method is the prevailing technique, which is popular for its low complexity, but it can introduce distortion into the Cardioid plot without additional signal processing. The Hilbert transform is an alternative method which has unity gain and phase shifts the ECG signal by 90 degrees to create the Cardioid plot. However, the Hilbert transform is seldom used and has historically been …
Portable Electrocardiogram Device And Signal Processing Design, Ryan F. Blaalid
Portable Electrocardiogram Device And Signal Processing Design, Ryan F. Blaalid
Electrical Engineering
Full 12-lead electrocardiogram (ECG) measurements require inconvenient and time consuming adhesive electrode placement. This project proposes a design for a Bluetooth based ECG for remote patient measurement. The device is designed to measure up to 6-leads and utilizes 5 dry (non-adhesive) electrodes to accomplish this. The device delivers the ECG data to the user’s mobile smart phone and can then be sent to the patient’s doctor for analysis. Since the contact of the dry electrodes to the skin is not perfect, low-frequency noise called baseline wandering is introduced. A signal processing technique borrowed from image processing called morphological filtering is …
Ecg Classification With An Adaptive Neuro-Fuzzy Inference System, Brad Thomas Funsten
Ecg Classification With An Adaptive Neuro-Fuzzy Inference System, Brad Thomas Funsten
Master's Theses
Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System …
An Approach Based On Wavelet Decomposition And Neural Network For Ecg Noise Reduction, Suranai Poungponsri
An Approach Based On Wavelet Decomposition And Neural Network For Ecg Noise Reduction, Suranai Poungponsri
Master's Theses
Electrocardiogram (ECG) signal processing has been the subject of intense research in the past years, due to its strategic place in the detection of several cardiac pathologies. However, ECG signal is frequently corrupted with different types of noises such as 60Hz power line interference, baseline drift, electrode movement and motion artifact, etc. In this thesis, a hybrid two-stage model based on the combination of wavelet decomposition and artificial neural network is proposed for ECG noise reduction based on excellent localization features: wavelet transform and the adaptive learning ability of neural network. Results from the simulations validate the effectiveness of this …