Abstract Short QT syndrome is an inherited arrhythmia disorder characterized by a short QT interval, typical T-wave and ST-segment morphology and an increased risk of sudden cardiac death. The purpose of this thesis was to study the epidemiology and prognosis of the subjects with short QT intervals. Special attention was paid to the ECG changes that could illustrate the prognosis of subjects with short QT interval. The first study comprised a group of patients with short QT syndrome. We report clinical presentation, ECG morphology, the prevalence of genetic mutations and the results of therapies in this group of patients. The second study population consisted of 10 822 randomly selected middle-aged subjects followed up for 29 ± 10 years. QT intervals were measured using three correction methods for the heart rate in order to assess the prevalence and prognosis of those subjects with short QT intervals. The third population consisted of three patients with short QT syndrome and nine controls. Holter recordings were analyzed to compare transmural dispersion of repolarization between patients and controls and also to study their capability to change repolarization indexes from baseline to maximal values. In the fourth study ECGs from 10 patients with short QT syndrome were compared with ECGs of 12 asymptomatic subjects with short QT intervals. The aim was to find ECG abnormalities that would predict the outcome of the patients. We found 62% of patients to be symptomatic, 34% had cardiac arrest. Atrial fibrillation was common. Most of the patients received an ICD or were placed on hydroquinidine. The prevalence of QTc < 320ms was 0.10% and QTc < 340ms was 0.4%, respectively. Mortality or other serious symptoms did not differ between subjects and controls. We also found that the TPE/QT ratio as an index for abnormal transmural dispersion of repolarization was high compared to controls. Short QT syndrome patients had also lesser capacity to change the QT interval, indicating blunted autonomic response in SQTS. Ten SQTS patients had significantly shorter Jpoint–Tpeak interval and higher TPE/QT ratio compared to controls. In conclusion, shorter than normal QT interval might represent a novel short QT syndrome. However, in the general community short QT interval can reflect only the extreme end of the normal Gaussian distribution of QT intervals and these subjects carry a good prognosis. TPE/QT ratio and Jpoint–Tend intervals can be used as risk stratifiers in subjects with short QT intervals.

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Advanced computer technologies can transform the traditional electrocardiogram (ECG) monitoring system for better efficiency and accuracy. ECG records a heart's electrical activity using electrodes placed on the skin, and it has become an essential tool for arrhythmia detection. The complexity comes from the variety of patients' heartbeats and massive amounts of information for humans to process correctly. The first part of the thesis presents an image based two-dimensional convolution neural network (CNN) to classify the arrhythmia heartbeats with inter-patient paradigm. It includes a new data pre-processing method. The inter-patient paradigm simulates the practical use case of an ECG heartbeat classifier. Compared to the reported work in the literature, the proposed solution achieves superior experiment results. The rest of the thesis introduces the remote ECG monitoring system. The RESTful API design concepts of the system are described. The proposed API supports an efficient and secure way of interaction between each module in this remote monitoring system. / Graduate

Biometrics is the study of physical or behavioral traits that establishes the identity of a person. Forensics, physical security and cyber security are some of the main fields that use biometrics. Unlike traditional authentication systems—such as password based—biometrics cannot be lost, forgotten or shared. This is possible because biometrics establishes the identity of a person based on a physiological/behavioural characteristic rather than what the person possess or remembers. Biometrics has two modes of operation: identification and authentication. Identification finds the identity of a person among a group of persons. Authentication determines if the claimed identity of a person is truthful. Biometric person authentication is an alternative to passwords or graphical patterns. It prevents shoulder surfing attacks, i.e., people watching from a short distance. Nevertheless, biometric traits of conventional authentication techniques like fingerprints, face—and to some extend iris—are easy to capture and duplicate. This denotes a security risk for modern and future applications such as digital twins, where an attacker can copy and duplicate a biometric trait in order to spoof a biometric system. Researchers have proposed ECG as biometric authentication to solve this problem. ECG authentication conceals the biometric traits and reduces the risk of an attack by duplication of the biometric trait. However, current ECG authentication solutions require 10 or more seconds of an ECG signal in order to have accurate results. The accuracy is directly proportional to the ECG signal time-length for authentication. This is inconvenient to implement ECG authentication in an end-user product because a user cannot wait 10 or more seconds to gain access in a secure manner to their device. This thesis addresses the problem of spoofing by proposing an accurate and secure ECG biometric authentication system with relatively short ECG signal length for authentication. The system consists of an ECG acquisition from lead I (two electrodes), signal processing approaches for filtration and R-peak detection, a feature extractor and an authentication process. To evaluate this system, we developed a method to calculate the Equal Error Rate—EER—with non-normal distributed data. In the authentication process, we propose an approach based on Support Vector Machine—SVM—and achieve 4.5% EER with 4 seconds of ECG signal length for authentication. This approach opens the door for a deeper understanding of the signal and hence we enhanced it by applying a hybrid approach of Convolutional Neural Networks—CNN—combined with SVM. The purpose of this hybrid approach is to improve accuracy by automatically detect and extract features with Deep Learning—in this case CNN—and then take the output into a one-class SVM classifier—Authentication; which proved to outperform accuracy for one-class ECG classification. This hybrid approach reduces the EER to 2.84% with 4 seconds of ECG signal length for authentication. Furthermore, we investigated the combination of two different biometrics techniques and we improved the accuracy to 0.46% EER, while maintaining a short ECG signal length for authentication of 4 seconds. We fuse Fingerprint with ECG at the decision level. Decision level fusion requires information that is available from any biometric technique. Fusion at different levels—such as feature level fusion—requires information about features that are incompatible or hidden. Fingerprint minutiae are composed of information that differs from ECG peaks and valleys. Therefore fusion at the feature level is not possible unless the fusion algorithm provides a compatible conversion scheme. Proprietary biometric hardware does not provide information about the features or the algorithms; therefore, features are hidden and not accessible for feature level fusion; however, the result is always available for a decision level fusion.

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.

The semestral thesis describes ECG signal compression methods designed to modify the data for transmission via communication channels. The thesis contains an introduction to Ethernet and explanation of communication in the network. The transport protocols TCP and UDP are discussed in more detail. In the practical part of the thesis was created two separate applications. The first application in the sender's computer opens a text file with the ECG signal. Loaded ECG signal is filtered by cascade of filters to eliminate interference. The resulting signal is displayed. A part of the application is the R wave detection, calculating the length of RR interval and heart rate. The application also allows to compress an ECG signal. ECG signal is sent via Ethernet network via UDP protocol for individual samples. Applications in the recipient's computer receives signal samples from the network. Recieved compressed data is reconstructed. The resulting ECG signal is displayed and there are again detected R waves, the length of RR intervals and sampling frequency are calculated.

This study was carried out to determine the usefulness of standard electrocardiography for the diagnosis of early canine heartworm disease. Baseline electrocardiograms were recorded in 12 dogs. Thirty artificial Dirofilaria immitis worms were inserted in the pulmonary artery of each dog. New electrocardiograms were recorded on days 1-4, 7, 14, 21, 28 and 35 after insertion of worms. A significant attenuation of amplitude of Q wave in lead I was recorded on days 1-4, 7, 14, 21, 28 and 35; of R wave in lead II on days 21, 28 and 35; and of S wave in lead aVR on days 7, 14, 21, 28 and 35. Duration of the QRS complex in lead AVF was significantly prolonged on day 14. In 41.6% of the dogs, right axis deviation was recorded. These results suggest that for a dog whose normal baseline standard electrocardiogram is known, a comparison of the QRS complex of the baseline with those of subsequent standard electrocardiograms may suggest early canine heartworm disease when there is an attenuation of amplitude of Q wave in lead I, R wave in lead II and S wave in lead aVR. An additional indication can be right axis deviation.

An ECG (electrocardiogram) simulator is an electronic tool that plays an essential role in the testing, design, and development of ECG monitors and other ECG equipment. Principally an ECG simulator provides ECG monitors with an electrical signal that emulates the human heart's electrical signal so that the monitor can be tested for reliability and important diagnostic capabilities. However, the current portable commercially available ECG simulators are lacking in their ability to fully test ECG monitors. Specifically, the portable simulators presently on the market do not produce authentic ECG signals but rather they endeavor to create the ECG signals mathematically. They even attempt to mathematically create arrhythmias (irregular heartbeats of which there are many different types). Arrhythmia detection is an important capability for any modern ECG monitor because arrhythmias are often the critical link to the diagnosis of heart conditions or cardiovascular disease. The focus of this thesis is the design and implementation of a portable ECG simulator. The important innovation of this prototype simulator is that it will not create its ECG signals mathematically, but rather it will store ECG data files on a memory module and use this data to produce an authentic ECG signal. The data files will consist of different types of ECG signals including different types of arrhythmias. The data files are obtained via the internet and require formatting and storing onto a memory chip. These files are then processed by a digital to analog converter and output on a four lead network to produce an authentic ECG signal. The system is built around the ultra-low power Texas Instruments MSP430 microcontroller.

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 implemented with a computationally expensive process. In this thesis we show a low-complexity method for implementing the Hilbert transform as a finite impulse response (FIR) filter. We compare the fundamental differences between Cardioid plots generated with the derivative and Hilbert transform methods and demonstrate the feature-preserving nature of the Hilbert transform method. Finally, we analyze the RMS values of the transformed signals to show how the Hilbert transform method can create near 1:1 aspect ratio Cardioid plots with very little distortion for any patient data.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.