The surface electrocardiogram (ECG) is a periodic signal portraying the electrical activity of the heart from the torso. The past fifty years have witnessed a proliferation of computer algorithms destined for ECG analysis. Signal averaging is a noise reduction technique believed to enable the surface ECG to act as a non-invasive surrogate for cardiac electrophysiology.
The P wave and the QRS complex of the ECG respectively depict atrial and ventricular depolarization. QRS detection is a pre-requisite to P wave and QRS averaging. A novel algorithm for robust QRS detection in mice achieves a four-fold reduction in false detections compared to leading commercial software, while its human version boasts an error rate of just 0.29% on a public database containing ECGs with varying morphologies and degrees of noise.
A fully automated P wave and QRS averaging and onset/offset detection algorithm is also proposed. This approach is shown to predict atrial fibrillation, a common cardiac arrhythmia which could cause stroke or heart failure, from normal asymptomatic ECGs, with 93% sensitivity and 100% specificity. Automated signal averaging also proves to be slightly more reproducible in consecutive recordings than manual signal averaging performed by expert users.
Several studies postulated that high-frequency energy content in the signal-averaged QRS may be a marker of sudden cardiac death. Traditional frequency spectrum analysis techniques have failed to consistently validate this hypothesis.
Layered Symbolic Decomposition (LSD), a novel algorithmic time-scale analysis approach requiring no basis function assumptions, is presented. LSD proves more reproducible than state-of-the-art algorithms, and capable of predicting sudden cardiac death in the general population from the surface ECG with 97% sensitivity and 96% specificity.
A link between atrial refractory period and high-frequency energy content of the signal-averaged P wave is also considered, but neither LSD nor other algorithms find a meaningful correlation.
LSD is not ECG-specific and may be effective in countless other signals with no known single basis function, such as other bio-potentials, geophysical signals, and socio-economic trends. / Thesis (Ph.D, Computing) -- Queen's University, 2013-09-30 23:54:21.137
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OKQ.1974/8392 |
| Date | 03 October 2013 |
| Creators | Torbey, Sami |
| Contributors | Queen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.)) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner. |
| Relation | Canadian theses |
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