Beneath the surface electrocardiogram: computer algorithms for the non-invasive assessment of cardiac electrophysiology
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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.