Key result
A computational method utilizing a reaction-diffusion system and Monte Carlo Markov chain successfully retrieved hidden atrial repolarization waves from standard 12-lead surface ECGs.
A novel computational method using Bayesian estimation and reaction-diffusion models successfully extracts hidden atrial repolarization waves from standard 12-lead ECGs, potentially aiding in the non-invasive diagnosis of atrial pathologies.
BACKGROUND: This study estimates atrial repolarization activities (Ta waves), which are typically hidden most of the time from body surface electrocardiography when diagnosing cardiovascular diseases. The morphology of Ta waves has been proven to be an important marker for the early sign of inferior injury, such as acute atrial infarction, or arrhythmia, such as atrial fibrillation. However, Ta waves are usually unseen except during conduction system malfunction, such as long QT interval or atrioventricular block. Therefore, justifying heart diseases based on atrial repolarization becomes impossible in sinus rhythm. METHODS: We obtain TMPs in the atrial part of the myocardium which reflects the correct excitation sequence starting from the atrium to the end of the apex. RESULTS: The resulting TMP shows the hidden atrial part of ECG waves. CONCLUSIONS: This extraction makes many diseases, such as acute atrial infarction or arrhythmia, become easily diagnosed.
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Tang et al. (2018) studied Healthy (n=1). Computational extraction method (reaction-diffusion system and MCMC) was evaluated on Retrieval of hidden atrial repolarization waves (Ta waves). A computational method utilizing a reaction-diffusion system and Monte Carlo Markov chain successfully retrieved hidden atrial repolarization waves from standard 12-lead surface ECGs.
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