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Atrial fibrillation (AF) is a global epidemic associated with significant cardiovascular comorbidity and mortality, with resultant burden on healthcare systems worldwide. However, silent or undetected AF is common, especially in the older population. Early detection and diagnosis of AF is therefore paramount to provide comprehensive management, to manage symptoms, slow progression of the disease, prevent severe complications like stroke and heart failure, reduce AF-related hospitalisations and possibly improve survival.
Arrhythmia documentation via 12-lead ECG recording is considered the gold standard to confirm an AF diagnosis. While this is of significant yield in those with persistent forms of the arrhythmia, those with paroxysmal or asymptomatic AF pose a greater challenge. In such scenarios, other forms of screening or monitoring may thus be warranted. In this context the European Society of Cardiology (ESC) guidelines for the management of AF recommend opportunistic screening for AF in all patients >65 years by pulse palpation or ECG recording.1 However, the proportion of incident AF yielded by opportunistic screening is relatively low, and paroxysmal AF is often missed. Besides invasive devices to monitor the heart rhythm, other methods have been developed to detect arrhythmias, such as smartphone applications, smart watches and wearable fitness trackers, which make use of photoplethysmography (PPG)—digital pulse waveforms—to detect irregularities in the heart rhythm.
In their Heart paper, Poh et al 2 report on their findings using a deep neural learning system for automated detection of AF in PPG pulse waveforms. Deep neural systems use machine learning algorithms, generating information based on statistical data modelling. This has demonstrated high accuracy in performing pattern recognition, …
Contributors All the authors have contributed to drafting and reviewing the manuscript.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
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Provenance and peer review Commissioned; internally peer reviewed.
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