Evaluating Smartwatch Detection of Atrial Fibrillation
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Smartwatches, such as the Apple Watch, are increasingly used for monitoring and
detecting serious health conditions like atrial fibrillation (AF), a common arrhythmia
with significant health risks. However, to avoid burdening the healthcare sector
with false alarms, their detection algorithms are designed to minimize false positives,
a choice that may compromise their sensitivity to sparse or intermittent AF.
While previous studies have analyzed the specificity of these alerts, the rate of false
negatives remains underreported. This thesis aims to quantify this performance
gap by evaluating the Apple Watch’s photoplethysmography-based irregular-pulse
notification and its rate of false negatives. Using a large clinical ambulatory ECG
dataset, we modeled and simulated long-term AF episode patterns via two distinct
stochastic methods: a two-state discrete-time Markov chain and a continuous-time
alternating bivariate Hawkes process. Analysis of the simulated data revealed that
a significant proportion of individuals, particularly those with low-to-moderate AF
burden (0.5%–5.5%), risk going undetected for extended periods. For example, for
a burden of 0.5%–1.5%, over 20% of cases remained undetected after five years, and
for a burden of 3.5%–4.5%, over 5% remained undetected after five years of monitoring.
The findings, consistent across both modeling approaches, demonstrate that
detection is highly dependent on the temporal pattern of AF episodes and not just
the AF burden. They also suggest that the Apple Watch algorithm has reduced sensitivity
to certain AF episode patterns, and users should be cautious not to interpret
the absence of an alert as confirmation of a normal rhythm.
Beskrivning
Ämne/nyckelord
Atrial fibrillation, Smartwatch, Markov chain, Hawkes process