Real-time Algorithm for Detection of Atrial Fibrillation

Motorina, S. V.; Kalinichenko, A. N.

doi:10.1007/s10527-016-9610-6

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…It is characterized by highly irregular and ally on more conventional statistical discriminators of the heart rate irregularity. For instance, the root mean square of successive differences (RMSSD), median absolute deviation (MAD) or features based on the turning points of a sequence Motorina & Kalinichenko (2016); Kennedy et al (2016); Dash et al (2009); Czabanski et al (2020) and Pal et al (2016). Overall, the research indicates that the combination of linear features, such as RMSSD and MAD, and non-linear, such as the largest Lyapunov exponent (LLE) lead to promising prediction performances.…”

Section: Motivationmentioning

confidence: 99%

Atrial fibrillation detection using heart rate variability and atrial activity: A hybrid approach

Hirsch

Jensen

Poulsen³

et al. 2021

Expert Systems with Applications

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Section: Motivationmentioning

confidence: 99%

Atrial fibrillation detection using heart rate variability and atrial activity: A hybrid approach

Hirsch

Jensen

Poulsen³

et al. 2021

Expert Systems with Applications

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A Two-Stage Algorithm for Detection of Atrial Fibrillation

Motorina

Kalinichenko

2018

Biomed Eng

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Episode‐level and clinical characterization of asymptomatic atrial fibrillation events

Ahluwalia,

Majumder,

Koehler

et al. 2024

Cardiovasc electrophysiol

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IntroductionNot all patients experience debilitating symptoms during Atrial Fibrillation (AF), some are asymptomatic. The reasons for this inter‐ and intrasubject variability is unknown.PurposeThe study objective was NOAH characterize episode‐level and clinical characteristics associated with symptomatic versus asymptomatic episodes of AF in patients with an implantable cardiac monitor (ICM).MethodsPatients with an AF episode detected on an ICM between 2007 and 2021 with overlapping clinical data from aggregated Electronic Health Records in the Optum® deidentified data set were included. Symptomatic episodes were labeled in real‐time by the patient. Heart rate (HR) at onset, mean HR, AF Evidence Score (a measure of beat‐to‐beat irregularity), episode duration and Activity Index were evaluated for association with symptom status using multivariable regression modeling.Results11 267 patients had AF episodes with clinical data available. The 1776 (15.8%) patients who reported symptomatic AF episodes were younger (67 ± 12 years vs. 71 ± 11 years old, p < .001) and had fewer cardiovascular co‐morbidities than patients with asymptomatic AF exclusively. Symptomatic episodes were longer (5.5 [2.4, 14.4] h vs. 3.7 [1.7, 11] h, p < .001), had higher mean HR (103 ± 22 bpm vs. 88 ± 22 bpm, p < .001) and higher AF evidence scores (98 ± 27 vs. 82 ± 24, p < .001). These features were independently associated with symptomatic episodes on multivariable regression analysis and per‐subject analysis in patients who had both symptomatic and asymptomatic episodes.DiscussionEpisode‐level characteristics differed between symptomatic AF episodes versus asymptomatic episodes in patients with ICMs. Symptomatic patients also had less comorbidities. These parameters may be useful in understanding variable symptomatic manifestation and remote stratification of AF episodes.

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Real-time Algorithm for Detection of Atrial Fibrillation

Cited by 3 publications

References 4 publications

Atrial fibrillation detection using heart rate variability and atrial activity: A hybrid approach

Atrial fibrillation detection using heart rate variability and atrial activity: A hybrid approach

A Two-Stage Algorithm for Detection of Atrial Fibrillation

Episode‐level and clinical characterization of asymptomatic atrial fibrillation events

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