Atrial Fibrillation detection by a subcutaneous monitoring device

Hindricks, Gerhard; Táborský, Miloš; Wohlgemuth, Peter; Rieger, G.; Beckers, Frank; Albers, Bettina

doi:10.1109/cic.2008.4749066

Cited by 2 publications

(3 citation statements)

References 3 publications

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“…Supraventricular premature beats, supraventricular tachycardia, and other kinds of arrhythmias may be confused with AF detection. G Hindricks et al have exploited a subcutaneous insertable cardiac monitor device to detect AF [51]. The device is optimized for the subcutaneous R-wave sensing [52], and classification of all cardiac rhythms is based on the captured RR intervals.…”

Section: Application Of Univariate Data Analysis On Rr Time Seriesmentioning

confidence: 99%

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Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data

Haddi¹,

Ananou

Alfaras³

et al. 2022

Algorithms

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Atrial fibrillation (AF) is still a major cause of disease morbidity and mortality, making its early diagnosis desirable and urging researchers to develop efficient methods devoted to automatic AF detection. Till now, the analysis of Holter-ECG recordings remains the gold-standard technique to screen AF. This is usually achieved by studying either RR interval time series analysis, P-wave detection or combinations of both morphological characteristics. After extraction and selection of meaningful features, each of the AF detection methods might be conducted through univariate and multivariate data analysis. Many of these automatic techniques have been proposed over the last years. This work presents an overview of research studies of AF detection based on RR interval time series. The aim of this paper is to provide the scientific community and newcomers to the field of AF screening with a resource that presents introductory concepts, clinical features, and a literature review that describes the techniques that are mostly followed when RR interval time series are used for accurate detection of AF.

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Section: Application Of Univariate Data Analysis On Rr Time Seriesmentioning

confidence: 99%

“…G Hindricks et al have exploited a subcutaneous insertable cardiac monitor device to detect AF [51]. The device is optimized for the subcutaneous R-wave sensing [52], and classification of all cardiac rhythms is based on the captured RR intervals.…”

Section: Application Of Multivariate Data Analysis On Rr Time Seriesmentioning

confidence: 99%

Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data

Haddi¹,

Ananou

Alfaras³

et al. 2022

Algorithms

View full text Add to dashboard Cite

show abstract

“…To capture these occasional episodes, patients can be fitted with event monitors, which can be worn for a month or longer [9]. However, event monitors typically only save ECG data around the irregular cardiac episode, which limits the degree of analysis that can be performed.…”

Section: Introductionmentioning

confidence: 99%

A Wearable Cardiac Monitor for Long-Term Data Acquisition and Analysis

Winokur

Delano

Sodini

2013

IEEE Trans. Biomed. Eng.

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A low-power wearable ECG monitoring system has been developed entirely from discrete electronic components and a custom PCB. This device removes all loose wires from the system and minimizes the footprint on the user. The monitor consists of five electrodes, which allow a cardiologist to choose from a variety of possible projections. Clinical tests to compare our wearable monitor with a commercial clinical ECG recorder are conducted on ten healthy adults under different ambulatory conditions, with nine of the datasets used for analysis. Data from both monitors were synchronized and annotated with PhysioNet's waveform viewer WAVE (physionet.org) [1]. All gold standard annotations are compared to the results of the WQRS detection algorithm [2] provided by PhysioNet. QRS sensitivity and QRS positive predictability are extracted from both monitors to validate the wearable monitor.

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Atrial Fibrillation detection by a subcutaneous monitoring device

Cited by 2 publications

References 3 publications

Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data

Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data

A Wearable Cardiac Monitor for Long-Term Data Acquisition and Analysis

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