Improvements in atrial fibrillation detection for real-time monitoring

Babaeizadeh, Saeed; Gregg, Richard E.; Helfenbein, Eric; Lindauer, James M.; Zhou, Sophia H.

doi:10.1016/j.jelectrocard.2009.06.006

Cited by 154 publications

(93 citation statements)

References 8 publications

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“…This may have lowered the confounding effect of rhythm irregularities due to premature beats on the AF classification. The overall, accuracy of the presented AF detection model was similar to that of previously presented ECG algorithms [10]. Yet, the incremental effect of ECG p-wave analysis on reducing false positive rate still needs to be evaluated.…”

Section: Discussionsupporting

confidence: 59%

“…Such algorithm, inspired by the work of Moody and Mark [9], has been previously presented and extensively validated on inter-beat-interval (IBI) data obtained from Holter ECG recordings [10]. A probability threshold was previously selected to process the output of the Markov model and to associate to each beat either an AF or non-AF label.…”

Section: Af Detection From Ppg Datamentioning

confidence: 99%

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Atrial Fibrillation Detection Using Photo:plethysmography and Acceleration Data at the Wrist

Bonomi

Schipper

Eerikäinen

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Section: Discussionsupporting

confidence: 59%

Section: Af Detection From Ppg Datamentioning

confidence: 99%

Atrial Fibrillation Detection Using Photo:plethysmography and Acceleration Data at the Wrist

Bonomi

Schipper

Eerikäinen

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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“…Such an algorithm, inspired by the work of Moody and Mark,24 has been previously trained and validated for AF detection on ECG data from Holter recordings 25. Output from the Markov model was an AF probability reflecting the relative likelihood of observing a sequence of pulse intervals during AF versus making the same observation outside AF.…”

Section: Methodsmentioning

confidence: 99%

Atrial Fibrillation Detection Using a Novel Cardiac Ambulatory Monitor Based on Photo‐Plethysmography at the Wrist

Bonomi

Schipper

Eerikäinen

et al. 2018

JAHA

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BackgroundLong‐term continuous cardiac monitoring would aid in the early diagnosis and management of atrial fibrillation (AF). This study examined the accuracy of a novel approach for AF detection using photo‐plethysmography signals measured from a wrist‐based wearable device.Methods and Results ECG and contemporaneous pulse data were collected from 2 cohorts of AF patients: AF patients (n=20) undergoing electrical cardioversion (ECV) and AF patients (n=40) that were prescribed for 24 hours ECG Holter in outpatient settings (HOL). Photo‐plethysmography and acceleration data were collected at the wrist and processed to determine the inter‐pulse interval and discard inter‐pulse intervals in presence of motion artifacts. A Markov model was deployed to assess the probability of AF given irregular pattern in inter‐pulse interval sequences. The AF detection algorithm was evaluated against clinical rhythm annotations of AF based on ECG interpretation. Photo‐plethysmography recordings from apparently healthy volunteers (n=120) were used to establish the false positive AF detection rate of the algorithm. A total of 42 and 855 hours (AF: 21 and 323 hours) of photo‐plethysmography data were recorded in the ECV and HOL cohorts, respectively. AF was detected with >96% accuracy (ECV, sensitivity=97%; HOL, sensitivity=93%; both with specificity=100%). Because of motion artifacts, the algorithm did not provide AF classification for 44±16% of the monitoring period in the HOL group. In healthy controls, the algorithm demonstrated a <0.2% false positive AF detection rate.ConclusionsA novel AF detection algorithm using pulse data from a wrist‐wearable device can accurately discriminate rhythm irregularities caused by AF from normal rhythm.

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“…; -Combination of independent data from the atrial and ventricular contractions analyses [9,10]. A comparative study of AF detection methods [11] highlights the techniques based on analysis of RR interval irregularity as the most robust against noise, providing the highest sensitivity and specificity, while the combination of RR and atrial activity analysis assures the highest positive predictive value.…”

Section: Introductionmentioning

confidence: 99%

“…The ECG analyses for AF detection are performed either in the time [3][4][5][6][7][8][9][10] or in the frequency domain, where the dominant AF frequency is usually assessed over a signal with extracted QRS-T complexes [12,13].…”

Section: Introductionmentioning

confidence: 99%

Arrhythmia Classification via Time and Frequency Domain Analyses of Ventricular and Atrial Contractions

Jekova

Stoyanov

Dotsinsky

2017

Computing in Cardiology Conference (CinC)

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Improvements in atrial fibrillation detection for real-time monitoring

Cited by 154 publications

References 8 publications

Atrial Fibrillation Detection Using Photo:plethysmography and Acceleration Data at the Wrist

Atrial Fibrillation Detection Using Photo:plethysmography and Acceleration Data at the Wrist

Atrial Fibrillation Detection Using a Novel Cardiac Ambulatory Monitor Based on Photo‐Plethysmography at the Wrist

Arrhythmia Classification via Time and Frequency Domain Analyses of Ventricular and Atrial Contractions

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