Atrial fibrillation detection using a novel cardiac ambulatory monitor based on photo-plethysmography at the wrist

Alberto G. Bonomi, Fons Schipper, Linda M. Eerikäinen, Jenny Margarito, Ralph van Dinther, Guido Muesch, Helma M. de Morree, Ronald M. Aarts, Saeed Babaeizadeh, David D. McManus, Lukas R.C. Dekker

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Abstract

Background-Long-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. Conclusions-A novel AF detection algorithm using pulse data from a wrist-wearable device can accurately discriminate rhythm irregularities caused by AF from normal rhythm.

Original languageEnglish
Article numbere009351
Number of pages17
JournalJournal of the American Heart Association
Volume7
Issue number15
DOIs
Publication statusPublished - 1 Aug 2018

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Plethysmography
Wrist
Atrial Fibrillation
Electric Countershock
Electrocardiography
Artifacts
Pulse
Equipment and Supplies

Keywords

  • Arrhythmia (heart rhythm disorders)
  • Cardioversion
  • Screening
  • Self-management

Cite this

Bonomi, Alberto G. ; Schipper, Fons ; Eerikäinen, Linda M. ; Margarito, Jenny ; van Dinther, Ralph ; Muesch, Guido ; de Morree, Helma M. ; Aarts, Ronald M. ; Babaeizadeh, Saeed ; McManus, David D. ; Dekker, Lukas R.C. / Atrial fibrillation detection using a novel cardiac ambulatory monitor based on photo-plethysmography at the wrist. In: Journal of the American Heart Association. 2018 ; Vol. 7, No. 15.
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title = "Atrial fibrillation detection using a novel cardiac ambulatory monitor based on photo-plethysmography at the wrist",
abstract = "Background-Long-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. Conclusions-A 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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author = "Bonomi, {Alberto G.} and Fons Schipper and Eerik{\"a}inen, {Linda M.} and Jenny Margarito and {van Dinther}, Ralph and Guido Muesch and {de Morree}, {Helma M.} and Aarts, {Ronald M.} and Saeed Babaeizadeh and McManus, {David D.} and Dekker, {Lukas R.C.}",
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Atrial fibrillation detection using a novel cardiac ambulatory monitor based on photo-plethysmography at the wrist. / Bonomi, Alberto G.; Schipper, Fons; Eerikäinen, Linda M.; Margarito, Jenny; van Dinther, Ralph; Muesch, Guido; de Morree, Helma M.; Aarts, Ronald M.; Babaeizadeh, Saeed; McManus, David D.; Dekker, Lukas R.C.

In: Journal of the American Heart Association, Vol. 7, No. 15, e009351, 01.08.2018.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Atrial fibrillation detection using a novel cardiac ambulatory monitor based on photo-plethysmography at the wrist

AU - Bonomi, Alberto G.

AU - Schipper, Fons

AU - Eerikäinen, Linda M.

AU - Margarito, Jenny

AU - van Dinther, Ralph

AU - Muesch, Guido

AU - de Morree, Helma M.

AU - Aarts, Ronald M.

AU - Babaeizadeh, Saeed

AU - McManus, David D.

AU - Dekker, Lukas R.C.

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N2 - Background-Long-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. Conclusions-A novel AF detection algorithm using pulse data from a wrist-wearable device can accurately discriminate rhythm irregularities caused by AF from normal rhythm.

AB - Background-Long-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. Conclusions-A novel AF detection algorithm using pulse data from a wrist-wearable device can accurately discriminate rhythm irregularities caused by AF from normal rhythm.

KW - Arrhythmia (heart rhythm disorders)

KW - Cardioversion

KW - Screening

KW - Self-management

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