Identification of Atrial Fibrillation by Quantitative Analyses of Fingertip Photoplethysmogram

Tang, Sung‐Chun; Huang, Po-Hsiang; Hung, Chi‐Sheng; Shan, Shih-Ming; Lin, Yen-Hung; Shieh, Jiann-Shing; Lai, Dar‐Ming; Wu, An-Yeu; Jeng, Jiann‐Shing

doi:10.1038/srep45644

Cited by 55 publications

(47 citation statements)

References 29 publications

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“…Applications using data provided by the camera sensor and lamp of a smartphone have been extensively validated to record pulse rate variability at the fingertip and identify AF during a spot check 29, 30. Continuous PPG measurements have been also investigated in very recent studies using earlobe‐, finger‐, and wrist‐wearable sensors 31, 32, 33. Accuracy of these solutions has been tested mostly under controlled hospital conditions, and AF detection was mainly derived from information carried by variability and entropy measures of IPI sequences.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…36 Other statistical approaches can also be applied to classify between AF and Non-AF such as logistic regression. 48 Logistic regression models use the logistic function, instead of a straight line or a hyperplane, to fit output the probability between 0 and 1 (corresponding to Non-AF and AF). Markov model is another statistical tool that could be used for AF detection.…”

Section: Ppg Representationsmentioning

confidence: 99%

Photoplethysmography based atrial fibrillation detection: a review

et al. 2020

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Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations-a technology known as photoplethysmography (PPG)-from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.npj Digital Medicine (2020) 3:3 ; https://doi.org/10.1038/s41746-019-0207-9 PHOTOPLETHYSMOGRAPHY PPG signal PPG waveform is generated during a cardiac cycle and typically measured at a peripheral site. Therefore, it is essentially a pulse pressure waveform that originates from the heart contraction and propagates through the vascular tree. As blood flow is controlled

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“…Multiwavelength analysis permits the calculation of oxygenated blood in the local tissue, an application which today is rarely absent from patient monitors. Beyond its conventional use in oxygen saturation monitoring, the PPG technology has advanced in recent years and is currently capable of detecting: pulse rate variability, as a surrogate of heart rate variability [14], respiratory rate [15], [16] sleep apnea [17]- [19], ectopic beats [20], heart rate turbulence [21] and atrial fibrillation [22], [23]. Estimation of blood pressure is another active area of research, however, achieving high accuracy remains challenging [24]- [26], particularly without patient-specific calibration.…”

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confidence: 99%

Non-Invasive Detection of Mechanical Alternans Utilizing Photoplethysmography

Besleaga

Badiani

Lloyd

et al. 2019

IEEE J. Biomed. Health Inform.

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Background and Significance: Mechanical alternans (MA) is a biomarker associated with mortality and life-threatening arrhythmias in heart failure patients. Despite showing prognostic value, its use is limited by the requirement of measuring continuous blood pressure (BP), which is costly and impractical. Objective: To develop and test, for the first time, non-invasive MA surrogates based on photoplethysmography (PPG). Methods: Continuous BP and PPG were recorded during clinical procedures and tests in 35 patients. MA was induced either by ventricular pacing (Group A, N=19) or exercise (Group B, N=16). MA was categorized as sustained or intermittent if MA episodes were observed in at least 20 or between 12 to 20 consecutive beats, respectively. Eight features characterizing pulse morphology were derived from the PPG and MA surrogates were evaluated. Results: Sustained alternans was observed in 9 patients (47%) from Group A, whereas intermittent alternans was observed in 13 patients (68%) from Group A and in 10 patients (63%) from Group B. The PPG-based MA surrogate showing the highest accuracy, V'M, was based on the maximum of the first derivative of the PPG pulse. It detected both sustained and intermittent MA with 100% sensitivity and 100% specificity in Group A and intermittent MA with 100% sensitivity and 83% specificity in Group B. Furthermore, the magnitudes of MA and its PPG-based surrogate were linearly correlated (R 2 =0.83, p<0.001). Conclusion: MA can be accurately identified non-invasively through PPG analysis. This may have important clinical implications for risk stratification and remote monitoring.

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Identification of Atrial Fibrillation by Quantitative Analyses of Fingertip Photoplethysmogram

Cited by 55 publications

References 29 publications

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

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

Photoplethysmography based atrial fibrillation detection: a review

Non-Invasive Detection of Mechanical Alternans Utilizing Photoplethysmography

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