Dataset Information

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.

SUBMITTER: Bonomi AG

PROVIDER: S-EPMC6201454 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Bonomi Alberto G AG Schipper Fons F Eerikäinen Linda M LM Margarito Jenny J van Dinther Ralph R Muesch Guido G de Morree Helma M HM Aarts Ronald M RM Babaeizadeh Saeed S McManus David D DD Dekker Lukas R C LRC

Journal of the American Heart Association 20180801 15

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 hour ...[more]

PMID: 30371247

Similar Datasets

Project description:Atrial fibrillation is often asymptomatic and intermittent making its detection challenging. A photoplethysmography (PPG) provides a promising option for atrial fibrillation detection. However, the shapes of pulse waves vary in atrial fibrillation decreasing pulse and atrial fibrillation detection accuracy. This study evaluated ten robust photoplethysmography features for detection of atrial fibrillation. The study was a national multi-center clinical study in Finland and the data were combined from two broader research projects (NCT03721601, URL: https://clinicaltrials.gov/ct2/show/NCT03721601 and NCT03753139, URL: https://clinicaltrials.gov/ct2/show/NCT03753139). A photoplethysmography signal was recorded with a wrist band. Five pulse interval variability, four amplitude features and a novel autocorrelation-based morphology feature were calculated and evaluated independently as predictors of atrial fibrillation. A multivariate predictor model including only the most significant features was established. The models were 10-fold cross-validated. 359 patients were included in the study (atrial fibrillation n = 169, sinus rhythm n = 190). The autocorrelation univariate predictor model detected atrial fibrillation with the highest area under receiver operating characteristic curve (AUC) value of 0.982 (sensitivity 95.1%, specificity 93.7%). Autocorrelation was also the most significant individual feature (p < 0.00001) in the multivariate predictor model, detecting atrial fibrillation with AUC of 0.993 (sensitivity 96.4%, specificity 96.3%). Our results demonstrated that the autocorrelation independently detects atrial fibrillation reliably without the need of pulse detection. Combining pulse wave morphology-based features such as autocorrelation with information from pulse-interval variability it is possible to detect atrial fibrillation with high accuracy with a commercial wrist band. Photoplethysmography wrist bands accompanied with atrial fibrillation detection algorithms utilizing autocorrelation could provide a computationally very effective and reliable wearable monitoring method in screening of atrial fibrillation.

Project description:Atrial fibrillation (AF) is the most common sustained arrhythmia characterized by rapid and multiple irregular excitations within the atria. AF is associated with serious morbidity and increased mortality, and its prevalence is prospected to increase as society ages. The limited therapeutic efficacy of AF treatment as well as its high socioeconomic burden makes AF a major clinical challenge. Despite our expanding knowledge of individual proteins and pathways involved in the complex pathophysiology of atrial fibrillation (AF), an unbiased overview of proteins and functionally enriched biological processes as well as their crosstalk is lacking. Here, we performed an explorative proteomics analysis to reveal the global abundance of proteins in cardiac tissue of patients, and deciphered functionally grouped gene ontologies (GO) to uncover a perspective of the disease biology driving or driven by AF. A total of 2703 proteins were identified by liquid chromatography coupled to tandem mass spectrometry. Among them, 150 proteins (accounting for 5.6% of 2703) had a significantly altered abundance (100 proteins increased and 50 decreased) in AF. A significant biological connection was found between those (protein-protein interaction enrichment p-value=1.0e-16). GO enrichment analysis showed that these 150 proteins were mainly located in extracellular/cytoplasmic vesicles, mitochondrion, and cytoskeletal compartments. Correspondingly, the 100 proteins increased in AF were significantly enriched in the GO terms related to immune system, metabolic process, iron process, ECM disassembly, mitochondrial translation and apoptotic signaling. Partially clustered proteins with dense functional link were found in immune system and metabolic process, and were respectively annotated in neutrophil degranulation, and oxoacid metabolic process coupled to the subunits of mitochondrial dehydrogenase NADH. Those processes enriched in AF had crosstalk via the proteins involved in neutrophil degranulation. Selected proteins such as LCN2 (neutrophil degranulation), CA3 (immune system), NDUFS2 (complex I) and MYH10 (actin motor protein) were validated by western blot or qPCR in an independent cohort. The 50 proteins decreased in AF were collectively enriched in vesicle-mediated transport and actin filament-based movement. We demonstrate that important biological processes underlying persistent AF as well as their crosstalk via the components of neutrophil degranulation. Our study provides a novel insight for a more efficient targeting strategy for AF treatment.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets