Dataset Information

Necklace-embedded electrocardiogram for the detection and diagnosis of atrial fibrillation.

ABSTRACT:

Background

Atrial fibrillation (AF) is the major cause of stroke since approximately 25% of all strokes are of cardioembolic-origin. The detection and diagnosis of AF are often challenging due to the asymptomatic and intermittent nature of AF.

Hypothesis

A wearable electrocardiogram (ECG)-device could increase the likelihood of AF detection. The aim of this study was to evaluate the feasibility and reliability of a novel, consumer-grade, single-lead ECG recording device (Necklace-ECG) for screening, identifying and diagnosing of AF both by a cardiologist and automated AF-detection algorithms.

Methods

A thirty-second ECG was recorded with the Necklace-ECG device from two positions; between the palms (palm) and between the palm and the chest (chest). Simultaneously registered 3-lead ECGs (Holter) served as a golden standard for the final rhythm diagnosis. Two cardiologists interpreted independently in a blinded fashion the Necklace-ECG recordings from 145 patients (66 AF and 79 sinus rhythm, SR). In addition, the Necklace-ECG recordings were analyzed with an automatic AF detection algorithm.

Results

Two cardiologists diagnosed the correct rhythm of the interpretable Necklace-ECG with a mean sensitivity of 97.2% and 99.1% (palm and chest, respectively) and specificity of 100% and 98.5%. The automatic arrhythmia algorithm detected the correct rhythm with a sensitivity of 94.7% and 98.3% (palm and chest) and specificity of 100% of the interpretable measurements.

Conclusions

The novel Necklace-ECG device is able to detect AF with high sensitivity and specificity as evaluated both by cardiologists and an automated AF-detection algorithm. Thus, the wearable Necklace-ECG is a new, promising method for AF screening.

Clinical trial registration

Study was registered in the ClinicalTrials.gov database (NCT03753139).

SUBMITTER: Santala OE

PROVIDER: S-EPMC8119818 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Necklace-embedded electrocardiogram for the detection and diagnosis of atrial fibrillation.

Santala Onni E OE Lipponen Jukka A JA Jäntti Helena H Rissanen Tuomas T TT Halonen Jari J Kolk Indrek I Pohjantähti-Maaroos Hanna H Tarvainen Mika P MP Väliaho Eemu-Samuli ES Hartikainen Juha J Martikainen Tero T

Clinical cardiology 20210225 5

<h4>Background</h4>Atrial fibrillation (AF) is the major cause of stroke since approximately 25% of all strokes are of cardioembolic-origin. The detection and diagnosis of AF are often challenging due to the asymptomatic and intermittent nature of AF.<h4>Hypothesis</h4>A wearable electrocardiogram (ECG)-device could increase the likelihood of AF detection. The aim of this study was to evaluate the feasibility and reliability of a novel, consumer-grade, single-lead ECG recording device (Necklace- ...[more]

PMID: 33629410

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Project description:BackgroundIn high-dimensional data analysis, the complexity of predictive models can be reduced by selecting the most relevant features, which is crucial to reduce data noise and increase model accuracy and interpretability. Thus, in the field of clinical decision making, only the most relevant features from a set of medical descriptors should be considered when determining whether a patient is healthy or not. This statistical approach known as feature selection can be performed through regression or classification, in a supervised or unsupervised manner. Several feature selection approaches using different mathematical concepts have been described in the literature. In the field of classification, a new approach has recently been proposed that uses the [Formula: see text]-metric, an index measuring separability between different classes in heart rhythm characterization. The present study proposes a filter approach for feature selection in classification using this [Formula: see text]-metric, and evaluates its application to automatic atrial fibrillation detection.MethodsThe stability and prediction performance of the [Formula: see text]-metric feature selection approach was evaluated using the support vector machine model on two heart rhythm datasets, one extracted from the PhysioNet database and the other from the database of Marseille University Hospital Center, France (Timone Hospital). Both datasets contained electrocardiogram recordings grouped into two classes: normal sinus rhythm and atrial fibrillation. The performance of this feature selection approach was compared to that of three other approaches, with the first two based on the Random Forest technique and the other on receiver operating characteristic curve analysis.ResultsThe [Formula: see text]-metric approach showed satisfactory results, especially for models with a smaller number of features. For the training dataset, all prediction indicators were higher for our approach (accuracy greater than 99% for models with 5 to 17 features), as was stability (greater than 0.925 regardless of the number of features included in the model). For the validation dataset, the features selected with the [Formula: see text]-metric approach differed from those selected with the other approaches; sensitivity was higher for our approach, but other indicators were similar.ConclusionThis filter approach for feature selection in classification opens up new methodological avenues for atrial fibrillation detection using short electrocardiogram recordings.

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Dataset Information

Necklace-embedded electrocardiogram for the detection and diagnosis of atrial fibrillation.

Background

Hypothesis

Methods

Results

Conclusions

Clinical trial registration

Publications

Necklace-embedded electrocardiogram for the detection and diagnosis of atrial fibrillation.

Similar Datasets

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