May 20, 2021Open Access

Robust heartbeat detection using multimodal recordings and ECG quality assessment with signal amplitudes dispersion

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Population

Multimodal recordings from the Physionet Computing in Cardiology Challenge 2014 database

Design

Other

Authors

ZKZahra Rezaei KhavasUniversity of Massachusetts Lowell BABabak Mohammadzadeh AslTarbiat Modares University

Discussion

Member takes

Implication

New multi-signal ECG noise detection may improve monitoring fidelity; leaves open prospective validation before clinical adoption.

Structured PICO

Population

Multimodal recordings from the Physionet Computing in Cardiology Challenge 2014 database

Intervention

A new algorithm for robust heartbeat detection using multimodal recordings (ECG, BP, ART, PAP, SV) and ECG quality assessment with signal amplitudes dispersion

Outcome

Heartbeat detection performance (average sensitivity, positive predictive value, accuracy, and F1 measure)

The proposed multimodal heartbeat detection algorithm achieved high accuracy (93.11%) and F1 measure (95.62%) on the Physionet Challenge 2014 database.

Cite This Study

Khavas et al. (2021) studied this question.

synapsesocial.com/papers/6a7cdf5017f2f70f4d3aa79b https://doi.org/10.48550/arxiv.2105.10046

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Robust heartbeat detection using multimodal recordings and ECG quality assessment with signal amplitudes dispersion

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Robust heartbeat detection using multimodal recordings and ECG quality assessment with signal amplitudes dispersion

Discussion

Member takes

Implication

Structured PICO

Cite This Study

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