October 6, 2015IEEE Transactions on Very Large Scale Integration (VLSI) Systems

Low-Power ECG-Based Processor for Predicting Ventricular Arrhythmia

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Population

Heart signal recordings from the MIT PhysioNet and the American Heart Association databases

Authors

NBNourhan BayasiAbu Dhabi University TTTemesghen TekesteKhalifa University of Science and Technology HSHani SalehKhalifa University of Science and Technology

Discussion

Member takes

Implication

May enable compact real-time arrhythmia prediction devices; leaves open prospective clinical validation before practice adoption.

Structured PICO

Population

Heart signal recordings from the MIT PhysioNet and the American Heart Association databases

Intervention

Fully integrated electrocardiogram (ECG) signal processor (ESP) using a naive Bayes classifier

Outcome

Classification accuracy for the prediction of ventricular arrhythmia

The proposed low-power ECG signal processor achieved 86% accuracy in predicting ventricular arrhythmia up to 3 hours before onset, demonstrating potential for early detection in wearable devices.

Cite This Study

Bayasi et al. (2015) studied this question.

synapsesocial.com/papers/6a7172e8e5469ee92be1fb3e https://doi.org/10.1109/tvlsi.2015.2475119

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Low-Power ECG-Based Processor for Predicting Ventricular Arrhythmia

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Low-Power ECG-Based Processor for Predicting Ventricular Arrhythmia

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Also Consider

Also Consider