What does this research mean for the field?

Artificial neural networks, when supported by critical data pre- and post-processing to reduce input dimensions, can effectively classify common ECG features such as arrhythmia and myocardial ischemia provided their architectures are tailored to the specific classification task. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

May 1, 1998IEEE Transactions on Signal Processing

Artificial neural networks for automatic ECG analysis

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Structured PICO

Population

ECG data from available ECG databases

Intervention

Artificial neural networks (ANNs) for automatic ECG analysis

Comparator

Competing alternatives

Outcome

Classification performance for arrhythmia, myocardial ischemia, and chronic alterations

Artificial neural networks, when combined with appropriate data pre- and post-processing, offer a viable approach for the automatic classification of ECG features such as arrhythmias and ischemia.

Discussion

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Implication

ANN with preprocessing shows promise for ECG classification; leaves open validation in clinical workflows before routine use.

Cite This Study

Silipo et al. (1998) studied this question.

synapsesocial.com/papers/6a1e25b07dd5fe0967e44718 https://doi.org/10.1109/78.668803

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Artificial neural networks for automatic ECG analysis

Structured PICO

Discussion

Implication

Cite This Study

Artificial neural networks for automatic ECG analysis

Structured PICO

Discussion

Implication

Cite This Study

Also Consider

Also Consider