January 1, 1998Journal of Medical Engineering & Technology

ECG processing techniques based on neural networks and bidirectional associative memories

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Population

ECG signals containing QRS complexes, premature ventricular contractions (PVCs), and normal or ischaemic beats

Design

Other

Authors

NMNicos MaglaverasElectrophysiology TST. StamkopoulosAristotle University of Thessaloniki CPC. PappasAristotle University of Thessaloniki

Discussion

Member takes

Implication

May support automated ECG monitoring tools; leaves open prospective clinical validation before adoption.

Structured PICO

Population

ECG signals containing QRS complexes, premature ventricular contractions (PVCs), and normal or ischaemic beats

Intervention

Neural network-based ECG processing techniques (nonlinear ECG mapping with a shrinking algorithm, and Bidirectional Associative Memory neural network)

Outcome

Classification accuracy of QRSs, PVCs, and normal versus ischaemic beatssurrogate

Neural network-based processing techniques, including Bidirectional Associative Memory, show promise for automated, fast, and reliable classification of ECG abnormalities such as PVCs and ischaemic beats.

Cite This Study

Maglaveras et al. (1998) studied this question.

synapsesocial.com/papers/6a709de4e36a167817e26db7 https://doi.org/10.3109/03091909809062475

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ECG processing techniques based on neural networks and bidirectional associative memories

Discussion

Member takes

Implication

Structured PICO

Cite This Study

ECG processing techniques based on neural networks and bidirectional associative memories

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Also Consider

Also Consider