A preliminary evaluation of Echo State Networks for Brugada syndrome classification
Author:
Affiliation:
1. University of Pisa,Department of Computer Science,Italy
2. Istituto di Fisiologia Clinica IFC CNR,Pisa,Italy
Funder
Regione Toscana
Italian National Research Council Council (CNR)
Università di Pisa
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9659537/9659538/09659966.pdf?arnumber=9659966
Reference32 articles.
1. The impact of digital filtering to ECG analysis: Butterworth filter application
2. Reservoir Topology in Deep Echo State Networks
3. A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification
4. Temporal Variability in Electrocardiographic Indices in Subjects With Brugada Patterns
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1. Echo state networks for the recognition of type 1 Brugada syndrome from conventional 12-LEAD ECG;Heliyon;2024-02
2. Deep learning techniques for biomedical data processing;Intelligent Decision Technologies;2023-04-20
3. Analysis and Interpretation of ECG Time Series Through Convolutional Neural Networks in Brugada Syndrome Diagnosis;Artificial Neural Networks and Machine Learning – ICANN 2023;2023
4. Learning-Based Approach to Predict Fatal Events in Brugada Syndrome;Applications of Artificial Intelligence and Neural Systems to Data Science;2023
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