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What is Hybrid CNN-LSTM for ECG Arrhythmia Classification about?

This paper presents a hybrid CNN-LSTM model for automated ECG signal classification, achieving an accuracy of 98.94% on the MIT-BIH Arrhythmia dataset. The model combines Convolutional Neural Networks for spatial feature extraction and Long Short-Term Memory networks for capturing temporal dependencies, enhancing arrhythmia detection. The study emphasizes the importance of accurate ECG analysis in diagnosing cardiovascular diseases and improving patient monitoring.

Author

Akshaya Ramesh

Language

EN