ankur219/ECG-Arrhythmia-classification
ECG arrhythmia classification using a 2-D convolutional neural network
Converts 1-D ECG signals into 2-D grayscale images to eliminate manual feature extraction and noise filtering, enabling data augmentation through image cropping techniques that improve model robustness. Implements a deep 2-D CNN to classify signals into seven categories (normal and six arrhythmia types). Includes a Flask web application for deployment with pretrained weights, accepting ECG data in CSV format.
332 stars. No commits in the last 6 months.
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332
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Language
Python
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Last pushed
Jan 28, 2020
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