This project applies ANNs, CNNs, LSTMs, and a Hybrid Transformer to classify ECG signals from the MIT-BIH dataset for arrhythmia detection. Includes preprocessing, class balancing, model comparison, and ensembles, achieving ~98% accuracy.
timeseries deep-learning projects project ecg classification signal dl arrhythmia 06-timeseries-rnn-recurrent-neural-network 05-timeseries-classifications
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Updated
Aug 19, 2025 - Jupyter Notebook