This Python framework provides tools for designing and training deep-learning models capable of reconstructing 12-lead Electrocardiograms (ECGs) from 3-lead inputs and assessing clinical conditions accurately.

• Perform explorative analysis on the dataset, associating elements with multiple clinical labels and computing statistical metrics for different classes.

• Discard corrupted elements and divide remaining ones into training, validation, and testing sets.

• Design and train a deep-learning model that reconstructs 12-lead ECGs using mathematical differences between reconstructed signals and originals as loss functions.

• Train a model to classify ECGs into specific clinical classes using detection accuracy as the optimization metric.

• Train a model to simultaneously reconstruct ECGs and classify signals, optimizing both reconstruction loss and classification probability.

• Train multiple models to perform ECG reconstruction using mathematical differences as loss functions.

• Train multiple models to classify ECG signals, optimizing detection accuracy.

• Train multiple models to reconstruct ECG signals while optimizing class association probabilities.

• Create new data classes as unions or intersections of existing clinical labels.

• Identify corrupted vs. cleaned elements within a class and gather insights into their differences.

• Compute probabilities of association with different clinical labels within a class.

• Execute cleaning and analysis sequentially over a specific class of data elements.