Towards automated optimisation of residual convolutional neural networks for electrocardiogram classification. (arXiv:2112.06024v2 [eess.SP] UPDATED)

Web: http://arxiv.org/abs/2112.06024

The interpretation of the electrocardiogram (ECG) gives clinical information
and helps in assessing heart function. There are distinct ECG patterns
associated with a specific class of arrythmia. The convolutional neural network
is currently one of the most commonly employed deep learning algorithms for ECG
processing. However, deep learning models require many hyperparameters to tune.
Selecting an optimal or best hyperparameter for the convolutional neural
network algorithm is a highly challenging task. Often, we end up tuning the
model manually with …

arxiv classification convolutional neural networks networks neural neural networks