Entropy-driven ECG Arrhythmia Classification

Deep learning models are at the forefront of solving classification tasks by recognizing intricate patterns in data using their non-linear functions. However, most data fed to these models exhibit high dimensionality, leading to substantial computational costs. Fortunately, not all information provided to the model is equally relevant for classifying data samples. By identifying and utilizing only essential information, we can significantly reduce computational costs while maintaining classification performance—an essential consideration for deploying models on resource-constrained edge devices.In this work, we propose a methodology for identifying the most relevant information within the data and feeding only that information to deep learning models. Specifically, we compute the Kullback-Leibler divergence on samples from the MIT-BIH arrhythmia classification task. By presenting the model with a fraction of the scalograms extracted using the Continuous Wavelet Transform (CWT), we achieve the following results: a 79% reduction in latency, 74% fewer Floating Point Operations (FLOPs), and a 44% reduction in model size, all while sacrificing only 4.57% accuracy.