The Curious Case of Randomness in Deep Learning Models for Heartbeat Classification

Abstract

The research hypothesis in this study is that different random number generator seeds using 1D Convolutional Neural Networks impact the performance results by more than 15% on the heartbeat classification performance. Furthermore, we address a research question to evaluate the impact level of random values in the initialization of model parameters experimenting on the classification of ventricular heartbeats in electrocardiogram training and evaluating models with various feature sets based on the width of the measured samples surrounding a heartbeat location. Specific test cases consist of differently selected initial neural network parameters guided by manually selected random number seeds while preserving the rest of the training environment and hyper-parameters. We examine the influence of the random number seed on the model’s learning dynamics and ultimate F1 score on the performance of the testing dataset and conclude fluctuations resulting in 24.61% root mean square error from the average. Furthermore, we conclude that optimizing the validation in the training process does not optimize the performance in the testing. The research results contribute a novel viewpoint to the field, paving the way for more efficient and accurate heartbeat classification systems and improving diagnostic and prognostic performance in cardiac health.