RL-ECGNet: resource-aware multi-class detection of arrhythmia through reinforcement learning

Arrhythmia is a fatal cardiac clinical condition that risks the lives of millions every year. It has multiple classes with variable prevalence rates. Some rare arrhythmia classes are equally critical as common ones, yet are very hard to detect due to limited training samples. While several methods accurately detect Arrhythmia's multi-class, minority class accuracy remains low and these methods are resource-intensive. Therefore, most of the existing detection systems ignore minority classes in their classification or focus on binary classification. In this study, we introduce RL-ECGNet, a resource-efficient reinforcement learning-based optimization for multi-class arrhythmia detection, encompassing minority classes, through ECG signal analysis. RL-ECGNet uses raw ECG signals, processes them to extract the temporal ECG features, and utilizes Reinforcement Learning (RL) to optimize the training and network hyperparameters of the Deep Learning (DL) models while reducing resource consumption. For evaluation, four DL models, namely, MLP, CNN, LSTM, and GRU, are trained and optimized. Moreover, time and memory usage are minimized to optimize resource consumption. Throughout the evaluation of the four DL models, the proposed RL model achieved accuracies ranging from 88.45% to 96.41% for all 9 arrhythmia classes, including minority classes. In addition, the proposed RL method improved performance by a factor ranging from 1.28 to 1.39 in terms of accuracy. Moreover, the optimized DL models had reduced training time, as well as minimized memory usage. The proposed method achieved resource consumption reduction ranging from 1.36 to 1.925 times for training time, and from 1.179 to 1.815 times for memory usage.