Why the study?
Deciphering large data sets to determine appropriate information remains a challenge in ECG-based cardiovascular disease diagnosis and treatment.
Does a deep neural network combined with a genetic algorithm improve the accuracy of ECG-based arrhythmia detection compared to state-of-the-art methods?
Comparison
Deep neural network strategy with genetic algorithm vs state-of-the-art methods
Key result
A multitier deep neural network strategy using a genetic algorithm for ECG-based arrhythmia detection achieved an average accuracy of 0.94 and an F1 score of 0.953.
Authors
Loading...
Hypothesis-generating for hybrid DNN arrhythmia detection; prospective trials needed before clinical adoption.
Does a deep neural network combined with a genetic algorithm improve the accuracy of ECG-based arrhythmia detection compared to state-of-the-art methods?
A novel deep learning model combining DNN and genetic algorithms improves the accuracy and F1 score of ECG-based arrhythmia detection.
Hammad et al. (2020) studied Arrhythmia. Deep neural network (DNN) strategy with genetic algorithm vs. State-of-the-art methods was evaluated on Average accuracy and F1 score. A multitier deep neural network strategy using a genetic algorithm for ECG-based arrhythmia detection achieved an average accuracy of 0.94 and an F1 score of 0.953.