Deep learning algorithms, such as LSTM and CNN, achieved 10% better performance in detecting atrial fibrillation from ECG signals compared to traditional machine learning classifiers.
Do deep learning algorithms (LSTM, CNN) improve the detection of atrial fibrillation from ECG signals compared to traditional machine learning classifiers?
Deep learning algorithms like LSTM and CNN outperform traditional machine learning classifiers by 10% in detecting atrial fibrillation from ECG signals.
The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate.
Liaqat et al. (Thu,) conducted a other in Atrial Fibrillation. Deep learning algorithms (LSTM and CNN) vs. Machine learning classifiers (support vectors, logistic regression) was evaluated on Detection of atrial fibrillation episodes from ECG signals. Deep learning algorithms, such as LSTM and CNN, achieved 10% better performance in detecting atrial fibrillation from ECG signals compared to traditional machine learning classifiers.