A CNN-LSTM deep learning architecture achieved a five-fold cross-validation accuracy of 0.834 in distinguishing normal from abnormal (cardiac arrhythmia) ECG signals.
Can deep learning techniques accurately detect cardiac arrhythmia from ECG signals?
A hybrid CNN-LSTM deep learning model can automatically detect cardiac arrhythmias from ECG signals with an accuracy of 0.834.
Effect estimate: Accuracy 0.834
Cardiac arrhythmia is a condition where heart beat is irregular. The goal of this paper is to apply deep learning techniques in the diagnosis of cardiac arrhythmia using ECG signals with minimal possible data pre-processing. We employ convolutional neural network (CNN), recurrent structures such as recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU) and hybrid of CNN and recurrent structures to automatically detect the abnormality. Unlike the conventional analysis methods, deep learning algorithms don’t have feature extraction based analysis methods. The optimal parameters for deep learning techniques are chosen by conducting various trails of experiments. All trails of experiments are run for 1000 epochs with learning rate in the range 0.01-0.5. We obtain five-fold cross validation accuracy of 0.834 in distinguishing normal and abnormal (cardiac arrhythmia) ECG with CNN-LSTM. Moreover, the accuracy obtained by other hybrid architectures of deep learning algorithms is comparable to the CNN-LSTM.
Swapna et al. (Mon,) conducted a other in Cardiac arrhythmia. Deep learning techniques (CNN-LSTM) vs. Conventional analysis methods was evaluated on Distinguishing normal and abnormal (cardiac arrhythmia) ECG (Accuracy 0.834). A CNN-LSTM deep learning architecture achieved a five-fold cross-validation accuracy of 0.834 in distinguishing normal from abnormal (cardiac arrhythmia) ECG signals.