Publication: Arrhythmia electrocardiogram (ECG) signal classification using long short term memory, lstm (deep learning).
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Date
2023-07
Authors
Kang, Xian Jie
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Abstract
Arrhythmias, which are irregularities in the heartbeat's rhythm, can seriously harm a person's health. The type of rhythm disruption and the region of the heart where the disturbance arises are used to classify arrhythmias. However, it is nearly impossible to obtain a 100% accurate test due to the complexity of the cardiac conduction system through human eye. Recurrent Neural Networks (RNNs) is a type of artificial neural network that is commonly used to analysing ECG data due to its ability to analyse sequences of inputs. However, the vanishing gradient problem, which affects RNNs, causes delayed convergence and makes learning long term dependencies difficult as the gradient values are very small during backpropagation. Thus, LSTM which is specifically designed to overcome this limitation of traditional RNN is chosen in this project to classified arrhythmias from a dataset. A dataset of ECG signals is collected from PhysioNet Database which contains the recordings of several types of arrhythmias such as sinus arrhythmia, atrial fibrillation, ventricular tachycardia, and ventricular fibrillation. The
raw data of ECG signals is first pre-processed to filter out the noise and normalizing the signals. Then, the data is then segmented into individual heartbeat and extracted relevant features for classification. Next, an LSTM model is developed to classify the ECG signals into different classes. Finally, the performance output of the model is evaluated using confusion matrix to assess the effect of different model parameters. Our findings show that LSTM neural networks show better results in classifying ECG arrhythmias compared to traditional RNN.