Detection of atrial fibrillation in ECG signals using machine learning




Almasi, Shahin

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An Electrocardiogram (ECG) records electrical signals from the heart to detect abnormal heart rhythms or cardiac arrhythmias. Atrial Fibrillation (AF) is the most common arrhythmia which leads to a large number of deaths annually. The diagnosis of heart disease is skill-dependent and time-consuming, therefore using an intelligent system is a time- and cost-effective approach which can also enhance diagnostic accuracy. This study uses several types of Neural Networks (NNs) including the Deep Neural Network (DNN) GoogLeNet, Multi-Layer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Long Short-Term Memory (LSTM) to identify arrhythmias in AF signals. The results obtained are compared in order to identify the most effective and accurate system for AF diagnosis. The proposed system has two main steps, preprocessing and postprocessing. In the preprocessing step, different approaches based on the classifier network are used. More specifically, for MLP, ANFIS, and LSTM the 1-D Daubechies wavelet is used, and the extracted wavelet coefficients and statistical features are used as input data to the network. For GoogLeNet, the Continuous Wavelet Transform (CWT) is used to create a time-frequency representation of the signal (scalogram) and extract key signal features. In the postprocessing step, the data obtained (extracted features) are used as the input data to classify the signals. Also, the train and test accuracies and the running times are compared. The results obtained indicate that GoogLeNet provides the best accuracy, but its running time is long. Further, although the ANFIS and MLP networks are much faster than LSTM and GoogLeNet, their accuracy is much lower.



ECG Signals