An automatic arrhythmia classification model based on improved Marine Predators Algorithm and Convolutions Neural Networks

Abstract

Preparation of Convolutional Neural Networks (CNNs) for classification purposes depends heavily on the knowledge of hyper-parameters tuning. This study aims, in particular in task of automated electrocardiograms (ECG), to minimize the user variability in the CNN training by searching and optimizing the CNN parameters automatically. In the clinical practice, the task of ECG classification analysis is restricted by existing models’ configuration. The hyper-parameters of the CNN model should be adjusted for the ECG classification problem. The best configuration for hyper-parameters is selected to have an impact on the production of the model. Deep knowledge of deep learning algorithms and suitable optimization techniques are also needed. Although there are many strategies for automated optimization, different benefits and disadvantages occur as they are applied to ECG classification problem. Here we present a CNN model for classification of non-ectopic (N), ventricular ectopic (V), supraventricular ectopic (S), and fusion (F) ECG rhythmias by the hybrid models based on modified version of Marine Predators algorithm (MPA) and CNN, known as the IMPA-CNN. The proposed model summarizes the feature extraction techniques of major features and, thus, outperforms other current classification models through automatically select the best hyper-parameters configuration of the CNN model. To reduce the time and complication complexity, optimum characteristics have been extracted directly from the raw signal using 1D-local binary pattern, higher order statistics, central moment, Hermite basis function discrete wavelet transform, and R–R intervals. Then, a modified version of MPA algorithm is used to select appropriate hyper-parameters for the CNN model like initial learning rate for the CNN model that is one of the major hyper parameters effect output performance, optimizer type which can be set to stochastic gradient descent (SGD), adaptive moment estimation (Adam), root mean square propagation (RMSprop), the activation function form used for modeling non-linear functions, set to ‘Rectified Linear Unit (ReLU), or ‘sigmoid’ and some other hyper-parameters are related to the optimization and training process of CNN model. Many available optimization algorithms for hyper-parameters optimization problems are provided. In addition, experiments with well know data sets like MIT-BIH arrhythmia, European ST-T database, and St. Petersburg INCART database are carried out to compare the performance of various optimization approaches and to provide practical illustration of the optimization of hyper-parameters for the proposed CNN model.