A lightweight 1D convolutional neural network model for arrhythmia diagnosis from electrocardiogram signal

Electrocardiogram (ECG) is used by cardiologist to diagnose heart diseases. The use of ECG signal in an artificial intelligence system can permit to automatically analyze these signals and thereby improve diagnosis quality. For this purpose, many models have been proposed in the literature. But many of these models are complex enough for implementation in an embedded system dedicated to medical diagnosis. Still others have performances that remain to be improved. To solve this problem of complexity, while improving performance, we propose a simple 1D convolutional neural network model for cardiac arrhythmia diagnosis. The proposed model combines two convolution layers, two max pooling layers, three dense layers, two dropout layers and a flatten layer. We apply the proposed model on the public MIT-BIH database for inter-patient classification of five distinct types of heartbeat rhythms which are consistent with the association for advancement of medical instrumentation (AAMI) standard. We also apply our model on the PTB database in order to evaluate its generalization capability. On the MIT-BIH database, the results provide an accuracy of 0.9842, a precision of 0.9523, a sensitivity of 0.8760, a specificity of 0.9869, a negative predictive value (NPV) of 0.9936, an average area under the ROC curve (AUC) of 0.99 and a F1-measure of 0.9095. The accuracy, precision, sensitivity, specificity, NPV, and AUC on the PTB dataset are 0.9924, 0.9938, 0.9957, 0.9844, 0.9892, and 1, respectively. Compared to other existing models, for unbalanced data, the performances obtained by our model are quite interesting for an inter-patient classification.