Automated analysis of ECG signals using nonlinearity and nonstationarity features fed into the MobilenetV2 CNN powered by transfer learning.

Atrial fibrillation (AFB) and atrial flutter (AFL) are cardiac arrhythmias very often associated with the aggravation of other cardiac pathologies and increase the risk of stroke and heart failure. Their detection is therefore crucial. Automated analysis of the ECG signal has been suggested to assist cardiologists in the diagnosis of AFB and AFL. In this paper, a novel automated electrocardiogram (ECG) signal analysis method to aid in the detection of AFB and AFL is presented. The first step of the method consists of processing the original ECG signal. The second step carries out the classification using a modified MobileNetV2 convolutional neural network (CNN) powered by transfer learning. This CNN classifies the fed-in ECG signals into atrial fibrillation (AFB), atrial flutter (AFL), other (OTH), normal sinus rhythms (NOR), and noisy (NOI) recordings. The performance of the proposed method was assessed and scored using the Physio Net/Computing in Cardiology (CinC) 2017 dataset and the MIT-BIH Atrial Fibrillation Database (MIT-BIH). The experimental results showed that the proposed method gave an F1 score of 96.08%, sensitivity of 97.1%, specificity of 99.53%, and accuracy of 95.1% for atrial fibrillation, for the CinC 2017 dataset. For the MIT-BIH dataset, an F1 score of 99.54%, sensitivity of 99.51%, specificity of 99.64%, and accuracy of 99.5% were obtained. The results disclosed above on 2 databases prove that the proposed algorithm is efficient, robust, and can be used to assist cardiologists.