Detection of electrocardiogram atrial fibrillation using modified multifractal detrended fluctuation analysis based on discrete transforms and fractional Fourier transform

Cardiovascular diseases (CVD) are the most common cause of death. Electrocardiography (ECG) is a preferred non-invasive method for detecting heart diseases. Atrial fibrillation (AF) is a common cardiac disease. This results from improper functioning of the sinoatrial node. It can cause heart failure and stroke. AF can be detected using ECG. Several studies used artificial intelligence to classify normal and abnormal ECG signals, such as AF signals. Most of them failed to achieve optimal classification parameter rates and had a significant error factor. Therefore, it is necessary to create a new model to overcome the classification errors in ECG signals. In this study, a novel method was developed to extract ECG signals using a modified multifractal detrended fluctuation analysis (MMFDFA) based on machine learning. The used ECG signals were obtained from the training dataset of the Physionet 2017 Challenge, which comprised 5050 normal and 738 AF signals. MMFDFA was a new version of multifractal detrended fluctuation analysis (MFDFA) by modifying the fluctuation function using one of the next discrete transforms and fractional Fourier transforms (FRFT). These discrete transforms were discrete cosine transform, discrete sine transform, discrete tan transform, discrete sinc transform, discrete hyperbolic cosine transform, discrete hyperbolic sine transform, and discrete hyperbolic tan transform. All of them were decomposed from the discrete sine or cosine transform. Seven approaches of MMFDFA were compared with MFDFA as the first approach based on deep learning (DL) and a support vector machine (SVM) as the classification methods. The deep learning parameters were selected using a simulated annealing optimization method. After using MMFDFA based on the discrete hyperbolic cosine transform and FRFT, the obtained classification parameter rates, such as the accuracy rate and area under the curve (AUC), were 99.3% and 0.996, respectively. These classification parameter rates were the maximum based on the DL. Therefore, MMFDFA is the preferable method for extracting the features of ECG signals based on DL. The software platform used was MATLAB 2022a. The selected model can be programmed on a laptop to rapidly diagnose patients with cardiac disease. Therefore, cardiologists can easily classify the ECG signals. The main contribution of this paper is summarized in the following points: