Detection of electrocardiogram atrial fibrillation using modified multifractal detrended fluctuation analysis based on discrete transforms and fractional Fourier transform
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| Publicado no: | SN Applied Sciences vol. 7, no. 11 (Nov 2025), p. 1366 |
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Springer Nature B.V.
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| Acesso em linha: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 2523-3963 | ||
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| 024 | 7 | |a 10.1007/s42452-025-07086-y |2 doi | |
| 035 | |a 3271770017 | ||
| 045 | 2 | |b d20251101 |b d20251130 | |
| 100 | 1 | |a Azmy, Mohamed Moustafa |u Alexandria University, Biomedical Engineering Department, Medical Research Institute, Alexandria, Egypt (GRID:grid.7155.6) (ISNI:0000 0001 2260 6941) | |
| 245 | 1 | |a Detection of electrocardiogram atrial fibrillation using modified multifractal detrended fluctuation analysis based on discrete transforms and fractional Fourier transform | |
| 260 | |b Springer Nature B.V. |c Nov 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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.Article HighlightsThe main contribution of this paper is summarized in the following points:<list list-type="order"><list-item></list-item>ECG features are extracted using MMFDFA built on FRFT and discrete transforms. They were novel techniques extracted from MFDFA and DCT, respectively.<list-item>Detection of AF by comparing different classifiers, such as deep learning and support vector machines, for extracting features of ECG signals.</list-item><list-item>Selection of the MMFDFA_DCHT_FRFT_BILSTM model for extracting heart sound features for achieving the highest classification parameter rates.</list-item> | |
| 653 | |a Feature extraction | ||
| 653 | |a Electrocardiography | ||
| 653 | |a Classification | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Cardiovascular diseases | ||
| 653 | |a Lung diseases | ||
| 653 | |a Machine learning | ||
| 653 | |a Discrete cosine transform | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Principal components analysis | ||
| 653 | |a Fibrillation | ||
| 653 | |a Optimization | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Methods | ||
| 653 | |a Simulated annealing | ||
| 653 | |a Ultrasonic imaging | ||
| 653 | |a Coronary artery disease | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Heart diseases | ||
| 653 | |a Trigonometric functions | ||
| 653 | |a Sinuses | ||
| 653 | |a EKG | ||
| 653 | |a Doppler effect | ||
| 653 | |a Cardiac arrhythmia | ||
| 653 | |a Support vector machines | ||
| 653 | |a Neural networks | ||
| 653 | |a Congestive heart failure | ||
| 653 | |a Cardiovascular disease | ||
| 653 | |a Literature reviews | ||
| 653 | |a Heart | ||
| 653 | |a Parameters | ||
| 653 | |a Social | ||
| 653 | |a Environmental | ||
| 773 | 0 | |t SN Applied Sciences |g vol. 7, no. 11 (Nov 2025), p. 1366 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3271770017/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3271770017/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3271770017/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |