An Improved Approach for Atrial Fibrillation Detection in Long-Term ECG Using Decomposition Transforms and Least-Squares Support Vector Machine

• Published in: Applied sciences Vol. 13; no. 22; p. 12187
• Main Author: Pander, Tomasz
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2023
• Subjects: Analysis; Artificial intelligence; Atrial fibrillation; Blood pressure; Cardiac arrhythmia; Deep learning; Discriminant analysis; dual-Q tunable-Q wavelet transform; ECG processing; EEMD; Electrocardiogram; Electrocardiography; Heart; Heart beat; Heart rate; LS-SVM classifier; Machine learning; MODWT; Neural networks; Sinuses; Support vector machines; Wavelet transforms; Wearable computers
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app132212187

Atrial fibrillation is a common heart rhythm disorder that is now becoming a significant healthcare challenge as it affects more and more people in developed countries. This paper proposes a novel approach for detecting this disease. For this purpose, we examined the ECG signal by detecting QRS complexes and then selecting 30 successive R-peaks and analyzing the atrial activity segment with a variety of indices, including the entropy change, the variance of the wavelet transform indices, and the distribution of energy in bands determined by the dual-Q tunable Q-factor wavelet transform and coefficients of the Hilbert transform of ensemble empirical mode decomposition. These transformations provided a vector of 21 features that characterized the relevant part of the electrocardiography signal. The MIT-BIH Atrial Fibrillation Database was used to evaluate the proposed method. Then, using the K-fold cross-validation method, the sets of features were fed into the LS-SVM and SVM classifiers and a trilayered neural network classifier. Training and test subsets were set up to avoid sampling from a single participant and to maintain the balance between classes. In addition, individual classification quality scores were analyzed for each signal to determine the dependencies of the classification quality on the subject. The results obtained during the testing procedure showed a sensitivity of 98.86%, a positive predictive value of 99.04%, and a classification accuracy of 98.95%.