## 1 Introduction
### Proposed Work and Contribution  
Numerous studies have been published on the use of machine learning methods for AF detection [4; 5; 6; 8; 14; 23; 47]. Most of them reported almost perfect quality indicators for AF detection. Researchers have applied different techniques to find the characteristics of AF. What impact do various characteristics have on the effectiveness of AF detection? Does using characteristics derived from heart rate provide a more effective result than features obtained from electrocardiogram signal examination? To answer these questions, three different signal transformations were applied to obtain an accurate description of the AF phenomenon and learn the selected classifiers.  
This paper presents an improved technique for automatically detecting atrial fibrillation and an assessment of its performance. The proposed algorithm considers the part of the ECG signal originating from atrial activity and the heart rate in the range of specified consecutive heartbeats. For this reason, the feature window gathers different kinds of information from 30 heartbeats without overlapping. The first step of the proposed method is pre-processing, which includes, among other things, signal filtering followed by a feature vector extraction step. Several parameters are applied to describe atrial activity in the ECG signal and heart rhythm.  
The novel approach is intended for the analysis of AA. The processing of AA is carried out with the dual-Q tunable Q-factor wavelet transform (DQ-TQWT), ensemble empirical mode decomposition (EEMD), and the maximal overlap discrete wavelet transform (MODWT) multiscale variances. DQ-TQWT-based features have never been used in the past for AF detection. These treatments provide the parameter values that are included in the feature vector. Several other parameters are also explored, among which it is worth mentioning the Higuchi fractal dimension and various types of entropy, which are known to be effective in AF detection. In the last step, feature selection is carried out to obtain the best performance and reduce the number of features. The genetic algorithm is used with the wrapper method to optimize feature selection for the highest accuracy. Finally, the LS-SVM method is applied for classification purposes. The proposed method outperforms many contemporary techniques developed in this field. The significant contributions of this paper are as follows:  
* The decomposition of part of the ECG signal with the dual-Q tunable Q-factor wavelet transform (DQ-TQWT), ensemble empirical mode decomposition (EEMD), and the corresponding analysis of variances from the maximal overlap discrete wavelet transform (MDDWT);
* The proposal of a differentiated set of features describing atrial activity in the ECG signal and heart rhythm;
* The application of a least-squares support vector machine for classification purposes;
* Experimental results showing that the proposed method's performance in detecting AF episodes was superior to that of state-of-the-art methods.  
The paper is organized as follows. The proposed method is presented in Section 2. Section 3 describes the dataset used and presents the experimental results of the proposed method. Section 4 discusses the results obtained. Finally, the conclusions and suggested future research are presented in Section 5.