## 5 Conclusions  
Atrial fibrillation is a condition that can have dangerous consequences for patients' health if left undetected. Diagnosing this type of arrhythmia requires the long-term monitoring of the heart, e.g., recording an ECG signal. Episodes of atrial fibrillation may be short and sporadic, making them difficult to detect. However, using automatic AF detection methods in the ECG signal can improve the AF detection performance in long-term signals. This study proposed and evaluated a novel and reliable method for detecting atrial fibrillation episodes in long-term ECG signals. Three signal decomposition techniques were used in this study, i.e., the dual-Q tunable-Q wavelet transform, ensemble empirical mode decomposition, and the corresponding analysis of variance from the maximum overlap discrete wavelet transform. Various features were proposed, including one extracted from the heart rate and others defined based on the segment of atrial activity in the ECG signal after decomposition \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Window**} & \multirow{2}{*}{**Classifiers**} & \multicolumn{6}{c}{**Results (\%)**} \\ \cline{5-8}  & & & & **Sen** & & **Spec** & **PPV** & **Acc** & **F1** \\ \hline [PERSON] et al., 2017 [26] & RR interval & 300 beats & SVM & 96.81 & 96.20 & n/a & 96.45 & n/a \\  & ECG features & 30 s & 94.27 & 98.84 & & 96.98 & \\ [PERSON] et al., 2018 [32] & HR irregularity features & 21 & Linear classifier & 95.42 & 96.12 & 94.97 & 95.62 & n/a \\ [PERSON] et al., 2019 [22] & RR intervals, Markov matrix & 60 s & RF & 97.7 & 98.5 & n/a & n/a & 97.7 \\ [PERSON] et al., 2019 [27] & RR interval & 10 s & CNN-LSTM & 98.98 & 96.95 & 95.76 & 97.8 & n/a \\ [PERSON] et al., 2019 [4] & HR irregularity features & 21 beats & LSVM & 98.94 & 98.80 & 98.39 & 98.86 & 98.66 \\ [PERSON] et al., 2020 [10] & Frequency features & 5 s & decision tree & 97.9 & 99.6 & n/a & n/a & n/a \\ [PERSON] et al., 2020 [30] & 83 RR and ECG features & 10 s & LSTM & 85.0 & n/a & 86.0 & 86.5 & 86.0 \\ [PERSON] et al., 2020 [2] & Signal & 10 s & ANN & 98.7 & 98.9 & n/a & 98.8 & n/a \\ [PERSON] et al., 2021 [8] & RR intervals AA features & 30 beats & RF & 98.00 & 97.4 & n/a & 97.6 & 97.1 \\ [PERSON] et al., 2021 [39] & Signal & 187 samples & CNN-LSTM & 97.87 & 99.29 & n/a & n/a & n/a \\ [PERSON] et al., 2023 [46] & Signal & 10 s & D2 AFNet & 98.39 & 98.57 & **99.19** & 98.45 & 98.78 \\ [PERSON] et al., 2023 [42] & Signal & 5 s & 99.26 & 95.30 & & 97.68 & \\ [PERSON] et al., 2023 [42] & Signal & 10 s & 1D-CNN & **99.72** & 93.23 & n/a & 97.50 & n/a \\  & 30 s & 98.57 & 93.99 & & 96.70 & \\ Proposed method, 2023 & RR interval AA features & 30 beats & LS-SVM & 98.86 & 98.96 & 99.04 & **98.95** & **98.84** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: A summary of published results for existing AF detection methods using the MIT-BIH Atrial Fibrillation Database in comparison to the proposed work (n/a—not available; the best results are in bold). with the abovementioned transforms. The LS-SVM classifier was applied with a five-fold cross-validation procedure, outperforming other automatic AF detection methods proposed in the literature. According to the numerical experiments, the first two transformations (DQ-TQWT and EEMD) were the most useful for AF detection. The parameters obtained from these experiments were essential components of a vector of features describing atrial activity in AF detection. Feature selection was also performed such that the selected subset of features provided the same or even better AF detection results than the complete set of features. The algorithm achieved a high accuracy, sensitivity, specificity, PPV, and F1 score, making it suitable for clinical applications. Individual ECG recordings were also classified and compared with the results of the five-fold CV method.