## 4 Discussion
### Comparison with State-of-the-Art Methods  
Several methods, including the proposed algorithm, were evaluated for their effectiveness in detecting atrial fibrillation using the MIT-BIH Atrial Fibrillation Database. The evaluation of the methods was conducted with the utmost rigor, taking into account the variations in features, data window size, and other parameters. The assessment was based on the values of the Acc, F1, Sen, Spec, and PPV parameters, which were carefully analyzed to determine the effectiveness of the methods. The results of this comparative analysis can be found in Table 7. This table provides an overview of the work of researchers on AF detection using the MIT-BIH AFDB database and includes the results obtained using SVM methods. In the study by [PERSON] [26], five time-domain features were proposed for detecting AF based on inter-beat intervals. These features included three entropy-based measures (sample entropy, coefficient of sample entropy, and Shannon entropy) and two measures based on the characteristics of the RR interval (root mean square of successive difference and normalized root mean square of successive difference). It is important to note that the results were obtained using an SVM classifier. The accuracy of the results varied depending on the duration of the time window and the number of beats used. For a 30-second window, the accuracy obtained was 96.98%, while using up to 300 beats resulted in a slightly lower accuracy of 96.45%. The papers [4; 32] proposed using only the heart rhythm signal to gather information. Five features of the heart rhythm signal were used in ref. [32] along with a simple linear classifier. This approach achieved Sen, Spec, PPV, and Acc values of 95.42%, 96.12%, 94.97%, and 95.62%, respectively, for a window width of 130 beats. [PERSON]'s work [4] demonstrated a significant expansion of the HR signal parameter set. The LSVM classifier used in this study produced some of the highest classification results to date, with a sensitivity of 98.94%, specificity of 98.8%, positive predictive value of 98.39%, accuracy of 98.86%, and F1 score of 98.66%. An additional aggregation stage was included in the research to provide even more reliable information on patient risk. It is worth noting that [PERSON]'s work [4] used a Lagrangian SVM classifier instead of the LS-SVM classifier utilized in our approach. In the work of [PERSON] et al. [27], a multi-layer deep learning network featuring convolutional and recurrent layers was used to extract high-level features from segments of RR intervals. This method had a sensitivity of 98.98% and specificity of 96.95% and was validated through a five-fold CV procedure. The great benefit of this approach was that it necessitated a small computational effort, allowing 24-hour ECG recordings to be processed in less than a second. However, its positive predictive value (PPV) of 95.76% was lower than the proposed method's result. In another study, [PERSON] et al. [30] proposed the use of long short-term memory (LSTM) and a CNN, but the results were much worse than those obtained with the proposed method, with an F1 score of 86%. A similar approach was proposed in ref. [39]. However, the obtained sensitivity and specificity were 97.87% and 99.29%, respectively. This specificity value is the highest in our comparison. However, the F1 score was lower than the highest value achieved by the proposed method. The results achieved were among the lowest presented in this summary (F1 = 86%). The features obtained from two-lead ECG recordings were processed with the wavelet packet transform, and the random process theory was used to provide efficient feature selection. A neural network was used as a classifier. The sensitivity and specificity obtained were 98.7% and 98.9%, respectively, representing some of the best values to date [2]. The lightweight 1D CNN model proposed in ref. [42] achieved outstanding results in AF detection, but the performance varied depending on the data length.