## 4 Discussion
### Quality Assessment of Classification Performance  
As mentioned above, three SVM-based classifiers as well as a trilayered neural network were chosen due to their popularity and the possibility of comparing the classification results [4; 6; 14; 30; 47]. The training time of the models varied due to the different ways in which the algorithms were implemented. The use of parallel computing capabilities in the Matlab environment could significantly reduce the training time despite the large size of the learning set. The testing procedure was therefore no longer such a problem.  
Table 5 reveals that the SVM classifiers with RBF functions achieved the best performance, while the SVM classifiers with a quadratic function produced slightly worse results. This was true for both the complete set of features and the reduced set of features. However, for the SVM method with a quadratic function, the differences were more pronounced when using the reduced set of features, exceeding the standard deviation interval.  
The results achieved by the trilayered neural network for both feature sets were comparable to those of the LS-SVM and SVM classifiers (both with RBF), although they were slightly poorer. The LS-SVM method yielded slightly better results when using a reduced feature set, although the standard deviation was also slightly higher. The SVM method with RBF followed a similar pattern. However, the SVM method with a quadratic function produced considerably lower results with the reduced set of features. Notably, using the LS-SVM method with a reduced set of features yielded the most precise classification outcomes. Nevertheless, removing only seven features may not substantially reduce the workload during the classifier's feature selection and learning phases. Moreover, further decreasing the number of features could potentially hinder the classifier's performance.  
The results of one of the LS-SVM models that underwent \(K\)-fold CV training are presented in Table 6. The training set was composed of randomly selected feature vectors from the entire AFDB, and the \(K\)-fold CV procedure combined both training and test data in a single record. However, for more realistic, critical, and useful results, individual signals were used as test sets with specific feature vectors determined for each. As seen in Table 6, file 5091 had the smallest F1 measure of 72.73%, while file 4015 had the second smallest F1 score of 84.21%. Most subjects had individual results above 96%. A direct comparison with the results presented in ref. [8] showed that for signals 4015 and 5091 (the worst individual results for AF detection in the sense of the smallest F1 scores), the proposed set of features led to better results. The lowest F1 score here was 72.73%, whereas for reference [8] it was below 30%. Files with a segment ratio of \(N_{AF}/N_{seg}\) close to or below unity exhibited the poorest performance, as evidenced by their achievement of the smallest F1 values. This indicated that the AF sections in the AFDB recordings were shorter than others. The values of the PPV ratio (below 80% in both cases) illustrated the difficulty in detecting episodes of AF. However, if the value of the ratio was more significant (such as in file 6453), the F1 index increased to over 96%.