## 5 Discussion  
After diving deep into the results, we found that the dropped windows can restructure the AF cases and it is also possible that many new short AF periods are generated from a long AF period, so it is important not to make decisions on corrupted data, but to add additional heuristics to data processing. We also took an overview on AF episode durations and inspected how minimum and maximum lengths of AF episode changes after dropping valuable windows. In the most outlier scenario, the maximum duration of an AF episode was dropped from 22 min 19 s 294 ms to 3 min 31 s 125 ms. It means that more than a 20 min long episode is fully lost. In the same scenario, if every second window is dropped, only a 3 min 786 ms long episode is kept. Considering the minimum AF durations and due to the dropped windows, we can have very short, so-called micro AFs. The minimum duration is always under 1 s after dropping windows. In the original data, we did not find AFs shorter than 1 s. The average AF duration can also drastically change. The most outlier case was a decrease from 542 s to 131 s.  
Finally, we added the second level of aggregation to the data with reduced data quality. We tested the AF classification system with multiple algorithms. It was observed that the main issue is the number of lost episodes and this is valid for the long ones, but in many cases the number of short episodes started to rise. The final classification changed in many cases. Since the classification algorithms are not as sensitive as the AF detection algorithm,  
Figure 10: Histogram of execution times in AF classification algorithm. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Use Case**} & **Data Quality Loss in** & **Data Quality Loss If** \\  & **Simulation-Based** & **Every Second Window** \\  & **Dropping** & **Is Dropped** \\ \hline Average number of AF episodes & 2.5\% & 1.75\% \\ Average number of AF windows & 4.25\% & 14.5\% \\ Classification \#1 & 12.99\% & 22.07\% \\ Classification \#2 & 20.78\% & 14.29\% \\ Classification \#3 & 12.74\% & 16.87\% \\ Classification \#4 & 12.85\% & 17.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data quality losses after dropping data windows. outstanding changes can be seen only in the first aggregation level. If the output of the classification changes, it may lead to wrong therapy or an increased risk of stroke. The first classification algorithm is the most common, which distinguishes four classes, first detected, paroxysmal, persistent and permanent [21]. Here, our ECG records are not longer than 24 h, so the persistent and permanent classes cannot be authoritative considering the data. Nevertheless, first detected and paroxysmal classes can be measured in binary interpretation. After evaluating the simulations, we found no data quality reduction. The second classification algorithm [22] has five classes, considering the length of the AF episodes. Here, we have AF episodes that last longer than 24 h, episodes which are at least 6 h but at most 24 h long, AF episodes from 1 to 6 h, periods between 10 min and 1 h and intervals shorter than 10 min. According to the classification method, AF episodes shorter than 10 min are not considered as real AF episodes. In our database, we did not have AF episodes longer than 6 h. It was observed during our simulation that there were no missed AF episodes between 1 and 6 h length, but we can see a decrease in the last class. Later, we evaluated the results using the third classification algorithm [22] that has two classes but here, the recurrence rate is more important than the length. If the number of AF episodes are at least five, ECG data receives a 5+ label, if it is less than five, the recurrence is not relevant. It is observed that during our simulation, we lost some AF episodes, so there is more than 15% reduction in the data quality. The fourth algorithm [23] classifies the ECG data based on the duration of AF episodes, but now the subclinical AF episodes are taken into account.