## 3 Results  
### Validation against the Human Scorers  
In total, 6228 \"real\" artifacts were identified (_i.e._, at least three out of four human scorers agreed). An overview of the validation results can be found in Table 2 and Figure 3. The sensitivity to \"real\" \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \(\mathbf{S_{1}}\) & \(\mathbf{S_{2}}\) & \(\mathbf{S_{3}}\) & \(\mathbf{S_{4}}\) & **AMARA** & **MARA** & **ABAMAR** & **AMARAacc** \\ \hline \(N_{Method}\) & 5553 & 5011 & 6998 & 7526 & 14,695 & 12,732 & 8478 & 11,334 \\ \(\bar{N}\) & \(58\pm 24\) & \(52\pm 22\) & \(73\pm 30\) & \(78\pm 28\) & \(153\pm 42\) & \(133\pm 60\) & \(88\pm 29\) & \(118\pm 35\) \\ \(L\) [s] & \(101\pm 42\) & \(74\pm 29\) & \(43\pm 18\) & \(87\pm 42\) & \(7\pm 2\) & \(3\pm 1\) & \(24\pm 10\) & \(26\pm 5\) \\ \(NI\) & 306 & 866 & 269 & 768 & 823 & 1427 & 487 & 362 \\ \(\bar{N}\) & (4.9\%) & (13.9\%) & (4.3\%) & (12.3\%) & (13.2\%) & (22.9\%) & (7.8\%) & (5.8\%) \\ \(S\) & 95.1\% & 86.1\% & 95.7\% & 87.7\% & 86.8\% & 77.1\% & 92.2\% & 94.2\% \\ \(\bar{N}_{real}\) & \(1\pm 0.08\) & \(1\pm 0.12\) & \(1\pm 0.08\) & \(1.1\pm 0.61\) & \(2.3\pm 1.9\) & \(2.3\pm 2.4\) & \(1.2\pm 0.6\) & \(1.4\pm 1.1\) \\ \(FP\) & 280 (5.0\%) & 144 (2.9\%) & 1301 (18.6\%) & & 674 & 2374 & 1779 & 1889 & 3088 \\  & & & & (9.0\%) & (16.2\%) & (14.0\%) & (22.3\%) & (27.2\%) \\ \hline \end{tabular}
\end{table} 
Table 2: Performance of artifact detection of AMARA, MARA, ABAMAR and the human scorers \(\mathrm{S_{1}-S_{4}}\). In total, each scorer analyzed 96 signals. \(N_{Method}\): number of total artifacts found. \(\bar{N}\): average number of artifacts scored per signal \(\pm\) SD. \(L\): average length of the artifacts in \(\mathrm{s}\pm\) SD. \(NI\): not identified artifacts; as percentage of “real” artifacts \(N_{real}\) in parenthesis. \(S\): sensitivity \(S=(NI-N_{real})/N_{real}\). \(\bar{N}_{real}\): average number of artifacts detected within a “real” artifact \(\pm\) SD. “Real” artifacts are those identified by at least 3 of the 4 human scorers. The total number of “real” artifacts was 6228. \(FP\): number of false positive detections; as percentage of the total number \(N_{Method}\) of artifacts in parenthesis. \begin{table}
\begin{tabular}{c c c c c|c c} \hline  & \multicolumn{4}{c|}{**Artifacts Identified**} & \multicolumn{4}{c}{**Movements Detected**} \\ \cline{2-7}  & **MARA** & **AMARA** & **AMARAaece** & **ABAMAR** & **ABAMAR** & **AMARAaece** \\ \hline \(NI\) & 0 & 2739 & 2271 & 2410 & 0 & 686 \\  & (0\%) & (21.5\%) & (17.8\%) & (18.9\%) & (0\%) & (8.1\%) \\ \(S\) & 100\% & 78.5\% & 82.2\% & 81.1\% & 100\% & 91.9\% \\ \(\bar{N}_{real}\) & \(1\pm 0.00\) & \(1\pm 0.07\) & \(1\pm 0.05\) & \(1\pm 0.00\) & \(1\pm 0.00\) & \(1.2\pm 0.78\) \\ \(FP\) & 0 & 6793 & 5116 & 3195 & 0 & 1969 \\  & (0\%) & (46.2\%) & (45.1\%) & (37.7\%) & (0\%) & (17.4\%) \\ \hline \end{tabular}
\end{table} 
Table 3: Left part: Detection performance comparing artifacts identified by AMARA, AMARAacc and ABAMAR with MARA. The MARA artifacts (12,732 in total) were considered as the true ones, _i.e._, MARA was the reference for comparison with the other algorithms. Thus, MARA has a sensitivity of 100%, no false positives and no missed ones. Right part: Comparison of the movement detection process; the ABAMAR (where the artifact detection only relies on the acceleration signals) artifacts (8478 total) have been set as true. Please refer to Table 2 for the description of the variables. AMARAacc: Movement detection part (Section 2.1.1) of the AMARA algorithm. Thus, only artifacts identified with accelerometer data were considered as artifacts. artifacts of AMARA was \(S=86.8\%\). The human scorers reached a sensitivity of \(S=86\%\)-96%, ABAMAR 92.2% and MARA 77.1%. AMARA had a higher sensitivity when only the movement detection was taken into account (taking place before the MA detection; \(S=94.2\%\), AMARAacc in Table 2). This was higher than ABAMAR's sensitivity, which relies on its detection only on the accelerometer signal and is directly comparable.