## 3 Results
### Validation against the Human Scorers
It was also close to the sensitivity of the human scorers S\({}_{1}\) and S\({}_{3}\). This means that AMARA's sensitivity is reduced due to rejection of MAs within identified movement epochs. One explanation might be that small (in terms of amplitude) artifacts close to large artifacts were not detected. The large artifacts possibly elevated the detection threshold above the amplitude of small artifacts. Subtle movements accounted most likely for the remainder of the undetected artifacts of AMARA (461 of 823, \(N\!I=44\%\)). Slow movements below the detection level may have caused this problem; it may also have been possible that some artifacts were not related to movements. Figure 3: Summary of the validation results. (**a**) Number of total artifacts (\(N_{Method}\)), subdivided into false positives (_FP,_ black) and true positives within ‘real’ artifacts (grey) and not identified (_NI, white_). The total number of artifacts found by each algorithm/human scorer is represented by the sum of ‘within ”real” artifact’ + _FP_. The horizontal line represents \(N_{real}=6228\); (**b**) The mean number of artifacts detected within one “real” artifact (\(\overline{N}_{real}\)); (**c**) The mean length (\(L\)) of identified artifact segments; (**d**) The average number (\(\overline{N}\)) of artifacts identified per recorded signal ([O\({}_{2}\)Hb] or [HHb]). S\({}_{1}\)–S\({}_{4}\): human scorers no. 1–4. Error bars always represent the standard deviation. Figure 4: **(a)** Comparison of the performance in artifact identification of the different methods and human scorers in case of the occurrence of two successive artifacts. \(x(t)\): time course (8 min) of [O\({}_{2}\)Hb] of an overnight recording in one subject. Black bars below the tracings mark identified artifacts. MARA, ABAMAR, AMARA: The reconstructed signals were based on the different methods. MARA identified five artifacts and always corrected the baseline to the baseline of the previous segment. ABAMAR identified two artifacts; the artifact was replaced by a constant value and the baselines were corrected to the value before the occurrence of the artifacts. AMARA identified three artifacts (indicated by the three black bars on top of the two longer ones below the tracing) occurring within two movement periods. The baselines before and after an artifact do not have to be at the same level due to the applied reconstruction. S\({}_{1}\)–S\({}_{4}\): reconstructed signal based on the artifacts identified by human scorers S\({}_{1}\)–S\({}_{4}\). The human scorers identified one artifact or two artifacts, which might have canceled out physiological information (_i.e._, signal trends just before, after and between the artifacts). \(a_{x}\), \(a_{y}\) and \(a_{z}\) represent the \(x\)-, \(y\)- and \(z\)-axis of the acceleration sensor data; (**b**) Time course (ca. 8.5 h) of cerebral [O\({}_{2}\)Hb] (sensor placed approximately above left prefrontal cortex) in a whole-night recording of a subject (different subject than the one illustrated in Figure 4a). \(x\): uncorrected signal; a number of obvious movement related artifacts are visible. The AMARA reconstruction retained the increasing trend of the signal across the night, which was visible in the original signal. ABAMAR and MARA however, changed the trend of the original signal. An obvious artifact after approximately 4 h was not recognized by ABAMAR, because the motion change was only slightly above the noise level in the acceleration signal and thus below the detection threshold. However, also without this jump, a decreasing trend remains in the ABAMAR signal. Reconstruction of AMARA was applied to reconstruct the signal based on the artifacts identified by S\({}_{1}\)–S\({}_{4}\). Acceleration signals as described in Figure 4a. The total number of artifacts detected was much higher for AMARA (14,695) than for the human scorers (\(\sim\)5000-7500), ABAMAR (8478) and MARA (12,732, see Figure 3a).