## 2 Materials and Methods  
Twenty endoscopically recorded HSV data from 20 healthy female subjects were investigated. All recordings were chosen from our existing clinical database. Data collection and usage was approved by the ethic committee of the Medical School at Friedrich-Alexander-University Erlangen-Nurnberg (no. 290_13B). All subjects phonated the vowel /i/ at a comfortable pitch and loudness level during examination. All 20 videos chosen for this study had a comparatively good recording quality with visibility of the entire glottis and good brightness and contrast. The chosen videos were recorded by the clinically used Photron Fastcam MC2 with a spatial resolution of 512 \(\times\) 256 pixels and a frame rate of 4000 fps. All chosen videos included at least 102 consecutive cycles of glottis closing and opening. The sequences of 100 cycles used for analysis ranged in length from 234.75 ms (427.11 Hz F0) to 426.50 ms (234.69 Hz F0). Therefore, with a sampling rate of 4000 Hz the Nyquist sampling criterion was more than satisfied with respect to GAW F0. All recordings were segmented using a modified version of our in house developed software, Glottis Analysis Tools (GAT-2018). This modified version was slightly adjusted to allow a smaller inter seed point distance and a more precise segmentation. The segmentation procedure is depicted in Figure 2 and was as follows:  
1. A region of interest in the video was selected, which included full view of glottis. 2. An interval containing at least 102 cycles during constant phonation was selected. When selecting the intervals, care was taken to choose sections in which the glottis was completely visible and the field of view moved as little as possible. 3. For the initial pre-segmentation, seed points (green crosses in Figure 2(3,4)) were set and brightness thresholds were used. All pixels surrounding a seed point position including the pixel on the position itself are marked, if they are darker than the selected brightness thresholds. 4. Afterwards the seed points were substituted by a regular seed point grid. In the grid region every second pixel was marked with a seed point. The grid was created semi-automatically by using a seed point drawing tool. 5. The brightness thresholds were adjusted yielding the finalized brightness settings. 6. The total GAW (GAW\({}_{\text{T}}\)) was extracted for each recording. The segmentation was performed using regular grids of seed points (i.e., setting the seed points in an organized mesh, as it can be seen in Figure 2(4)). This segmentation style was chosen to ensure a more objective segmentation and minimalize errors by missed small sections of the glottal area. However, this method of segmentation is only applicable for recordings with sufficiently good contrast and clearly visible boundaries of the glottal area. Altogether 20 GAW\({}_{\text{T}}\) signals were calculated. Figure 2: Illustration of the segmentation process: (**1**) Selection of the region of interest; (**2**) Selection of a time interval with constant phonation; (**3**) Rough pre-segmentation; (**4**) Applying a seed point grid; (**5**) Refinement of the brightness thresholds; (**6**) Extraction of the total GAW. Maximum based cycle detection was chosen to determine the cycles of the \(\text{GAW}_{\text{T}}\) signals. Each cycle starts at a significant local maximum and ends one frame before the next one. Beginning with the second detected cycle, as Figure 3 illustrates, for each GAW 5, 10, 20, 50 and 100 consecutive cycles were selected for parameter computation, yielding five \"cycle sets\" per GAW. Since significant influences on the parameter calculation by frequency shifts in the phonation or field of view movements become more likely with growing recording length [43], no longer cycle sets were chosen. Furthermore, greater numbers of cycles will add more analysis time and would not be feasible in a clinical setting. From the cycle sets, 16 different perturbation parameters were calculated.