Christian T Herbst1, Jakob Unger2, Hanspeter Herzel3, Jan G Švec4, Jörg Lohscheller5. 1. Voice Research Laboratory, Department of Biophysics, Faculty of Science, Palacký University Olomouc, Tr. 17. listopadu 12, 771 46 Olomouc, Czech Republic. Electronic address: herbst@ccrma.stanford.edu. 2. Institute of Imaging & Computer Vision, RWTH Aachen University, Kopernikusstr. 16, 52074 Aachen, Germany. 3. Institute for Theoretical Biology, Humboldt University Berlin, Invalidenstraße 43, 10115 Berlin, Germany. 4. Voice Research Laboratory, Department of Biophysics, Faculty of Science, Palacký University Olomouc, Tr. 17. listopadu 12, 771 46 Olomouc, Czech Republic. 5. Department of Computer Science, University of Applied Sciences, Schneidershof, 54293 Trier, Germany.
Abstract
INTRODUCTION: In a recent publication, the phasegram, a bifurcation diagram over time, has been introduced as an intuitive visualization tool for assessing the vibratory states of oscillating systems. Here, this nonlinear dynamics approach is augmented with quantitative analysis parameters, and it is applied to clinical laryngeal high-speed video (HSV) endoscopic recordings of healthy and pathological phonations. METHODS: HSV data from a total of 73 females diagnosed as healthy (n = 42), or with functional dysphonia (n = 15) or with unilateral vocal fold paralysis (n = 16), were quantitatively analyzed. Glottal area waveforms (GAW) and left and right hemi-GAWs (hGAW) were extracted from the HSV recordings. Based on Poincaré sections through phase space-embedded signals, two novel quantitative parameters were computed: the phasegram entropy (PE) and the phasegram complexity estimate (PCE), inspired by signal entropy and correlation dimension computation, respectively. RESULTS: Both PE and PCE assumed higher average values (suggesting more irregular vibrations) for the pathological as compared with the healthy participants, thus significantly discriminating healthy group from the paralysis group (P = 0.02 for both PE and PCE). Comparisons of individual PE or PCE data for the left and the right hGAW within each subject resulted in asymmetry measures for the regularity of vocal fold vibration. The PCE-based asymmetry measure revealed significant differences between the healthy group and the paralysis group (P = 0.03). CONCLUSIONS: Quantitative phasegram analysis of GAW and hGAW data is a promising tool for the automated processing of HSV data in research and in clinical practice. Copyright Â
INTRODUCTION: In a recent publication, the phasegram, a bifurcation diagram over time, has been introduced as an intuitive visualization tool for assessing the vibratory states of oscillating systems. Here, this nonlinear dynamics approach is augmented with quantitative analysis parameters, and it is applied to clinical laryngeal high-speed video (HSV) endoscopic recordings of healthy and pathological phonations. METHODS: HSV data from a total of 73 females diagnosed as healthy (n = 42), or with functional dysphonia (n = 15) or with unilateral vocal fold paralysis (n = 16), were quantitatively analyzed. Glottal area waveforms (GAW) and left and right hemi-GAWs (hGAW) were extracted from the HSV recordings. Based on Poincaré sections through phase space-embedded signals, two novel quantitative parameters were computed: the phasegram entropy (PE) and the phasegram complexity estimate (PCE), inspired by signal entropy and correlation dimension computation, respectively. RESULTS: Both PE and PCE assumed higher average values (suggesting more irregular vibrations) for the pathological as compared with the healthy participants, thus significantly discriminating healthy group from the paralysis group (P = 0.02 for both PE and PCE). Comparisons of individual PE or PCE data for the left and the right hGAW within each subject resulted in asymmetry measures for the regularity of vocal fold vibration. The PCE-based asymmetry measure revealed significant differences between the healthy group and the paralysis group (P = 0.03). CONCLUSIONS: Quantitative phasegram analysis of GAW and hGAW data is a promising tool for the automated processing of HSV data in research and in clinical practice. Copyright Â
Authors: Anne Schützenberger; Melda Kunduk; Michael Döllinger; Christoph Alexiou; Denis Dubrovskiy; Marion Semmler; Anja Seger; Christopher Bohr Journal: Biomed Res Int Date: 2016-11-21 Impact factor: 3.411