Advanced waveform decomposition for high-speed videoendoscopy analysis.

This article presents a novel approach to analyze nonperiodic vocal fold behavior of high-speed videoendoscopy (HSV) data. Although HSV can capture true vibrational motions of the vocal folds, its clinical advantage over the videostroboscopy has not widely been accepted. One of the key advantages of the HSV over the videostroboscopy is its ability to capture vocal folds' nonperiodic behavior, which is more prominent in pathological vocal folds. However, such nonperiodicity in the HSV data has not been fully explored quantitatively beyond simple perturbation analysis. This article presents an advanced waveform modeling and decomposition technique for HSV-based waveforms. Waveforms are modeled to have three components: harmonic signal, deterministic nonharmonic signal, and random nonharmonic signal. This decomposition is motivated by the fact that voice disorders introduce signal content that is nonharmonic but carries deterministic quality such as subharmonic or modulating content. The proposed model is aimed to isolate such disordered behaviors as deterministic nonharmonic signal and quantify them. In addition to the model, the article outlines model parameter estimation procedures and a family of harmonics-to-noise ratio (HNR) parameters. The proposed HNR parameters include harmonics-to-deterministic-noise ratio (HDNR) and harmonics-to-random-noise ratio. A preliminary study demonstrates the effectiveness of the extended model and its HNR parameters. Vocal folds with and without benign lesions (Nwith = 13; Nwithout = 20) were studied with HSV glottal area waveforms. All three HNR parameters significantly distinguished the disordered condition, and the HDNR reported the largest effect size (Cohen's d = 2.04).