Nonlinear Analyses of Elicited Modal, Raised, and Pressed Rabbit Phonation

Abstract

Objectives/Hypothesis The purpose of this study was to use nonlinear dynamic analysis methods such as phase space portraits and correlation dimension (D2) as well as descriptive spectrographic analyses to characterize acoustic signals produced during evoked rabbit phonation. Methods Seventeen New Zealand white breeder rabbits were used to perform the study. A Grass S-88 stimulator (SA Instrumentation, Encinitas, CA) and constant current isolation unit (Grass Telefactor, model PSIU6; West Warwick, RI) were used to provide electrical stimulation to laryngeal musculature, and transglottal airflow rate and stimulation current (mA) were manipulated to elicit modal, raised intensity, and pressed phonations. Central 1 second portions of the most stable portion of the acoustic waveform for modal, raised intensity, and pressed phonations were edited, and then analyzed via phase space portraits, Poincaré sections, and the estimation of the correlation dimension (D2). In an attempt to limit the effects of the highly variable and nonstationary characteristics of some of the signals being analyzed, D2 analysis was also performed on the most stable central 200 ms portion of the acoustic waveform. Descriptive analysis of each phonation was also conducted using sound spectrograms. Results Results showed that the complexity of phonation and the subsequent acoustic waveform is increased as transglottal airflow rate and degree of glottal adduction is manipulated in the evoked rabbit phonation model. In particular, phonatory complexity, as quantified via correlation dimension analyses and demonstrated via spectrographic characteristics, increases from “modal” (i.e., phonation elicited at just above the phonation threshold pressure) to raised intensity (phonation elicited by increasing transglottal airflow rate) to pressed (phonation elicited by increasing the stimulation current delivered to the larynx). Variations in a single dynamic dimension (airflow rate or adductory force) resulted in significantly increased productions of nonlinear phenomenon, including bifurcations from periodicity to regions of subharmonic content, F0 and harmonic jumps, and evidence of periodicity within aperiodic regions (“chaos”). Conclusions The evoked rabbit phonation model described in this study allows for the elicitation of various types of phonations under controlled conditions and therefore, has the potential to provide insight regarding important variables that may elicit examples of nonlinear phenomena such as subharmonics and deterministic chaos.

Nonstimulated rabbit phonation model: Cricothyroid approximation

“…Acoustic analysis of elicited phonation samples revealed vocal intensity and fundamental frequency values that are consistent with modal intensity phonation using pulsed electrical stimulation . Sound spectrograms from the present study revealed modal intensity‐elicited phonations, characterized by strong harmonics and rare occurrences of aperiodicity, as described previously . Additionally, the aerodynamic properties of elicited phonations, notably airflow rate and subglottal pressure, are in congruence with expectations for rabbit‐evoked modal intensity phonation.…”

Investigation of phonatory characteristics using ex vivo rabbit larynges

“…However, Mills et al (2017) achieved phonation at lower flow rates starting from $17 mls À1 . Also the experiments reported by Awan et al (2014) showed good consistency with our experiments regarding flow rates (85 mls À1 -144 mls À1 ). However, we could apply flow rates of up to 180 mls À1 (25% higher) yielding regular vibrations.…”

“…One study focused on establishing the ex vivo, and another the in vivo, rabbit model as a feasible model for phonatory analysis (Maytag et al, 2013;Ge et al, 2009). Awan et al (2014) analyzed in vivo nonlinear dynamic phenomena by varying flow rate and adduction levels. However, only one ex vivo study performed detailed parameter studies (Mills et al, 2017).…”

Using Rate of Divergence as an Objective Measure to Differentiate between Voice Signal Types Based on the Amount of Disorder in the Signal

“…9,10 Nonlinear dynamic measurements such as correlation dimension (D2) or largest Lyapunov exponent are successful in differentiating between normal and irregular phonations, while traditional perturbation methods fail. 9–12 Recently, Sprecher (2010) introduced the addition of a fourth voice type. This scheme reclassified type 3 voice as chaotic with a finite dimension, and defined type 4 voice as chaotic with infinite dimension.…”