EMG-Free Monitorization of the Acoustic Startle Reflex with a Mobile Phone: Implications of Sound Parameters with Posture Related Responses

Abstract

(1) Background: Acute acoustic (sound) stimulus prompts a state of defensive motivation in which unconscious muscle responses are markedly enhanced in humans. The orbicularis oculi (OO) of the eye is an easily accessed muscle common for acoustic startle reaction/response/reflex (ASR) investigations and is the muscle of interest in this study. Although the ASR can provide insights about numerous clinical conditions, existing methodologies (Electromyogram, EMG) limit the usability of the method in real clinical conditions. (2) Objective: With EMG-free muscle recording in mind, our primary aim was to identify and investigate potential correlations in the responses of individual and cooperative OO muscles to various acoustic stimuli using a mobile and wire-free system. Our secondary aim was to investigate potential altered responses to high and also relatively low intensity acoustics at different frequencies in both sitting and standing positions through the use of biaural sound induction and video diagnostic techniques and software. (3) Methods: This study used a mobile-phone acoustic startle response monitoring system application to collect blink amplitude and velocity data on healthy males, aged 18-28 community cohorts during (n = 30) in both sitting and standing postures. The iPhone X application delivers specific sound parameters and detects blinking responses to acoustic stimulus (in millisecond resolution) to study the responses of the blinking reflex to acoustic sounds in standing and sitting positions by using multiple acoustic test sets of different frequencies and amplitudes introduced as acute sound stimuli (<0.5 s). The single acoustic battery of 15 pure-square wave sounds consisted of frequencies and amplitudes between 500, 1000, 2000, 3000, and 4000 Hz scales using 65, 90, and 105 dB (e.g., 3000 Hz_90 dB). (4) Results: Results show that there was a synchronization of amplitude and velocity between both eyes to all acoustic startles. Significant differences (p = 0.01) in blinking reaction time between sitting vs. standing at the high intensity (105 dB) 500 Hz acoustic test set was discovered. Interestingly, a highly significant difference (p < 0.001) in response times between test sets 500 Hz_105 dB and 4000 Hz_105 dB was identified. (5) Conclusions: To our knowledge, this is the first mobile phone-based acoustic battery used to detect and report significant ASR responses to specific frequencies and amplitudes of sound stimulus with corresponding sitting and standing conditions. The results from this experiment indicate the potential significance of using the specific frequency, amplitude, and postural conditions (as never before identified) which can open new horizons for ASR to be used for diagnosis and monitoring in numerous clinical and remote or isolated conditions.

Keywords: acoustic; blink; mobile; reaction; reflex; response; sound; startle.

Figure 1

Blink–reflex detection using eye aspect ratio (EAR) across time and the geometric anchors (P1–6). The equation that provides an output for the eye size and hence acts as a blink marker based on Soukupová and Čech (2016).

Figure 2

Seated volunteer with Sound Stimulus App, iPhone X, insert earphones, and noise reduction cups.

Figure 3

Display of eyelid geodynamics across collection time (~10 s). Left eye (blue) and right eye (red) response amplitudes and velocities. RT = Reaction time of blink reflex to acoustic stimuli, Green line = ASR sound stimulus, Black line = Blink reflex.

Figure 4

Radar plots showing the average response times (s) and amplitudes for left (L, blue) and right (R, orange) eyes and sitting (red) and standing (green) position/postures for the multiple stimuli. RT = Reaction time (s), RA = Blink response amplitude (given by change in EAR during blink). * Statistically significant p < 0.05.

Figure 5

Average response times at varying frequencies and intensities. Significant amplitude-specific differences demonstrated within 500 Hz and 4000 Hz frequencies between 65 and 105 dB. Highly significant frequency-specific differences between 500 and 4000 Hz were also identified from 105 dB amplitudes. Statistically significant * p < 0.05, ** p < 0.001.

Figure 6

Average blink response times of sitting and standing to various frequencies at 65 dB. Significant differences were found between 500 and 1000 Hz and 1000 and 4000 Hz. R² = fit of the line to the data, minimal trend in RT that can be explained by the frequency (R² = 0.087). * Statistically significant p < 0.05.

Figure 7

Average blink response times of sitting and standing to various frequencies at 90 dB. Significant differences were found between 500 and 4000 Hz and 1000 and 4000 Hz Indication of reduction in RT with increasing frequency, moderate linear trend (R² = 0.680). * Statistically significant p < 0.05.

Figure 8

Average blink response times of sitting and standing to various frequencies at 105 dB. Significant differences were found between 500 and 2000, 3000, and 4000 Hz and between 1000 and 4000 Hz. Indication of reduction in RT with increasing frequency, moderate to strong linear trend (R² = 0.797). * Statistically significant p < 0.05.