Background: Diagnosis of benign paroxysmal positional vertigo (BPPV) depends on the accurate interpretation of nystagmus induced by positional tests. However, difficulties in interpreting eye-movement often can arise in primary care practice or emergency room. We hypothesized that the use of machine learning would be helpful for the interpretation. Methods: From our clinical data warehouse, 91,778 nystagmus videos from 3467 patients with dizziness were obtained, in which the three-dimensional movement of nystagmus was annotated by four otologic experts. From each labeled video, 30 features changed into 255 grid images fed into the input layer of the neural network for the training dataset. For the model validation, video dataset of 3566 horizontal, 2068 vertical, and 720 torsional movements from 1005 patients with BPPV were collected. Results: The model had a sensitivity and specificity of 0.910 ± 0.036 and 0.919 ± 0.032 for horizontal nystagmus; of 0.879 ± 0.029 and 0.894 ± 0.025 for vertical nystagmus; and of 0.783 ± 0.040 and 0.799 ± 0.038 for torsional nystagmus, respectively. The affected canal was predicted with a sensitivity of 0.806 ± 0.010 and a specificity of 0.971 ± 0.003. Conclusions: As our deep-learning model had high sensitivity and specificity for the classification of nystagmus and localization of affected canal in patients with BPPV, it may have wide clinical applicability.

Even though vestibular rehabilitation therapy (VRT) using head-mounted display (HMD) has been highlighted recently as a popular virtual reality platform, we should consider that HMD itself do not provide interactive environment for VRT. This study aimed to test the feasibility of interactive components using eye tracking assisted strategy through neurophysiologic evidence. HMD implemented with an infrared-based eye tracker was used to generate a virtual environment for VRT. Eighteen healthy subjects participated in our experiment, wherein they performed a saccadic eye exercise (SEE) under two conditions of feedback-on (F-on, visualization of eye position) and feedback-off (F-off, non-visualization of eye position). Eye position was continuously monitored in real time on those two conditions, but this information was not provided to the participants. Electroencephalogram recordings were used to estimate neural dynamics and attention during SEE, in which only valid trials (correct responses) were included in electroencephalogram analysis. SEE accuracy was higher in the F-on than F-off condition (P=0.039). The power spectral density of beta band was higher in the F-on condition on the frontal (P=0.047), central (P=0.042), and occipital areas (P=0.045). Beta-event-related desynchronization was significantly more pronounced in the F-on (-0.19 on frontal and -0.22 on central clusters) than in the F-off condition (0.23 on frontal and 0.05 on central) on preparatory phase (P=0.005 for frontal and P=0.024 for central). In addition, more abundant functional connectivity was revealed under the F-on condition. Considering substantial gain may come from goal directed attention and activation of brain-network while performing VRT, our preclinical study from SEE suggests that eye tracking algorithms may work efficiently in vestibular rehabilitation using HMD.

Although contemporary researchers are concerned about overexposure of portable listening devices (PLD) for adolescents and young adults who often prefer listening to music at high levels for a long time, many of these studies have focused on either comparing sound pressure levels of various kinds of earphones or evaluating the recognition of noise-included hearing loss and listening habits through surveys. Further still, current criteria were developed for occupational noise-induced hearing loss, so there are only a few published guidelines for hearing insults due to recreational noise exposure. The present study, therefore, measures actual listening levels and PLD time in college students using a real-time measurement system and applying that gathered scientific data to the internationally recommended noise exposure standards. Thirty-four college students were asked to listen to music similar to their daily lifestyles for 4-weeks. After installing the application, the Google account that linked to the user’s mobile phone was logged into the server communication. When a subject listened to music, the average and maximum listening levels and listening time could then be recognized as his or her Google account ID and stored in the database for analysis. User data was measured at 1-s intervals and delivered to the main server system every 5 s. The data were analyzed as LZeq for mean levels and LCpeak for maximum levels, and also for PLD use time. The mean of the preferred listening level was 68–70 dB SPL for 4 weeks with long enough break times. That is, the listening levels of college students were not high enough to induce instant hearing loss when they used PLD. However, there was a large individual difference in the listening levels and use times. When applied to three recommended noise exposure criteria, the number of exceeded subjects also differed from 0 to 56.72% depending on the criterion. We thus suggest that appropriate and standardized criteria for music-induced hearing loss might be proposed for recreational PLD users.

The imperialist competitive algorithm combined with chaos theory (CICA) demonstrates excellent performance in global optimization problems. However, its computational complexity increases with the introduction of chaotic maps. To address this, we integrate CICA with a dropout strategy that randomly samples the dimensions of each solution at each iteration of the computation. We investigate the potential of the proposed algorithm with different chaotic maps through six symmetric and six asymmetric benchmark functions. We also apply the proposed algorithm to AUVs’ path planning application showing its performance and effectiveness in solving real problems. The simulation results show that the proposed algorithm not only has low computational complexity but also enhances local search capability near the globally optimal solution with an insignificant loss in the success rate.