Conclusion. The recurrence rate of vertigo due to any cause in patients with vestibular neuritis (VN) was about 26.0% in our study. No clinical characteristics were significantly associated with recurrences. Objective: We aimed to: 1) determine the prevalence of recurrent VN; 2) compare the clinical characteristics and epidemiologic factors of patients with non-recurrent and recurrent VN; and 3) determine the prevalence of benign paroxysmal positional vertigo (BPPV) in patients with VN. Methods: In 131 patients diagnosed as having VN (68 males, aged 49.8 ± 14.9 years and 63 females, aged 57.5 ± 12.5 years), a retrospective chart review and telephone survey about the recurrent vertigo were performed. Vestibular function testing, inner ear magnetic resonance imaging (MRI), and pure tone audiograms were performed on every patient. Vestibular function testing was repeated in patients with recurrences. Results: Fourteen of 131 patients (10.7%) had recurrent VN. The subjective intensity of recurrent VN was less than the first attack. There were no significant differences in clinical characteristics such as accompanying headache or preceding respiratory infection and in epidemiologic risk factors between patients with recurrent and non-recurrent VN. Twenty of 131 patients (15.3%) experienced BPPV during the follow-up period.

Hearing aids are small electronic devices designed to improve hearing for persons with impaired hearing, using sophisticated audio signal processing algorithms and technologies. In general, the speech enhancement algorithms in hearing aids remove the environmental noise and enhance speech while still giving consideration to hearing characteristics and the environmental surroundings. In this study, a speech enhancement algorithm was proposed to improve speech quality in a hearing aid environment by applying noise reduction algorithms with deep neural network learning based on noise classification. In order to evaluate the speech enhancement in an actual hearing aid environment, ten types of noise were self-recorded and classified using convolutional neural networks. In addition, noise reduction for speech enhancement in the hearing aid were applied by deep neural networks based on the noise classification. As a result, the speech quality based on the speech enhancements removed using the deep neural networks—and associated environmental noise classification—exhibited a significant improvement over that of the conventional hearing aid algorithm. The improved speech quality was also evaluated by objective measure through the perceptual evaluation of speech quality score, the short-time objective intelligibility score, the overall quality composite measure, and the log likelihood ratio score.

Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hypergravity accelerator. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accelerometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The method proposed in this paper was trained with transfer learning, a deep learning model that replaced the VGG19 model with a Fully Connected Layer (FCL) and Global Average Pooling (GAP) by converting the vibration signal into a short-time Fourier transform (STFT) or Mel-Frequency Cepstral Coefficients (MFCC) spectrogram and converting the input into a 2D image. As a result, the model proposed in this paper has seven times decreased trainable parameters of VGG19, and it is possible to quantify the severity while looking at the defect areas that cannot be seen with 1D.

To analyze the clinical characteristics of vestibular neuritis patients with minimal canal paresis (canal paresis <25%). Patients clinically diagnosed with vestibular neuritis and treated at our institute (n=201) underwent otoneurological examination and vestibular function tests. Patients were categorized in terms of the results of caloric testing (canal paresis<25%, n=58; canal paresis≥25%, n=143). Clinical characteristics and laboratory outcomes were compared between two groups. Existence of underlying diseases, preceding symptoms, and direction of spontaneous nystagmus were not different between the groups. The mean duration of spontaneous nystagmus was shortest in the minimal canal paresis group (P<0.001) and the direction of spontaneous nystagmus changed more frequently in this group (P<0.001) during recovery. Among the subgroup with minimal canal paresis, only 29.5% had an abnormal finding on the rotatory chair test, as compared to 81.5% of the canal paresis group. The minimal canal paresis group showed higher sensory organization test scores in computerized dynamic posturography. Patients with minimal canal paresis (canal paresis <25%) show similar clinical manifestations as conventional vestibular neuritis patients, but have faster recovery of symptoms and a higher incidence of recovery nystagmus. This finding support that the minimal canal paresis could be considered as a milder type of vestibular neuritis.

The initial software fitting prescribed by the fitting formula largely depends on the patient’s hearing loss, which may not be the optimal preference for a particular user. Certain criteria must also be readjusted by an audiologist to meet the user-specific requirements. Therefore, this study focuses on the novel application of a neural network (NN) technique to build a suitable fitting algorithm with prescribed hearing loss and the corresponding preferred gain to minimize the gap between optimized fittings. The algorithm intended to learn the hearing preferences of an individual user such that the initial fitting may be optimized. These findings demonstrate the efficiency of the algorithm, with and without additional features. Using the clinical fitting data, the average mean square error (MSE) for the simple NN algorithm was 5.4183%. By adding additional features to the data, the algorithm performed better, and the average MSE was as low as 5.2530%. However, the algorithm outperformed Company A fitting software, as the MSE was the highest at 5.4748%. As the company’s automatic fitting has a noticeable discrepancy with clinical fitting records, the impeccable results from this study can lead to a better path towards fitting satisfaction, thus benefiting the hearing-impaired community to a larger extent.