An environment-adaptive management algorithm for hearing-support devices incorporating listening situation and noise type classifiers.

In order to provide more consistent sound intelligibility for the hearing-impaired person, regardless of environment, it is necessary to adjust the setting of the hearing-support (HS) device to accommodate various environmental circumstances. In this study, a fully automatic HS device management algorithm that can adapt to various environmental situations is proposed; it is composed of a listening-situation classifier, a noise-type classifier, an adaptive noise-reduction algorithm, and a management algorithm that can selectively turn on/off one or more of the three basic algorithms-beamforming, noise-reduction, and feedback cancellation-and can also adjust internal gains and parameters of the wide-dynamic-range compression (WDRC) and noise-reduction (NR) algorithms in accordance with variations in environmental situations. Experimental results demonstrated that the implemented algorithms can classify both listening situation and ambient noise type situations with high accuracies (92.8-96.4% and 90.9-99.4%, respectively), and the gains and parameters of the WDRC and NR algorithms were successfully adjusted according to variations in environmental situation. The average values of signal-to-noise ratio (SNR), frequency-weighted segmental SNR, Perceptual Evaluation of Speech Quality, and mean opinion test scores of 10 normal-hearing volunteers of the adaptive multiband spectral subtraction (MBSS) algorithm were improved by 1.74 dB, 2.11 dB, 0.49, and 0.68, respectively, compared to the conventional fixed-parameter MBSS algorithm. These results indicate that the proposed environment-adaptive management algorithm can be applied to HS devices to improve sound intelligibility for hearing-impaired individuals in various acoustic environments.

A new asymmetric directional microphone algorithm with automatic mode-switching ability for binaural hearing support devices.

For hearing support devices, it is important to minimize the negative effect of ambient noises for speech recognition but also, at the same time, supply natural ambient sounds to the hearing-impaired person. However, conventional fixed bilateral asymmetric directional microphone (DM) algorithms cannot perform in such a way when the DM-mode device and a dominant noise (DN) source are placed on the same lateral hemisphere. In this study, a new binaural asymmetric DM algorithm that can overcome the defects of conventional algorithms is proposed. The proposed algorithm can estimate the position of a specific DN in the 90°-270° range and switch directional- and omnidirectional-mode devices automatically if the DM-mode device and the DN are placed in opposite lateral hemispheres. Computer simulation and KEMAR mannequin recording tests demonstrated that the performance of the conventional algorithm deteriorated when the DM-mode device and the DN were placed in the opposite hemisphere; in contrast, the performance of the proposed algorithm was consistently maintained regardless of directional variations in the DN. Based on these experimental results, the proposed algorithm may be able to improve speech quality and intelligibility for hearing-impaired persons who have similar degrees of hearing impairment in both ears.

Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning.

BACKGROUND AND OBJECTIVE: Rotator cuff muscle tear is one of the most frequent reason of operations in orthopedic surgery. There are several clinical indicators such as Goutallier grade and occupation ratio in the diagnosis and surgery of these diseases, but subjective intervention of the diagnosis is an obstacle in accurately detecting the correct region. METHODS: Therefore, in this paper, we propose a fully convolutional deep learning algorithm to quantitatively detect the fossa and muscle region by measuring the occupation ratio of supraspinatus in the supraspinous fossa. In the development and performance evaluation of the algorithm, 240 patients MRI dataset with various disease severities were included. RESULTS: As a result, the pixel-wise accuracy of the developed algorithm is 0.9984 ± 0.073 in the fossa region and 0.9988 ± 0.065 in the muscle region. The dice coefficient is 0.9718 ± 0.012 in the fossa region and 0.9463 ± 0.047 in the muscle region. CONCLUSIONS: We expect that the proposed convolutional neural network can improve the efficiency and objectiveness of diagnosis by quantifying the index used in the orthopedic rotator cuff tear.

A data-driven artificial intelligence model for remote triage in the prehospital environment.

In a mass casualty incident, the factors that determine the survival rate of injured patients are diverse, but one of the key factors is the time for triage. Additionally, the main factor that determines the time of triage is the number of medical personnel. However, when relying on a small number of medical personnel, the ability to increase survivability is limited. Therefore, developing a classification model for survival prediction that can quickly and precisely triage via wearable devices without medical personnel is important. In this study, we designed a consciousness index to substitute the factor by manpower and improved the classification accuracy by applying a machine learning algorithm. First, logistic regression analysis using vital signs and a consciousness index capable of remote monitoring through wearable devices confirmed the high efficiency of the consciousness index. We then developed a classification model with high accuracy which corresponds to existing injury severity scoring systems through the machine learning algorithms. We extracted 460,865 cases which met our criteria for developing the survival prediction from the national sample project in the national trauma databank which contains 408,316 cases of blunt injury and 52,549 cases of penetrating injury. Among the dataset, 17,918 (3.9%) cases died while the other survived. The AUCs with 95% confidence intervals (CIs) for the different models with the proposed simplified consciousness score as follows: RTS (as baseline), 0.78 (95% CI = 0.775 to 0.785); logistic regression, 0.87 (95% CI = 0.862 to 0.870); random forest, 0.87 (95% CI = 0.862 to 0.872); deep neural network, 0.89 (95% CI = 0.882 to 0.890). As a result, we confirmed the possibility of remote triage using a wearable device. It is expected that the time required for triage can be effectively reduced by using the developed classification model of survival prediction.

A Trainable Hearing Aid Algorithm Reflecting Individual Preferences for Degree of Noise-Suppression, Input Sound Level, and Listening Situation.

OBJECTIVES: In an effort to improve hearing aid users' satisfaction, recent studies on trainable hearing aids have attempted to implement one or two environmental factors into training. However, it would be more beneficial to train the device based on the owner's personal preferences in a more expanded environmental acoustic conditions. Our study aimed at developing a trainable hearing aid algorithm that can reflect the user's individual preferences in a more extensive environmental acoustic conditions (ambient sound level, listening situation, and degree of noise suppression) and evaluated the perceptual benefit of the proposed algorithm. METHODS: Ten normal hearing subjects participated in this study. Each subjects trained the algorithm to their personal preference and the trained data was used to record test sounds in three different settings to be utilized to evaluate the perceptual benefit of the proposed algorithm by performing the Comparison Mean Opinion Score test. RESULTS: Statistical analysis revealed that of the 10 subjects, four showed significant differences in amplification constant settings between the noise-only and speech-in-noise situation (P<0.05) and one subject also showed significant difference between the speech-only and speech-in-noise situation (P<0.05). Additionally, every subject preferred different ß settings for beamforming in all different input sound levels. CONCLUSION: The positive findings from this study suggested that the proposed algorithm has potential to improve hearing aid users' personal satisfaction under various ambient situations.

Comparison of Endoscopic Tympanoplasty to Microscopic Tympanoplasty.

OBJECTIVES: This study aimed to compare the outcome of endoscopic and microscopic tympanoplasty. METHODS: This was a retrospective comparative study of 73 patients (35 males and 38 females) who underwent type I tympanoplasty at Samsung Medical Center from April to December 2014. The subjects were classified into two groups; endoscopic tympanoplasty (ET, n=25), microscopic tympanoplasty (MT, n=48). Demographic data, perforation size of tympanic membrane at preoperative state, pure tone audiometric results preoperatively and 3 months postoperatively, operation time, sequential postoperative pain scale (NRS-11), and graft success rate were evaluated. RESULTS: The perforation size of the tympanic membrane in ET and MT group was 25.3%±11.7% and 20.1%±11.9%, respectively (P=0.074). Mean operation time of MT (88.9±28.5 minutes) was longer than that of the ET (68.2±22.1 minutes) with a statistical significance (P=0.002). External auditory canal (EAC) width was shorter in the ET group than in the MT group (P=0.011). However, EAC widening was not necessary in the ET group and was performed in 33.3% of patients in the MT group. Graft success rate in the ET and MT group were 100% and 95.8%, respectively; the values were not significantly different (P=0.304). Pre- and postoperative audiometric results including bone and air conduction thresholds and air-bone gap were not significantly different between the groups. In all groups, the postoperative air-bone gap was significantly improved compared to the preoperative air-bone gap. Immediate postoperative pain was similar between the groups. However, pain of 1 day after surgery was significantly less in the ET group. CONCLUSION: With endoscopic system, minimal invasive tympanoplasty can be possible with similar graft success rate and less pain.

Effects of the simultaneous application of nonlinear frequency compression and dichotic hearing on the speech recognition of severely hearing-impaired subjects: simulation test.

OBJECTIVES: The clinical effects of the simultaneous application of nonlinear frequency compression and dichotic hearing on people with hearing impairments have not been evaluated previously. In this study, the clinical effects of the simultaneous application of these two techniques on the recognition of consonant-vowel-consonant (CVC) words with fricatives were evaluated using normal-hearing subjects and a hearing loss simulator operated in the severe hearing loss setting. METHODS: A total of 21 normal-hearing volunteers whose native language was English were recruited for this study, and two different hearing loss simulators, which were configured for severe hearing loss in the high-frequency range, were utilized. The subjects heard 82 English CVC words, and the word recognition score and response time were measured. RESULTS: The experimental results demonstrated that the simultaneous application of these two techniques showed almost even performance compared to the sole application of nonlinear frequency compression in a severe hearing loss setting. CONCLUSION: Though it is generally accepted that dichotic hearing can decrease the spectral masking thresholds of an hearing-impaired person, simultaneous application of the nonlinear frequency compression and dichotic hearing techniques did not significantly improve the recognition of words with fricatives compared to the sole application of nonlinear frequency compression in a severe hearing loss setting.