• International journal of thyroidology
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• v.11 no.2
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• pp.130-136
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• 2018

Background and Objectives: Intraoperative neural monitoring (IONM) of recurrent laryngeal nerve (RLN) in thyroid surgery has been employed worldwide to identify and preserve the nerve as an adjunct to visual identification. The aims of this study was to evaluate the efficacy of IONM and difficulties in the learning curve. Materials and Methods: We studied 63 patients who underwent thyroidectomy with IONM during last 2 years. The standard IONM procedure was performed using NIM 3.0 or C2 Nerve Monitoring System. Patients were divided into two chronological groups based on the success rate of IONM (33 cases in the early period and 30 cases in the late period), and the outcomes were compared between the two groups. Results: Of 63 patients, 32 underwent total thyroidectomy and 31 thyroid lobectomy. Failure of IONM occurred in 9 cases: 8 cases in the early period and 1 case in the late period. Loss of signal occurred in 8 nerves of 82 nerves at risk. The positive predictive value increased from 16.7% in the early period to 50% in the late period. The mean amplitude of the late period was higher than that of the early period (p<0.001). Conclusion: IONM in thyroid surgery is effective to preserve the RLN and to predict postoperative nerve function. However, failure of IONM and high false positive rate can occur in the learning curve, and the learning curve was about 30 cases based on the results of this study.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.12 no.2
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• pp.126-132
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• 2001

Background and Objectives : Signal traduction through phospholipase C(PLC) participate in the regulation of cell growth and differentiation. Growth factors bind to their receptors and thereby induce tyrosine phophorylation of the phospholipase C-${\gamma}$1(PLC-${\gamma}$1). PLC-${\gamma}$1 is a substrate for several receptor tyrosine kinases and its catalytic activity is increased by tyrosine phosphorylation. Tyrosine kinase phosphorylation of PLC-${\gamma}$1 stimulates PLC activation and cell proliferation. However the signal transduction pathway and the significance of PLC in injured recurrent laryngeal nerve regeneration is unknown. Therefore after we obtained fuctionally recovered rats using PEMF in this study, we attempt to provide some evidence that PLC plays a role in nerve regeneration itself and regeneration related to PEMF through the analysis of the difference between fucntional recovery group and non-recovery group in the recurrent laryngeal nerve. Materials and Method : Using 32 healthy male Sprague-Dawley rats, transections and primary anastomosis were performed on their left recurrent laryngeal nerves. Rats were then randomly assigned to 2 groups. The experimental group(n=16) received PEMS by placing them in custom cages equipped with Helm-holz coils(3hr/day, 5days/wk, for 12wk). The control group(n=16) were handled the same way as the experimental group, except that they did not receive PEMS. Laryngo-videoendoscopy was performed before and after surgery and followed up weekly. Laryngeal EMG was obtained in both PCA and TA muscles. Immunohistochemisty staining and Western blotting analysis using monoclonal antibody was performed to detect PLC-${\gamma}$1 in recurrent laryngeal nerve and nodose ganglion. Results : 10 rats(71%) in experimental group and 4 rats(38%) in the control group showed recovery of vocal fold motion. Functionally-recoverd rats show PLC-${\gamma}$1 positive cells in neuron and ganglion cells after 12 weeks from nerve injury. Conclusion : This study shows that PLC1-${\gamma}$ involved in singnal trasduction pathway in functinal recovery of injured recurrent laryngeal nerve and PEMF enhance the functional recovery by effect on this molecule.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.14 no.2
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• pp.110-116
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• 2003

Background and Objectives : Laryngeal cancer discrimination using voice signals is a non-invasive method that can carry out the examination rapidly and simply without giving discomfort to the patients. n appropriate analysis parameters and classifiers are developed, this method can be used effectively in various applications including telemedicine. This study examines voice analysis parameters used for laryngeal disease discrimination to help discriminate laryngeal diseases by voice signal analysis. The study also estimates the laryngeal cancer discrimination activity of the Gaussian mixture model (GMM) classifier based on the statistical modelling of voice analysis parameters. Materials and Methods : The Multi-dimensional voice program (MDVP) parameters, which have been widely used for the analysis of laryngeal cancer voice, sometimes fail to analyze the voice of a laryngeal cancer patient whose cycle is seriously damaged. Accordingly, it is necessary to develop a new method that enables an analysis of high reliability for the voice signals that cannot be analyzed by the MDVP. To conduct the experiments of laryngeal cancer discrimination, the authors used three types of voices collected at the Department of Otorhinorlaryngology, Pusan National University Hospital. 50 normal males voice data, 50 voices of males with benign laryngeal diseases and 105 voices of males laryngeal cancer. In addition, the experiment also included 11 voices data of males with laryngeal cancer that cannot be analyzed by the MDVP, Only monosyllabic vowel /a/ was used as voice data. Since there were only 11 voices of laryngeal cancer patients that cannot be analyzed by the MDVP, those voices were used only for discrimination. This study examined the linear predictive cepstral coefficients (LPCC) and the met-frequency cepstral coefficients (MFCC) that are the two major cepstrum analysis methods in the area of acoustic recognition. Results : The results showed that this met frequency scaling process was effective in acoustic recognition but not useful for laryngeal cancer discrimination. Accordingly, the linear frequency cepstral coefficients (LFCC) that excluded the met frequency scaling from the MFCC was introduced. The LFCC showed more excellent discrimination activity rather than the MFCC in predictability of laryngeal cancer. Conclusion : In conclusion, the parameters applied in this study could discriminate accurately even the terminal laryngeal cancer whose periodicity is disturbed. Also it is thought that future studies on various classification algorithms and parameters representing pathophysiology of vocal cords will make it possible to discriminate benign laryngeal diseases as well, in addition to laryngeal cancer.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.351-356
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• 2018

Changes in vocal cords due to aging can change the frequency of speech, and the speech signals of the elderly can be automatically distinguished from normal speech signals through various analyzes. The purpose of this study is to provide a tool that can be easily accessed by the elderly and disabled people who can be excluded from the rapidly changing technological society and to improve the voice recognition performance. In the study, the gender of the subjects was reported as sex analysis, and the number of female and male voice samples was used equally. In addition, the gender analysis was applied to set the voices of the elderly without using voices of all ages. Finally, we applied a review methodology of standards and reference models to reduce gender difference. 10 Korean women and 10 men aged 70 to 80 years old are used in this study. Comparing the F0 value extracted directly with the waveform and the F0 extracted with TF32 and the Wavesufer speech analysis program, Wavesufer analyzed the F0 of the elderly voice better than TF32. However, there is a need for a voice analysis program for elderly people. In conclusions, analyzing the voice of the elderly will improve speech recognition and synthesis capabilities of existing smart medical systems.

• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.697-705
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• 2009

An unsupervised (blind) method is proposed aiming at extracting rhythmic sources from commercial polyphonic music whose number of channels is limited to one. Commercial music signals are not usually provided with more than two channels while they often contain multiple instruments including singing voice. Therefore, instead of using conventional modeling of mixing environments or statistical characteristics, we should introduce other source-specific characteristics for separating or extracting sources in the under determined environments. In this paper, we concentrate on extracting rhythmic sources from the mixture with the other harmonic sources. An extension of nonnegative matrix factorization (NMF), which is called nonnegative matrix partial co-factorization (NMPCF), is used to analyze multiple relationships between spectral and temporal properties in the given input matrices. Moreover, temporal repeatability of the rhythmic sound sources is implicated as a common rhythmic property among segments of an input mixture signal. The proposed method shows acceptable, but not superior separation quality to referred prior knowledge-based drum source separation systems, but it has better applicability due to its blind manner in separation, for example, when there is no prior information or the target rhythmic source is irregular.