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J Korean Soc Laryngol Phoniatr Logop > Volume 14(1); 2003 > Article
Journal of The Korean Society of Laryngology, Phoniatrics and Logopedics 2003;14(1): 16-25.
후두암 감별진단에 있어 성문전도(Electroglottograph) 파라미터의 유용성
송인무1, 고의경1, 전경명1, 권순복2, 김기련2, 전계록3, 김광년4, 정동근4, 조철우5
1부산대학교 의과대학 이비인후과학교실2부산대학교 의과대학 의공학협동과정3부산대학교 의과대학 의공학교실4동아대학교 의과대학 의공학교실5창원대학교 공과대학 제어계측학과
The Effectiveness of Electroglottographic Parameters in Differential Diagnosis of Laryngeal Cancer
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ABSTRACT
Background and Objectives
s : Electroglottography(EGG) is a non-invasive method of monitoring the vocal cord vibration by measuring the variation of physiological impedance across the vocal folds through the neck skin. It reveals especially the vocal fold contact area and is widely used for basic laryngeal researches, voice analysis and synthesis. The purpose of this study is to investigate the effectiveness of EGG parameters in differential diagnosis of laryngeal cancer.
Materials and Method
s : The author investigated 10 laryngeal cancer and 25 benign laryngeal disease patients who visited at the Department of Otolaryngology, Pusan National University Hospital. The EGG equipment was devised in the author's Department. Among various parameters of EGG, closed quotient(CQ), speed quotient(SQ), speed index(SI), Jitter, Shimmer, Fo were determined by an analysis program made with MATLAB 6.5$^{circledR}$(Mathwork, Inc.). In order to differentiate various laryngeal diseases from pathologic voice signals, the author has used the electroglottographic parameters using the neural network of multilayer perceptron structure.
Results
: SQ, SI, Jitter and Shimmer values except those of CQ and Fo showed remarkable differences between benign and malignant laryngeal disease groups. From the artificial neural network, the percentage of differentiating the laryngeal cancer was over 80% in SQ, SI, Jitter, Shimmer except for CQ and Fo. These results indicated that it is possible to discriminate the benign and malignant laryngeal diseases by EGG parameters using the artificial neural network.
Conclusion
: If parameters of EGG which can reveal for the pathology of laryngeal diseases are additionally developed and the current classification algorithm is improved, the discrimination of laryngeal cancer will become much more accurate.
KEY WORDS: Laryngeal cancer;Electroglottograph;Neural network;Differential diagnosis;
중심 단어: 후두암;성문전도;
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