Methods: 383 snores from 40 participants who complained of snoring were digitally recorded during natural and induced sleep using a level III polysomnography monitor with a built-in microphone. During drug-induced sleep endoscopy (DISE), the real-time site of upper airway obstruction was assessed, and the sound frequency of snoring was recorded synchronously.
Results: The mean peak of snoring frequency for unilevel palatal, oropharynx and epiglottis obstruction were 522.5, 482.4 and 300.0 Hz, respectively. Most participants showed multilevel obstruction at the palate and oropharynx, in which the mean for bi-peak snoring frequency were 402.90 Hz and 1086.96 Hz, respectively. Severity of OSA was significantly associated with multilevel obstruction.
Conclusions: There was a significant association between the snoring sound frequency and site of unilevel obstruction. Palatal or oropharyngeal obstruction produced sound at mid-frequency range, while the epiglottis produced a low frequency range. Multilevel obstruction documented a bi-peak snoring frequency.
METHODS: The study population consisted of 53 participants, 23 patients with BVFI after endolaryngeal laser posterior cordectomy and 30 healthy volunteers. All of them had body plethysmography (airway resistance, Raw), spirometry (ratio of forced expiratory flow at 50% to forced inspiratory flow at 50%, FEF50/FIF50 and peak inspiratory flow, PIF), 6 min-walking-test (6MWT) and Medical Research Council (MRC) dyspnea scale measurements. The tests were repeated and reliability was evaluated using intraclass correlation (ICC) and Spearman correlation.
RESULTS: The reliability of Raw was high with ICC of 0.92, comparable to the spirometry measurements: FEF50/FIF50(ICC = 0.72) and PIF (ICC = 0.97). The mean of Raw was significantly higher in patient group. A strong significant correlation between Raw and MRC dyspnea scale (r = 0.79; p<0.05) and a moderate negative correlation between Raw and 6MWT (r = 0.4; p<0.05) was demonstrated.
CONCLUSION: Body plethysmography (Raw) is a reliable tool in objective measurement of upper airway resistance that reflects the patient's perception of breathlessness. A larger number of participants are necessary to confirm this finding.
METHODS: The study included 382 participants (252 normal voices and 130 dysphonic voices) in the proposed database MVPD. Complete data were obtained for both groups, including voice samples, laryngostroboscopy videos, and acoustic analysis. The diagnoses of patients with dysphonia were obtained. Each voice sample was anonymized using a code that was specific to each individual and stored in the MVPD. These voice samples were used to train and test the proposed OSELM algorithm. The performance of OSELM was evaluated and compared with other classifiers in terms of the accuracy, sensitivity, and specificity of detecting and differentiating dysphonic voices.
RESULTS: The accuracy, sensitivity, and specificity of OSELM in detecting normal and dysphonic voices were 90%, 98%, and 73%, respectively. The classifier differentiated between structural and non-structural vocal fold pathology with accuracy, sensitivity, and specificity of 84%, 89%, and 88%, respectively, while it differentiated between malignant and benign lesions with an accuracy, sensitivity, and specificity of 92%, 100%, and 58%, respectively. Compared to other classifiers, OSELM showed superior accuracy and sensitivity in detecting dysphonic voices, differentiating structural versus non-structural vocal fold pathology, and between malignant and benign voice pathology.
CONCLUSION: The OSELM algorithm exhibited the highest accuracy and sensitivity compared to other classifiers in detecting voice pathology, classifying between malignant and benign lesions, and differentiating between structural and non-structural vocal pathology. Hence, it is a promising artificial intelligence that supports an online application to be used as a screening tool to encourage people to seek medical consultation early for a definitive diagnosis of voice pathology.