A new method to detect snoring episodes in sleep sound recordings is proposed. Sleep sound segments (i.e., 'sound episodes' or simply 'episodes') are classified as snores and nonsnores according to their subband energy distributions. The similarity of inter- and intra-individual spectral energy distributions motivated the representation of the feature vectors in a lower dimensional space. Episodes have been efficiently represented in two dimensions using principal component analysis, and classified as snores or nonsnores. The sound recordings were obtained from individuals who are suspected of OSAS pathology while they were connected to the polysomnography in Gülhane Military Medical Academy Sleep Studies Laboratory (GMMA-SSL), Ankara, Turkey. The data from 30 subjects (18 simple snorers and 12 OSA patients) with different apnoea/hypopnea indices were classified using the proposed algorithm. The system was tested by using the manual annotations of an ENT specialist as a reference. The accuracy for simple snorers was found to be 97.3% when the system was trained using only simple snorers' data. It drops to 90.2% when the training data contain both simple snorers' and OSA patients' data. (Both of these results were obtained by using training and testing sets of different individuals.) In the case of snore episode detection with OSA patients the accuracy is 86.8%. All these results can be considered as acceptable values to use the system for clinical purposes including the diagnosis and treatment of OSAS. The method proposed here has been used to develop a tool for the ENT clinic of GMMA-SSL that provides information for objective evaluation of sleep sounds.

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Introduction: Tuberculosis of the parotid gland is a rare clinical entity which causes some difficulties in diagnosis because of the similarities in presentation to that of a neoplasm. Diagnosis mainly relies in the treating physician having a high index of suspicion. The diagnosis is generally overlooked by otolaryngologists and most cases are undergoing unnecessary surgery.

To evaluate the effect on snoring of structural nasal valve dilatation with butterfly spreader grafts in patients with nasal valve insufficiency. Design: Retrospective medical chart review and telephone follow-up; mean ± SD follow-up time, 20.7±11.34 months (range, 3-48 months). Settings: Tertiary care referral center. Subjects: A total of 37 snoring patients with nasal valve insufficiency who underwent nasal valve dilatation with a butterfly spreader graft. Interventions: The conchal cartilage butterfly graft technique was performed during rhinoplasty through either an external or endonasal approach. Main Outcome Measure: To establish through a retrospective review that butterfly graft conchal cartilage nasal reconstruction is effective in reducing snoring. Results: After surgery, 30 patients (81%) had significant improvement in breathing, 5 (14%) had slight improvement, and 2 (5%) had no benefit in breathing. Snoring stopped completely in 11 (30%) of the patients after surgery. The improvement in snoring was significant in 13 patients (35%) and slight in 3 (8%). Twenty-six patients (70%) reported tiredness and grogginess on awakening before the surgery. Surgery significantly improved patients' tiredness and grogginess on awakening in 15 cases (58%), slightly improved them in 5 (19%), and did not change the patients' tiredness and grogginess in 6 cases (23%). Conclusion: The conchal cartilage butterfly graft yields successful results not only in breathing but also in snoring symptoms in patients with nasal valve insufficiency.

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