Solà-Soler Jordi, Jané Raimon, Fiz José Antonio, Morera José
Centre de Recerca en Enginyeria Biomedica, Universitat Politècnica de Catalunya, Pau Gargallo, 5. 08028 Barcelona, Spain.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:6094-7. doi: 10.1109/IEMBS.2007.4353739.
A new method for indirect identification of Sleep Apnea patients through snoring characteristics is proposed. The method uses a logistic regression model which is fed with several time and frequency parameters from snores and their variability. The information is contained in all the snores automatically detected in nocturnal sound recordings. In the validation of the model, subjects are classified with a sensitivity higher than 93% and a specificity between 73% and 88% when all detected snores are used. The model can also be adjusted to obtain 100% specificity with a corresponding sensitivity between 70% and 87%. This results are better than previous reported methods based on snoring analysis, but with a single channel, and are comparable to the classification scores of several portable apnea monitors when evaluated on a similar number of patients. This technique is a promising tool for the screening of snorers, allowing snorers with a low Apnea-Hypopnea Index (AHI<10) to avoid a full-night polysomnographic study at the hospital.
提出了一种通过打鼾特征间接识别睡眠呼吸暂停患者的新方法。该方法使用逻辑回归模型，该模型由来自鼾声及其变异性的几个时间和频率参数提供数据。信息包含在夜间声音记录中自动检测到的所有鼾声中。在模型验证中，当使用所有检测到的鼾声时，受试者分类的灵敏度高于93%，特异性在73%至88%之间。该模型还可以进行调整，以获得100%的特异性，相应的灵敏度在70%至87%之间。这些结果优于先前基于打鼾分析报道的单通道方法，并且在对相似数量的患者进行评估时，与几种便携式呼吸暂停监测仪的分类评分相当。这项技术是筛查打鼾者的一个有前途的工具，使呼吸暂停低通气指数较低（AHI<10）的打鼾者能够避免在医院进行整夜的多导睡眠图研究。
