首页> 外文期刊>Journal of medical systems >Pairwise ANFIS approach to determining the disorder degree of obstructive sleep apnea syndrome.
【24h】

Pairwise ANFIS approach to determining the disorder degree of obstructive sleep apnea syndrome.

机译:成对ANFIS方法确定阻塞性睡眠呼吸暂停综合症的障碍程度。

获取原文
获取原文并翻译 | 示例
           

摘要

Obstructive sleep apnea syndrome (OSAS) is an important disease that affects both the right and the left cardiac ventricle. This paper presents a novel classification method called pairwise ANFIS based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and one against all method for detecting the obstructive sleep apnea syndrome. In order to extract the features related with OSAS, we have used the clinical features obtained from Polysomnography device as a diagnostic tool for obstructive sleep apnea (OSA) in patients clinically suspected of suffering from this disease. The clinical features obtained from Polysomnography Reports are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO2 minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO2 intervals bigger than 89%. Since ANFIS has output with one class, we have extended the output of ANFIS to multi class by means of one against all method to diagnose the OSAS that has four classes consisting of normal (25 subjects), mild OSAS (AHI=5-15 and 14 subjects), middle OSAS (AHI<15-30 and 18 subjects), and heavy OSAS (AHI>30 and 26 subjects). The classification accuracy, sensitivity and specifity analysis, mean square error, and confusion matrix have been used to test the performance of proposed method. The obtained classification accuracies are 82.92%, 82.92%, 85.36%, and 87.80% for each class including normal, mild OSAS, middle OSAS, and heavy OSAS using ANFIS with one against all method with 50-50% train-test split, respectively. Combining ANFIS and one against all method that is firstly proposed by us was firstly applied for diagnosing the OSAS. The proposed method has produced very promising results in the detecting the obstructive sleep apnea syndrome.
机译:阻塞性睡眠呼吸暂停综合症(OSAS)是一种重要的疾病,会同时影响左右心室。本文提出了一种基于自适应神经模糊推理系统(ANFIS)的成对ANFIS分类方法,以及一种针对所有方法的阻塞性睡眠呼吸暂停综合征检测方法。为了提取与OSAS相关的特征,我们已使用从多导睡眠监测仪获得的临床特征作为临床怀疑患有该病的患者的阻塞性睡眠呼吸暂停(OSA)诊断工具。从多导睡眠监测仪报告中获得的临床特征是:觉醒指数(ARI),呼吸暂停和呼吸不足指数(AHI),REM阶段的SaO2最小值以及SaO2间隔大于89%的阶段的睡眠时间百分比(PST)。由于ANFIS的输出为一类,因此我们通过一种针对所有方法的诊断方法将ANFIS的输出扩展为多类,以诊断OSAS分为四类,包括正常(25名受试者),轻度OSAS(AHI = 5-15和14位受试者),中级OSAS(AHI <15-30位和18位受试者)和重度OSAS(AHI> 30位和26位受试者)。使用分类准确性,灵敏度和特异性分析,均方误差和混淆矩阵来测试所提出方法的性能。使用ANFIS的正常,轻度OSAS,中度OSAS和重度OSAS的每种分类的获得的分类准确度分别为82.92%,82.92%,85.36%和87.80%,其中一种方法针对所有方法,采用50-50%的火车测试分割。我们首先提出了将ANFIS和一种针对所有人的方法相结合的方法,用于诊断OSAS。所提出的方法在检测阻塞性睡眠呼吸暂停综合症方面取得了非常有希望的结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号