Azam Muhammad Awais, Shahzadi Aeman, Khalid Asra, Anwar Syed M, Naeem Usman
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:445-448. doi: 10.1109/EMBC.2018.8512452.
Human breath analysis plays important role for diagnosis and management of pulmonary diseases to guarantee normal health. The critical task is to distinguish normal and abnormal lung sounds. This research work presents a scheme for breath analysis used to detect irregular patterns occurred in respiratory cycles due to respiratory diseases. After de-noising breath segments using wavelet de-noising method, intrinsic mode functions are extracted with complete ensemble empirical mode decomposition (CEEMD). Instantaneous frequency (IF) and instantaneous envelope are extracted to get robust features for classification. The study contains breath samples captured using smartphone under natural setting. The data set contains 255 breath cycles. For cycle classification, Bag-of-word was applied to group segments based features. The support vector machine (SVM) was applied on randomly partitioned data samples. Experiments resulted with performance accuracy of (75.21%±2) for asthmatic inspiratory cycles and (75.5%±3%) for complete Respiratory Sounds (RS) cycle with diagnostic odds ratio (DOR) of 20.61% and 13.S7% respectively.
人体呼吸分析对于肺部疾病的诊断和管理以确保正常健康起着重要作用。关键任务是区分正常和异常的肺部声音。这项研究工作提出了一种用于呼吸分析的方案，用于检测由于呼吸系统疾病在呼吸周期中出现的不规则模式。使用小波去噪方法对呼吸段进行去噪后，通过完全集合经验模态分解（CEEMD）提取本征模态函数。提取瞬时频率（IF）和瞬时包络以获得用于分类的稳健特征。该研究包含在自然环境下使用智能手机采集的呼吸样本。数据集包含255个呼吸周期。对于周期分类，应用词袋法基于特征对段进行分组。支持向量机（SVM）应用于随机划分的数据样本。实验结果显示，哮喘吸气周期的性能准确率为（75.21%±2），完整呼吸音（RS）周期的性能准确率为（75.5%±3%），诊断比值比（DOR）分别为20.61%和13.57%。
