Nabi Fizza Ghulam, Sundaraj Kenneth, Lam Chee Kiang, Palaniappan Rajkumar
School of Mechatronic Engineering, Universiti Malaysia Perlis, Malaysia.
Centre for Telecommunication Research & Innovation, Fakulti Kejuruteraan Elektronik & Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Malaysia.
J Asthma. 2020 Apr;57(4):353-365. doi: 10.1080/02770903.2019.1576193. Epub 2019 Feb 27.
: This study aimed to statistically analyze the behavior of time-frequency features in digital recordings of wheeze sounds obtained from patients with various levels of asthma severity (mild, moderate, and severe), and this analysis was based on the auscultation location and/or breath phase. : Segmented and validated wheeze sounds were collected from the trachea and lower lung base (LLB) of 55 asthmatic patients during tidal breathing maneuvers and grouped into nine different datasets. The quartile frequencies , , , and , mean frequency (MF) and average power (AP) were computed as features, and a univariate statistical analysis was then performed to analyze the behavior of the time-frequency features. : All features generally showed statistical significance in most of the datasets for all severity levels [ = 6.021-71.65, < 0.05, η = 0.01-0.52]. Of the seven investigated features, only AP showed statistical significance in all the datasets. , , and exhibited statistical significance in at least six datasets [ = 4.852-65.63, < 0.05, η = 0.01-0.52], and , and MF showed statistical significance with a large η in all trachea-related datasets [ = 13.54-55.32, < 0.05, η = 0.13-0.33]. : The results obtained for the time-frequency features revealed that (1) the asthma severity levels of patients can be identified through a set of selected features with tidal breathing, (2) tracheal wheeze sounds are more sensitive and specific predictors of severity levels and (3) inspiratory and expiratory wheeze sounds are almost equally informative.
本研究旨在对从不同哮喘严重程度（轻度、中度和重度）患者获得的哮鸣音数字记录中的时频特征行为进行统计分析，该分析基于听诊位置和/或呼吸阶段。从55名哮喘患者在潮式呼吸动作期间的气管和下肺基底（LLB）收集分段且经验证的哮鸣音，并将其分组为九个不同的数据集。计算四分位频率、、、和，平均频率（MF）和平均功率（AP）作为特征，然后进行单变量统计分析以分析时频特征的行为。所有特征在所有严重程度水平的大多数数据集中通常都显示出统计学意义[=6.021 - 71.65，<0.05，η=0.01 - 0.52]。在七个研究特征中，只有AP在所有数据集中显示出统计学意义。、、和在至少六个数据集中显示出统计学意义[=4.852 - 65.63，<0.05，η=0.01 - 0.52]，并且、和MF在所有与气管相关的数据集中显示出具有大η的统计学意义[=13.54 - 55.32，<0.05，η=0.13 - 0.33]。时频特征获得的结果表明：（1）可以通过一组潮式呼吸的选定特征识别患者的哮喘严重程度水平；（2）气管哮鸣音是严重程度水平更敏感和特异的预测指标；（3）吸气和呼气哮鸣音的信息量几乎相同。
