Nabi Fizza Ghulam, Sundaraj Kenneth, Lam Chee Kiang
Institute of Quality and Technology Management, University of the Punjab, Lahore, Pakistan.
Centre for Telecommunication Research Innovation CeTRI, Fakulti Kejuruteraan Elektronik Dan Kejuruteraan Komputer FKEKK, Universiti Teknikal Malaysia Melaka UTeM.
J Pak Med Assoc. 2021 Jan;71(1(A)):41-46. doi: 10.47391/JPMA.156.
Breath sound has information about underlying pathology and condition of subjects. The purpose of this study was to examine asthmatic acuteness levels (Mild, Moderate, Severe) using frequency features extracted from wheeze sounds. Further, analysis was extended to observe behaviour of wheeze sounds in different datasets.
Segmented and validated wheeze sounds was collected from 55 asthmatic patients from the trachea and lower lung base (LLB) during tidal breathing maneuvers. Segmented wheeze sounds have been grouped in to nine datasets based on auscultation location, breath phases and a combination of phase and location. Frequency based features F25, F50, F75, F90, F99 and mean frequency (MF) were calculated from normalized power spectrum. Subsequently, multivariate analysis was performed.
Generally frequency features observe statistical significance (p < 0.05) for the majority of datasets to differentiate severity level Ʌ = 0.432-0.939, F(12, 196-1534) = 2.731-11.196, p < 0.05, ɳ2 = 0.061-0.568. It was observed that selected features performed better (higher effect size) for trachea related samples Ʌ = 0.432-0.620, F(12, 196-498) = 6.575-11.196, p < 0.05, ɳ2 = 0.386-0.568.
The results demonstrated dthat severity levels of asthmatic patients with tidal breathing can be identified through computerized wheeze sound analysis. In general, auscultation location and breath phases produce wheeze sounds with different characteristics.
呼吸音包含有关受试者潜在病理状况和身体状况的信息。本研究的目的是利用从哮鸣声中提取的频率特征来检查哮喘急性程度水平（轻度、中度、重度）。此外，分析范围扩大到观察不同数据集中哮鸣声的表现。
在潮式呼吸动作期间，从55名哮喘患者的气管和下肺基底（LLB）收集分段且经验证的哮鸣声。根据听诊位置、呼吸阶段以及阶段与位置的组合，将分段后的哮鸣声分为九个数据集。从归一化功率谱计算基于频率的特征F25、F50、F75、F90、F99和平均频率（MF）。随后进行多变量分析。
一般来说，频率特征在大多数数据集中具有统计学意义（p < 0.05），以区分严重程度水平Ʌ = 0.432 - 0.939，F(12, 196 - 1534) = 2.731 - 11.196，p < 0.05，ɳ2 = 0.061 - 0.568。观察到所选特征在与气管相关的样本中表现更好（效应量更高）Ʌ = 0.432 - 0.620，F(12, 196 - 498) = 6.575 - 11.196，p < 0.05，ɳ2 = 0.386 - 0.568。
结果表明，通过计算机化的哮鸣声分析可以识别潮式呼吸哮喘患者的严重程度水平。一般来说，听诊位置和呼吸阶段会产生具有不同特征的哮鸣声。
