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首页> 外文会议>IEEE Conference on Systems, Process and Control >Classification of asthma severity levels by wheeze sound analysis
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Classification of asthma severity levels by wheeze sound analysis

机译：通过喘息声分析对哮喘严重程度进行分类

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Asthma is among the most common condition, and it has been reported that it is currently poorly controlled. In this work, wheeze sound were analysed to classify different levels of asthma severity. Wheeze signals were obtained from patients with three asthma severity levels, namely mild, moderate and severe asthma. The wheeze sounds detected were then used for a feature extraction process using mel-frequency cepstral coefficients (MFCC). The extracted features were then evaluated by one-way ANOVA, and the MFCC features were then subjected to a classification process using the K-nearest neighbour (KNN) algorithm for the classification process. The performance of the KNN was found to exhibit an average accuracy of 97.5%. This study reveals that wheeze analysis is a competent approach for designing a computerized system for monitoring severity of asthma based on wheeze sounds.
机译：哮喘是最常见的条件之一，据报道，它目前的控制很差。在这项工作中，分析了喘息声，以分类不同水平的哮喘严重程度。从哮喘严重程度的患者获得喘息信号，即轻度，中度和严重的哮喘。然后使用熔融频率谱系数（MFCC）使用检测到的喘息声的声音。然后通过单向ANOVA评估提取的特征，然后使用用于分类过程的K-COMBUST邻（KNN）算法进行MFCC特征。发现knn的性能表现出97.5％的平均精度。该研究表明，喘息分析是设计计算机化系统，用于监测基于喘息声的哮喘严重程度。

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