## 4 Discussion  
A total of 1,482 asthmatic and normal-voluntary coughs were used in this study to train a GMM-UGM classification model, exploiting audio features such as MFCCs and CQCCs. The resulting classification model was verified using 852 cough sounds from 66 children. As presented above, it is evident that analyzing the cough sounds provides a feasible strategy to help distinguish between asthmatic and healthy patients with reasonably high confidence. Cough is a major symptom in asthma [7, 8, 11], a growing global chronic health problem in children. Asthma is characterized by a reversible variable airway obstruction, airway inflammation, and an increased airway responsiveness to stimuli [7, 12, 36]. While classical asthma is described by the triad of dyspnea, cough, and wheeze, cough alone might be the only manifestation of cough-variant asthma [6, 12, 13], a leading cause of chronic cough in children [6, 7, 12, 37, 38], which can remain undiagnosed. Cough has also been found to signal the onset of asthmatic exacerbation [14]. At the same time, there is a growing concern about overdiagnosis of asthma based on cough alone, in recent years [16]. The clinical diagnosis, assessment of severity, monitoring of asthma progression, and  
Figure 4: Receiver Operator Characteristic (ROC) curves of the classification model built with (**A**) MFCCs, (**B**) CQCCs, and (**C**) Fused features (area under ROC, shaded). response to therapy is, therefore, a significant clinical challenge. To add to the challenge, healthy children can also have normal epochs of cough [14]. Despite its prevalence and recognition as a cardinal symptom/sign in most respiratory diseases, the clinical value of cough for diagnosis has, up until now, been fairly limited. This is due to the poor reliability and reproducibility of cough reporting and the lack of validated objective measurement and assessment of cough characteristics in terms of severity and type [1; 8; 15]. If meaningful data could be extracted from the cough sound signals, then cough as a symptom/sign could and should be used as an objective tool for assessment of cough. Cough presents in a wide range of diseases, including asthma, particularly since asthma results in a variable airway narrowing and air flow obstruction [7; 12; 36]. Therefore, the expiratory expulsion of airflow through the airway and laryngeal structures becomes turbulent, leading to changes in the cough sound. Further, in cases where diagnosis is uncertain, chest radiography is regularly performed as a part of the clinical evaluation [1]; one-fifth of our subjects in the asthmatic cohort underwent chest radiography. From a medical perspective, if assistive diagnosis can be provided by other means, unnecessary radiological exposure can be reduced in children. While spirometry with or without tests of bronchodilator responsiveness is useful in assessing asthma, these tests are not feasible on young children and infants. An automated cough-audio-based classification system, such as the one proposed in this paper, offers the advantage of a contact-free, readily-accessible solution that uses machine-assisted support for diagnosis by physicians and home-screening by parents. To our knowledge, this report is the first to use such a large sample size for automatic cough sound analysis between asthmatic and normal-voluntary coughs. Additionally, our study is novel as the cough sounds were recorded simply using a smartphone, while interacting with the patient in an ambient ecological setting right in the clinic/ward (i.e., not a noise-free environment, and in situ). We found that audio features (MFCCs and CQCCs) can be used to train a classification model to distinguish asthmatic versus normal-voluntary coughs, in a pediatric population--we were able to achieve a competitive physician agreement rate, with sensitivity and specificity greater than 80%. It might be worth noting that in this study, children recruited in hospital settings could present a rather more severe asthma than in primary healthcare settings.