Stratifying asthma severity in children using cough sound analytic technology.
Vinayak SwarnkarUdantha AbeyratneJamie TanTi Wan NgJoanna M BrisbaneJennifer ChoveauxPaul PorterPublished in: The Journal of asthma : official journal of the Association for the Care of Asthma (2019)
Introduction: Asthma is a common childhood respiratory disorder characterized by wheeze, cough and respiratory distress responsive to bronchodilator therapy. Asthma severity can be determined by subjective, manual scoring systems such as the Pulmonary Score (PS). These systems require significant medical training and expertise to rate clinical findings such as wheeze characteristics, and work of breathing. In this study, we report the development of an objective method of assessing acute asthma severity based on the automated analysis of cough sounds.Methods: We collected a cough sound dataset from 224 children; 103 without acute asthma and 121 with acute asthma. Using this database coupled with clinical diagnoses and PS determined by a clinical panel, we developed a machine classifier algorithm to characterize the severity of airway constriction. The performance of our algorithm was then evaluated against the PS from a separate set of patients, independent of the training set.Results: The cough-only model discriminated no/mild disease (PS 0-1) from severe disease (PS 5,6) but required a modified respiratory rate calculation to separate very severe disease (PS > 6). Asymptomatic children (PS 0) were separated from moderate asthma (PS 2-4) by the cough-only model without the need for clinical inputs.Conclusions: The PS provides information in managing childhood asthma but is not readily usable by non-medical personnel. Our method offers an objective measurement of asthma severity which does not rely on clinician-dependent inputs. It holds potential for use in clinical settings including improving the performance of existing asthma-rating scales and in community-management programs.AbbreviationsAMaccessory muscleBIbreathing indexCIconfidence intervalFEV1forced expiratory volume in one secondLRlogistic regressionPEFRpeak expiratory flow ratePSpulmonary scoreRRrespiratory rateSDstandard deviationSEstandard errorWAWestern Australia.
Keyphrases
- chronic obstructive pulmonary disease
- lung function
- allergic rhinitis
- machine learning
- healthcare
- young adults
- deep learning
- drug induced
- depressive symptoms
- emergency department
- end stage renal disease
- stem cells
- intensive care unit
- public health
- chronic kidney disease
- early onset
- physical activity
- respiratory failure
- hepatitis b virus
- neuropathic pain
- acute respiratory distress syndrome
- spinal cord injury
- cell therapy
- spinal cord
- peritoneal dialysis
- cancer therapy
- patient reported outcomes