Vinayak Swarnkar1, Udantha Abeyratne1, Jamie Tan2, Ti Wan Ng3, Joanna M Brisbane3,4, Jennifer Choveaux3, Paul Porter2,4,5. 1. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia. 2. Department of Paediatrics, Joondalup Health Campus, Joondalup, WA, Australia. 3. Joondalup Health Campus, Joondalup, WA, Australia. 4. School of Nursing, Midwifery and Paramedicine, Curtin University, Bentley, WA, Australia. 5. Department of Emergency Medicine, Perth Children's Hospital, Nedlands, WA, Australia.
Abstract
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.
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.
Authors: Peter P Moschovis; Esther M Sampayo; Anna Cook; Gheorghe Doros; Blair A Parry; Jesiel Lombay; T Bernard Kinane; Kay Taylor; Tony Keating; Udantha Abeyratne; Paul Porter; John Carl Journal: Contemp Clin Trials Date: 2021-01-12 Impact factor: 2.226