A remote and personalised novel approach for monitoring asthma severity levels from EEG signals utilizing classification algorithms

Asthma is a complex respiratory disorder in which structural changes in the conducting airway cause variable airflow limitation. The excessive narrowing of the airway lumen underlies the morbidity and the mortality that are attributable to the disease, which reduces quality of life in people of all ages. Monitoring of the disease progression to gather data continuously as an objective marker of morbidity and to support therapy is essential. Currently, patients with asthma are examined only a few times a year, which prevents continuous monitoring of the disease progression and any advance in precision medicine. Remote non-invasive monitoring tools will enable the collection of data with minimal effort from patients. To date, however, assessment and monitoring of asthma based on electroencephalogram (EEG) signals have not been studied. The objective of this research was to develop a general approach for identifying asthma severity levels based on EEG signals for personalised remote and non-invasive monitoring of patients with asthma. Simultaneous measurements of EEG and respiration mo-tion signals were acquired from adults with suspected asthma, during the entire methacholine challenge test. The EEG segments were categorized into three classes, each representing a level of asthma severity based on par-ticipant's spirometry score. Three artificial intelligence (AI) methodologies were designed and examined: the first aimed to identify a subject's asthma severity levels based on their known data, the second based on mixed data comprising data from all subjects including the subject's personal data, and the third methodology based on the datasets of all other subjects, reflecting a situation of a new patient. To overcome multi-subject variations in the third methodology, the probabilities of being at each one of the possible asthma severity levels in previous breathing cycles, as inputs for predictions of asthma severity levels in the current breathing cycle, was used. The classification was done based on ordinal and non-ordinal classification algorithms. In the first and second methodologies, an ensemble approach was also applied to enhance predictive performance. Good performance measures were obtained to identify asthma severity level using both the first and second methodologies, espe-cially by the ensemble model and XGBoost classifier. In the third methodology, the combination of random forest and ordinal random forest in different stages of the methodology, yielded the best improvement in performance measures. The results of all three AI methodologies demonstrate that they may be employed for personalised home asthma monitoring and management that do not depend on patients exerting an effort.