Identification of Asthmatic Patient During Exercise Using Feature Extraction of Carbon Dioxide Waveform Asthmatic athletes encounter greater challenges while practicing sport in dealing with their disorder. Hence, this study explores the significance of features extracted from the different phases of carbon dioxide (CO 2 ) waveform morphology from an asthmatic patient during exercise. Herein, CO 2  data was collected using human respiration CO 2  measurement device from 9 asthmatic subjects in the stable mild state before and after exercise, aged between 20xe2x80x9325 years via convenience sampling method, chosen from the UTM Health Center. The subjects were asked to run on the medical treadmill (TMX42815% elevation, 7.5 mh) for 2 minutes. Thereafter, we automatically segmented each breath cycle into subcycles using threshold and computed Area as a feature from each part using numerical methods compared with slope or derivative of the capnogram. We found that the feature (Area) for the segmented part of the mix of upward expiratory phase and alveolar phase possess higher area under curve (AUC) of 0.94 via receiver operating curve analysis. Further, the feature was sent to the classifier for the classification of athlete asthmatic condition before and after exercise. We found that the decision tree possesses higher accuracy (88.89%), sensitivity (100%) and specificity (77.78%) with an AUC of 0.85 than other classifiers. Thus, the incorporation of these features into a newly developed device could possibly allow asthmatic athletes to manage their condition during exercise and sports.

Extraction of Voice Parameters from Continuous Running Speech for Pulmonary Disease Monitoring Pulmonary disease is one of the leading causes of death, and individuals with chronic diseases like asthma and chronic obstructive pulmonary disease (COPD) will have to manage their disease throughout their lifetime. Passive monitoring with mobile devices such as smartphones represents a costeffective solution that is closely coupled with the user to detect and continuously track adverse pulmonary trends. Identifying changes in the useru0027s voice patterns has seen recent research interest as a potentially useful biomarker that is relatively convenient and efficient to monitor. Prosodic features of the voice such as shimmer and jitter have been traditionally extracted from sustained vowel sounds in the majority of the previous work in this area. In this work, we describe a method to extract these parameters from continuous running speech, and show that they have better agreement with the corresponding parameters from sustained vowel sounds. This method was validated on real data from pulmonary patients collected under two different environments, and also compared to an existing speech processing package that is widely used in the literature to extract such features. The described method can be an important step in realizing passive monitoring of voice changes from the natural speech of pulmonary patients in their day to day lives.