Respiration Disorders Classification With Informative Features for m-Health Applications

• Published in: IEEE journal of biomedical and health informatics Vol. 20; no. 3; pp. 733 - 747
• Main Authors: Fekr, Atena Roshan, Janidarmian, Majid, Radecka, Katarzyna, Zilic, Zeljko
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abdomen - physiology; Accelerometer sensor; Accelerometers; Accelerometry - instrumentation; Accuracy; Algorithms; Assessments; Biomedical measurement; Breathing; Classification; Decision Trees; Disorders; Feature extraction; Health; Humans; Lungs; m-health application; Microelectromechanical systems; Monitoring, Ambulatory - instrumentation; Monitoring, Ambulatory - methods; Respiration; Respiration disorder; Respiration Disorders - diagnosis; Sensitivity and Specificity; Sensors; Signal Processing, Computer-Assisted; Sleep apnea; Support Vector Machine; Support vector machines; Telemedicine - methods; Thorax - physiology; Accelerometer sensor; m-health application; classification; respiration disorder
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2015.2458965

Respiratory disorder is a highly prevalent condition associated with many adverse health problems. As the current means of diagnosis are obtrusive and ill-suited for real-time m-health applications, we explore a convenient and low-cost automatic approach that uses wearable microelectromechanical system sensor technology. The proposed system introduces the use of motion sensors to detect the changes in the anterior-posterior diameter of the chest wall during breathing function as well as extracting the informative respiratory features to be used for breathing disorders classification. Extensive evaluations are provided on six well-known classifiers with novel feature extraction techniques to distinguish among eight different pathological breathing patterns. The effects of the number of sensors, sensor placement, as well as feature selection on the classification performance are discussed. The experimental results conducted with ten subjects show the best accuracy rates of 97.50% by support vector machine and 97.37% with decision tree bagging (DTB) with all features and after feature selection, correspondingly. Furthermore, a binary classification is proposed for distinguishing between healthy people and patients with breath problems. The different assessments of classification parameters are provided by measuring the accuracy, sensitivity, specificity, F1-score and Mathew correlation coefficient. The accuracy rates above 98% suggest superior performance of DTB in binary recognition supported by the suggested new features.