Classification of COPD and normal lung airways using feature extraction of electromyographic signals
Open Access
- 1 October 2019
- journal article
- research article
- Published by Elsevier BV in Journal of King Saud University - Computer and Information Sciences
- Vol. 31 (4), 506-513
- https://doi.org/10.1016/j.jksuci.2017.05.006
Abstract
No abstract availableKeywords
This publication has 15 references indexed in Scilit:
- A Novel Algorithm for EMG Signal Processing and Muscle Timing MeasurementIEEE Transactions on Instrumentation and Measurement, 2015
- Machine learning algorithms and forced oscillation measurements to categorise the airway obstruction severity in chronic obstructive pulmonary diseaseComputer Methods and Programs in Biomedicine, 2015
- Chest wall volumes during inspiratory loaded breathing in COPD patientsRespiratory Physiology & Neurobiology, 2013
- Differences in classification of COPD group using COPD assessment test (CAT) or modified Medical Research Council (mMRC) dyspnea scores: a cross-sectional analysesBMC Pulmonary Medicine, 2013
- Peak and average rectified EMG measures: Which method of data reduction should be used for assessing core training exercises?Journal of Electromyography and Kinesiology, 2011
- Myoelectric control systems—A surveyBiomedical Signal Processing and Control, 2007
- Obstructive and restrictive spirometric patterns: fixed cut-offs for FEV1/FEV6 and FEV6European Respiratory Journal, 2006
- Inspiratory muscular activation during threshold® therapy in elderly healthy and patients with COPDJournal of Electromyography and Kinesiology, 2005
- FEV6 Is an Acceptable Surrogate for FVC in the Spirometric Diagnosis of Airway Obstruction and RestrictionAmerican Journal of Respiratory and Critical Care Medicine, 2000