title: An ECG oximetry system for identifying obstructive and central apnoea events.
abstract: An automatic algorithm for processing simultaneously acquired electrocardiogram (ECG) and oximetry signals that identifies epochs of pure central apnoea, epochs containing obstructive apnoea and epochs of normal breathing is presented. The algorithm uses time and spectral features from the ECG derived heart-rate and respiration information, as well as features capturing desaturations from the oximeter sensor. Evaluation of performance of the system was achieved by using leave-one-record-out cross validation on the St. Vincent's University Hospital / University College Dublin Sleep Apnea Database from the Physionet collections of recorded physiologic signals. When classifying the three epoch types, our system achieved a specificity of 80%, a sensitivity to central apnoea of 44% and sensitivity to obstructive apnoea of 35%. A sensitivity of 81% was achieved when the central and obstructive epochs were combined into one class.
authors.forename: Philip
authors.initials: P
authors.lastname: de Chazal
authors.forename: Nadi
authors.initials: N
authors.lastname: Sadr
authors.forename: Madhuka
authors.initials: M
authors.lastname: Jayawardhana
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D004562:Electrocardiography / Q000295:instrumentation / Q000379:methods*; D005260:Female; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D008297:Male; D010092:Oximetry / Q000295:instrumentation* / Q000379:methods; D012119:Respiration; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000503:physiopathology; D020182:Sleep Apnea, Central; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis / Q000503:physiopathology