title: Sleep versus wake classification from heart rate variability using computational intelligence: consideration of rejection in classification models.
abstract: Reliability of classification performance is important for many biomedical applications. A classification model which considers reliability in the development of the model such that unreliable segments are rejected would be useful, particularly, in large biomedical data sets. This approach is demonstrated in the development of a technique to reliably determine sleep and wake using only the electrocardiogram (ECG) of infants. Typically, sleep state scoring is a time consuming task in which sleep states are manually derived from many physiological signals. The method was tested with simultaneous 8-h ECG and polysomnogram (PSG) determined sleep scores from 190 infants enrolled in the collaborative home infant monitoring evaluation (CHIME) study. Learning vector quantization (LVQ) neural network, multilayer perceptron (MLP) neural network, and support vector machines (SVMs) are tested as the classifiers. After systematic rejection of difficult to classify segments, the models can achieve 85%-87% correct classification while rejecting only 30% of the data. This corresponds to a Kappa statistic of 0.65-0.68. With rejection, accuracy improves by about 8% over a model without rejection. Additionally, the impact of the PSG scored indeterminate state epochs is analyzed. The advantages of a reliable sleep/wake classifier based only on ECG include high accuracy, simplicity of use, and low intrusiveness. Reliability of the classification can be built directly in the model, such that unreliable segments are rejected.
authors.affiliation: Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13699, USA. lewickat@clarkson.edu
authors.forename: Aaron
authors.initials: A
authors.lastname: Lewicke
authors.forename: Edward
authors.initials: E
authors.lastname: Sazonov
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Corwin
authors.forename: Michael
authors.initials: M
authors.lastname: Neuman
authors.forename: Stephanie
authors.initials: S
authors.lastname: Schuckers
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D007231:Infant, Newborn; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods; D017286:Polysomnography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012894:Sleep Stages / Q000502:physiology*; D014851:Wakefulness / Q000502:physiology*