Floating search methods in feature selection
Pattern Recognition Letters
Detection of Signals in Noise
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Neural Networks - 2005 Special issue: IJCNN 2005
Computer Methods and Programs in Biomedicine
A review on the combination of binary classifiers in multiclass problems
Artificial Intelligence Review
On the Complexity of Finite Sequences
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
Hierarchical linear support vector machine
Pattern Recognition
Expert Systems with Applications: An International Journal
Hierarchical framework for robust and fast multiple-target tracking in surveillance scenarios
Expert Systems with Applications: An International Journal
Fingerprint classification by a hierarchical classifier
Pattern Recognition
Hi-index | 12.05 |
Currently, Sleep Apnea-Hypopnea Syndrome (SAHS) is accurately diagnosed in Sleep Units. In the last decade, in order to reduce the burden for health systems and the consequent impact in patients, several home-located methods based on the binary classification of SAHS using overnight pulse oximetry (SpO"2) have been proposed. Binary classifiers give rise to higher accuracies, but the cost of misclassifying leads to high penalizations in terms of either health care costs or risks in patient's health. This study presents a novel hierarchical classification scheme for the four-class SAHS diagnosis using a set of features extracted from SpO"2 and reported in specialized literature. An accuracy of 82.6% was achieved in the assessment of the four-class classification. The proposed method could be useful in the diagnosis of SAHS in an ambulatory home-based setting and could alleviate under-diagnosis rate and the waiting lists in sleep units.