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Query: UMLS:C0037315 (sleep apnea)
 8,000 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The aim of this study is to assess the utility of traditional statistical pattern recognition techniques to help in obstructive sleep apnoea (OSA) diagnosis. Classifiers based on quadratic (QDA) and linear (LDA) discriminant analysis, K-nearest neighbours (KNN) and logistic regression (LR) were evaluated. Spectral and nonlinear input features from oxygen saturation (SaO(2)) signals were applied. A total of 187 recordings from patients suspected of suffering from OSA were available. This initial dataset was divided into training set (74 subjects) and test set (113 subjects). Twelve classification algorithms were developed by applying QDA, LDA, KNN and LR with spectral features, nonlinear features and combination of both groups. The performance of each algorithm was measured on the test set by means of classification accuracy and receiver operating characteristic (ROC) analysis. QDA, LDA and LR showed better classification capability than KNN. The classifier based on LDA with spectral features provided the best diagnostic ability with an accuracy of 87.61% (91.05% sensitivity and 82.61% specificity) and an area under the ROC curve (AROC) of 0.925. The proposed statistical pattern recognition techniques could be applied as an OSA screening tool.
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PMID:Assessment of four statistical pattern recognition techniques to assist in obstructive sleep apnoea diagnosis from nocturnal oximetry. 1959 90

In this paper, we present an approximation method for principal component analysis (PCA) and apply it to estimating the respiration from the overnight ECG signal. The approximation method is computationally fast with low memory requirements. We compare it to a full PCA method which is applied to segments of the ECG. Features were calculated from the two ECG derived respiration signals (EDR) and classifiers trained to detect obstructive sleep apnoea (OSA). The Extreme Learning Machine and Linear Discriminant classifier were used to classify the recordings. The data from 35 overnight ECG recordings from MIT PhysioNet Apnea-ECG training database was utilized in the paper. Apnoea detection was evaluated with leave-one-record-out cross validation. The approximated PCA method obtained the highest accuracy of 76.4% by ELM classifier at fan-out 10 and accuracy of 78.4% by LDA. While, the segmented PCA achieved lower accuracies for both classifiers, 75.9% by ELM classifier and 76.6% by LDA. We conclude that the approximation method for PCA is well suited to deriving the respiration signal from overnight ECGs.
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PMID:A fast approximation method for principal component analysis applied to ECG derived respiration for OSA detection. 2826 67

In this paper, we extracted hand crafted features from the ECG signals and evaluated the performance of different combination of features for sleep apnoea detection. We calculated the ECG derived respiratory (EDR) signal using three methods (QRS area, amplitude demodulation and fast PCA methods) and then calculated the cardiopulmonary coupling (CPC) spectrum using each EDR method. We then extracted features from the CPC spectrums and the time and frequency representations of the heart rate variability (HRV) and EDR signals Then, we compared the performance results of different combinations of the features used for automated sleep apnoea detection. We also applied a temporal optimisation method by averaging the features of every three adjacent epochs. Two classifiers were used to detect sleep apnoea: the extreme learning machine (ELM), and linear discriminant analysis. The features were evaluated on the MIT PhysioNet Apnea-ECG database. Apnoea detection was evaluated with leave-one-record-out cross-validation. The PCA CPC features obtained the highest accuracy of 86.5% and AUC of 0.94 using LDA classifier. The performance results of the combined features (of PCA method) obtained the same results. We conclude that for this study, the CPC features using fast PCA method are our best feature set for sleep apnoea detection.
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PMID:Comparing Different Methods of Hand-crafted HRV, EDR and CPC Features for Sleep Apnoea Detection. 3194 18