BackgroundDetection and quantification of cyclic alternating patterns (CAP) components has the potential to serve as a disease bio-marker. Few methods exist to discriminate all the different CAP components, they do not present appropriate sensitivities, and often they are evaluated based on accuracy (AC) that is not an appropriate measure for imbalanced datasets.MethodsWe describe a knowledge discovery methodology in data (KDD) aiming the development of automatic CAP scoring approaches. Automatic CAP scoring was faced from two perspectives: the binary distinction between A-phases and B-phases, and also for multi-class classification of the different CAP components. The most important KDD stages are: extraction of 55 features, feature ranking/transformation, and classification. Classification is performed by (i) support vector machine (SVM), (ii) k-nearest neighbors (k-NN), and (iii) discriminant analysis. We report the weighted accuracy (WAC) that accounts for class imbalance.ResultsThe study includes 30 subjects from the CAP Sleep Database of Physionet. The best alternative for the discrimination of the different A-phase subtypes involved feature ranking by the minimum redundancy maximum relevance algorithm (mRMR) and classification by SVM, with a WAC of 51%. Concerning the binary discrimination between A-phases and B-phases, k-NN with mRMR ranking achieved the best WAC of 80%.ConclusionsWe describe a KDD that, to the best of our knowledge, was for the first time applied to CAP scoring. In particular, the fully discrimination of the three different A-phases subtypes is a new perspective, since past works tried multi-class approaches but based on grouping of different sub-types. We also considered the weighted accuracy, in addition to simple accuracy, resulting in a more trustworthy performance assessment. Globally, better subtype sensitivities than other published approaches were achieved.

Cyclic alternating patterns (CAPs) occur during normal sleep, but higher CAP rates are associated with abnormal conditions, such as epilepsy. Efficient automatic classification of CAP A-phase sub-types would be of remarkable importance for the consideration of CAP as a disease bio-marker. This paper reports a multi-step methodology for the classification of A-phases subtypes. The methodology encompasses: feature extraction, feature ranking, and classification (Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Discriminant Analysis (DA)). The study was carried out on 30 subjects with nocturnal frontal lobe epilepsy. The best classifier is based on a SVM that achieved an accuracy of 71%. For each Aphase subtype, i.e. A1, A2, and A3, the sensitivities were 55%, 37% and 25%, respectively. The classifiers developed are an innovation compared to what is found on literature, because they are designed to detect all subtypes and achieved better performance values. However, the performance values still need to be improved to achieve a reliable classifier that would not need a human technician supervision.

Healthy human aging is associated with a deterioration of visual acuity, retinal thinning, visual field map shrinkage and increasing population receptive field sizes. Here we ask how these changes are related to each other in a cross-sectional sample of fifty healthy adults aged 20–80 years. We hypothesized that age-related loss of macular retinal ganglion cells may lead to decreased visual field map sizes, and both may lead to increased pRF sizes in the cortical central visual field representation. We measured our participants’ perceptual corrected visual acuity using standard ophthalmological letter charts. We then measured their early visual field map (V1, V2 and V3) functional population receptive field (pRF) sizes and structural surface areas using fMRI, and their retinal structure using high-definition optical coherence tomography. With increasing age visual acuity decreased, pRF sizes increased, visual field maps surface areas (but not whole-brain surface areas) decreased, and retinal thickness decreased. Among these measures, only functional pRF sizes predicted perceptual visual acuity, and Bayesian statistics support a null relationship between visual acuity and cortical or retinal structure. However, pRF sizes were in turn predicted by cortical structure only (visual field map surface areas), which were only predicted by retinal structure (thickness). These results suggest that simultaneous disruptions of neural structure and function throughout the early visual system may underlie the deterioration of perceptual visual acuity in healthy aging.

The Cyclic Alternating Pattern (CAP) is a periodic cerebral activity prevalent during Non-Rapid Eye Movement (NREM) sleep-stages. The CAP is composed by A-phases that are related to a change in amplitude, frequency or both from the background activity epochs, called B-phases. Depending on the type of increase the A-phase could be classified as A1, A2 or A3 subtype. This paper proposes the usage of the Teager Energy Operator (TEO) to analyze the amplitude changes in the different frequency-bands to detect A-phases subtypes. The TEO classification performance is compared with the performance of a state-of-the art EEG feature, applied previously for CAP scoring and referred as the macro-micro structure descriptor (MMSD). In general, the TEO is the best feature and the improved results were obtained in the delta band for the A1 and A2 sub-types. More precisely, a sensitivity and specificity of 80.31% and 82.93% were obtained for the A1 subtype, respectively. A2 phases were detected with 76.96% of sensitivity and 73.22% of specificity. The two features detected A3 subtype with approximately the same sensitivity (approx. 70%) and specificity (approx. 75%), however the results were improved by considering the highest frequency band. These results are consistent with the frequency content of the different sub-phases.

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