Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep.

This study aims to develop an automatic detector of the A phases of the cyclic alternating pattern, periodic activity that generally occurs during non-REM (NREM) sleep. Eight polysomnographic recordings from healthy subjects were examined. From EEG recordings, five band descriptors, an activity descriptor and a variance descriptor were extracted and used to train different machine-learning algorithms. A visual scoring provided by an expert clinician was used as golden standard. Four alternative mathematical machine-learning techniques were implemented: (1) discriminant classifier, (2) support vector machines, (3) adaptive boosting, and (4) supervised artificial neural network. The results of the classification, compared with the visual analysis, showed average accuracies equal to 84.9 and 81.5% for the linear discriminant and the neural network, respectively, while AdaBoost had a slightly lower accuracy, equal to 79.4%. The SVM leads to accuracy of 81.9%. The performance achieved by the automatic classification is encouraging, since an efficient automatic classifier would benefit the practice in everyday clinics, preventing the physician from the time-consuming activity of the visually scoring of the sleep microstructure over whole 8-h sleep recordings. Finally, the classification based on learning algorithms would provide an objective criterion, overcoming the problems of inter-scorer disagreement.

Liver hemangioma and vascular liver diseases in patients with systemic lupus erythematosus.

AIM: To investigate whether systemic lupus erythematosus (SLE) is associated with benign focal liver lesions and vascular liver diseases, since these have been occasionally reported in SLE patients. METHODS: Thirty-five consecutive adult patients with SLE and 35 age- and sex-matched healthy controls were evaluated. Hepatic and portal vein patency and presence of focal liver lesions were studied by colour-Doppler ultrasound, computerized tomography and magnetic resonance were used to refine the diagnosis, clinical data of SLE patients were reviewed. RESULTS: Benign hepatic lesions were common in SLE patients (54% vs 14% controls, P < 0.0001), with hemangioma being the most commonly observed lesion in the two groups. SLE was associated with the presence of single hemangioma [odds ratios (OR) 5.05; 95% confidence interval (CI) 1.91-13.38] and multiple hemangiomas (OR 4.13; 95% CI 1.03-16.55). Multiple hemangiomas were associated with a longer duration of SLE (9.9 ± 6.5 vs 5.5 ± 6.4 years; P = 0.04). Imaging prior to SLE onset was available in 9 patients with SLE and hemangioma, showing absence of lesions in 7/9. The clinical data of our patients suggest that SLE possibly plays a role in the development of hemangioma. In addition, a Budd-Chiari syndrome associated with nodular regenerative hyperplasia (NRH), and a NRH associated with hepatic hemangioma were observed, both in patients hospitalized for abdominal symptoms, suggesting that vascular liver diseases should be specifically investigated in this population. CONCLUSION: SLE is associated with 5-fold increased odds of liver hemangiomas, suggesting that these might be considered among the hepatic manifestations of SLE.

Characterization of A phases during the cyclic alternating pattern of sleep.

OBJECTIVE: This study aims to identify, starting from a single EEG trace, quantitative distinctive features characterizing the A phases of the Cyclic Alternating Pattern (CAP). METHODS: The C3-A2 or C4-A1 EEG leads of the night recording of eight healthy adult subjects were used for this analysis. CAP was scored by an expert and the portions relative to NREM were selected. Nine descriptors were computed: band descriptors (low delta, high delta, theta, alpha, sigma and beta); Hjorth activity in the low delta and high delta bands; differential variance of the EEG signal. The information content of each descriptor in recognizing the A phases was evaluated through the computation of the ROC curves and the statistics sensitivity, specificity and accuracy. RESULTS: The ROC curves show that all the descriptors have a certain significance in characterizing A phases. The average accuracy obtained by thresholding the descriptors ranges from 59.89 (sigma descriptor) to 72.44 (differential EEG variance). CONCLUSIONS: The results show that it is possible to attribute a significant quantitative value to the information content of the descriptors. SIGNIFICANCE: This study gives a mathematical confirm to the features of CAP generally described qualitatively, and puts the bases for the creation of automatic detection methods.

Automatic detection of a phases of the cyclic alternating pattern during sleep.

This study aimed to develop an automatic algorithm to detect the activation phases (A phases) of the Cyclic Alternating Pattern. The sleep EEG microstructure of 4 adult, healthy subjects was scored by a sleep medicine expert. Features were calculated from each of the six EEG bands (low delta, high delta, theta, alpha, sigma and beta), and three additional characteristics were computed: the Hjorth activity in the low delta and high delta bands, and the differential variance of the raw EEG signal. The correlation between couples of features was analyzed to find redundancies for the automatic analysis. The features were used to train an Artificial Neural Network to automatically find the A phases of CAP. The data were divided into training, validation and testing set, and the visual scoring provided by the clinician was used as the desired output. The statistics on the second by second classification show an average sensitivity equal to 76%, specificity equal to 83% and accuracy equal to 82%. The results obtained are encouraging, since an automatic classification of the A phases could benefit the practice in clinics, preventing the physician from the time-consuming activity of visually scoring the sleep microstructure over the whole eight-hour sleep recordings. Moreover, it would provide an objective criterion capable of overcoming the problems of inter-scorer variability.

Ocular surface and intraocular inflammation are related in SS-I and rheumatoid arthritis patients.

The purpose of this study is to evaluate the intraocular inflammation at a sub clinical level, in patients suffering from Sjogren's syndrome (SS-I) and Rheumatoid Arthritis (RA), to relate it with the ocular surface status and to verify the diagnostic performance of the method. Twenty-eight patients suffering from SS-I, 31 patients suffering from RA and 31 normal subjects matched in age and gender were included in the study. A Kowa 500F laser cell flare meter was utilized to quantify the aqueous cells and flare in vivo, ocular surface inflammation was graded by conjunctival cytology and dosage of serum albumin in tears, eye dryness was scored with Tear Function Index. All data resulted significantly different in both SS-I and RA patients vs. control group and also different comparing SS-I vs. RA patients group, except for the Flare values. A blood-aqueous barrier breakdown occurs either in SS-I and RA patients; the degree of the damage is related with ocular surface inflammation and dryness. We recommend the aqueous flare be analysed in those rheumatic patients where an ocular surface inflammation has been documented.