Regional differences in cell-mediated immunity in people with diabetic peripheral neuropathy.

AIM: To study cell-mediated immunity in the feet of people with type 2 diabetes with polyneuropathy. METHODS: In a cohort comprising people with type 2 diabetes with polyneuropathy (n = 17) and without polyneuropathy (n = 12) and a healthy control group (n = 12) indurations due to delayed-type hypersensitivity responses to intracutaneous Candida albicans antigen were determined in the foot and compared with those in the arm (an area relatively spared in diabetic polyneuropathy). The sizes of indurations on the foot were correlated with electromyographic measurements in the participants with diabetes. RESULTS: No differences were observed in the median size of indurations between the foot and arm in healthy controls and participants without polyneuropathy; in participants with polyneuropathy, induration sizes on the foot were smaller than on the arm: 0 (95% CI 0 to 1) vs 5 (95% CI 2 to 6) mm (P < 0.01). In participants with diabetes, larger indurations correlated with better nerve function (Spearman's rho 0.35 to 0.39). CONCLUSION: Our findings suggest that diabetic peripheral polyneuropathy negatively affects cell-mediated immunity in the foot. (Clinical Trials registry no.: NCT01370837).

Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection.

Continuous electroencephalographic monitoring of critically ill patients is an established procedure in intensive care units. Seizure detection algorithms, such as support vector machines (SVM), play a prominent role in this procedure. To correct for inter-human differences in EEG characteristics, as well as for intra-human EEG variability over time, dynamic EEG feature normalization is essential. Recently, the median decaying memory (MDM) approach was determined to be the best method of normalization. MDM uses a sliding baseline buffer of EEG epochs to calculate feature normalization constants. However, while this method does include non-seizure EEG epochs, it also includes EEG activity that can have a detrimental effect on the normalization and subsequent seizure detection performance. In this study, EEG data that is to be incorporated into the baseline buffer are automatically selected based on a novelty detection algorithm (Novelty-MDM). Performance of an SVM-based seizure detection framework is evaluated in 17 long-term ICU registrations using the area under the sensitivity-specificity ROC curve. This evaluation compares three different EEG normalization methods, namely a fixed baseline buffer (FB), the median decaying memory (MDM) approach, and our novelty median decaying memory (Novelty-MDM) method. It is demonstrated that MDM did not improve overall performance compared to FB (p < 0.27), partly because seizure like episodes were included in the baseline. More importantly, Novelty-MDM significantly outperforms both FB (p = 0.015) and MDM (p = 0.0065).

Optimal training dataset composition for SVM-based, age-independent, automated epileptic seizure detection.

Automated seizure detection is a valuable asset to health professionals, which makes adequate treatment possible in order to minimize brain damage. Most research focuses on two separate aspects of automated seizure detection: EEG feature computation and classification methods. Little research has been published regarding optimal training dataset composition for patient-independent seizure detection. This paper evaluates the performance of classifiers trained on different datasets in order to determine the optimal dataset for use in classifier training for automated, age-independent, seizure detection. Three datasets are used to train a support vector machine (SVM) classifier: (1) EEG from neonatal patients, (2) EEG from adult patients and (3) EEG from both neonates and adults. To correct for baseline EEG feature differences among patients feature, normalization is essential. Usually dedicated detection systems are developed for either neonatal or adult patients. Normalization might allow for the development of a single seizure detection system for patients irrespective of their age. Two classifier versions are trained on all three datasets: one with feature normalization and one without. This gives us six different classifiers to evaluate using both the neonatal and adults test sets. As a performance measure, the area under the receiver operating characteristics curve (AUC) is used. With application of FBC, it resulted in performance values of 0.90 and 0.93 for neonatal and adult seizure detection, respectively. For neonatal seizure detection, the classifier trained on EEG from adult patients performed significantly worse compared to both the classifier trained on EEG data from neonatal patients and the classier trained on both neonatal and adult EEG data. For adult seizure detection, optimal performance was achieved by either the classifier trained on adult EEG data or the classifier trained on both neonatal and adult EEG data. Our results show that age-independent seizure detection is possible by training one classifier on EEG data from both neonatal and adult patients. Furthermore, our results indicate that for accurate age-independent seizure detection, it is important that EEG data from each age category are used for classifier training. This is particularly important for neonatal seizure detection. Our results underline the under-appreciated importance of training dataset composition with respect to accurate age-independent seizure detection.

EEG feature pre-processing for neonatal epileptic seizure detection.

Aim of our project is to further optimize neonatal seizure detection using support vector machine (SVM). First, a Kalman filter (KF) was used to filter both feature and classifier output time series in order to increase temporal precision. Second, EEG baseline feature correction (FBC) was introduced to reduce inter patient variability in feature distributions. The performance of the detection methods is evaluated on 54 multi channel routine EEG recordings from 39 both term and pre-term newborns. The area under the receiver operating characteristics curve (AUC) as well as sensitivity and specificity are used to evaluate the performance of the classification method. SVM without KF and FBC achieves an AUC of 0.767 (sensitivity 0.679, specificity 0.707). The highest AUC of 0.902 (sensitivity 0.801, specificity 0.831) is achieved on baseline corrected features with a Kalman smoother used for training data pre-processing and a KF used to filter the classifier output. Both FBC and KF significantly improve neonatal epileptic seizure detection. This paper introduces significant improvements for the state of the art SVM based neonatal epileptic seizure detection.

Neurophysiologic, audiometric and vestibular function tests in patients with hyperostosis cranialis interna.

OBJECTIVE: Hyperostosis cranialis interna (HCI) is an autosomal dominant sclerosing bone dysplasia affecting the skull base and the calvaria, characterized by cranial nerve deficits due to stenosis of neuroforamina. The aim of this study is to describe the value of several neurophysiological, audiometric and vestibular tests related to the clinical course of the disorder. METHODS: Ten affected subjects and 13 unaffected family members were recruited and tested with visual evoked potentials, masseter reflex, blink reflex, pure tone and speech audiometry, stapedial reflexes, otoacoustic emissions, brainstem evoked response audiometry and electronystagmography. RESULTS: Due to the symmetrical bilateral nature of this disease, the sensitivity of visual evoked potentials (VEPs), masseter reflex and blink reflex is decreased (25-37.5%), therefore reducing the value of single registration. Increased hearing thresholds and increased BERA latency times were found in 60-70%. The inter-peak latency I-V parameter in BERA has the ability to determine nerve encroachment reliably. 50% of the patients had vestibular abnormalities. No patient had disease-related absence of otoacoustic emissions, because the cochlea is not affected. CONCLUSION: In patients with HCI and similar craniofacial sclerosing bone dysplasias we advise monitoring of vestibulocochlear nerve function with tone and speech audiometry, BERA and vestibular tests. VEPs are important to monitor optic nerve function in combination with radiological and ophthalmologic examination. We do not advise the routine use of blink and masseter reflex.