Mouse Anti Human Emerin (amino acids 11-17) Hybridoma [NBOFN9(8C0)]

The neurological disease such as the epilepsy is diagnosed using the analysis of electroencephalogram (EEG) recordings. The areas of the brain associated with the consequence of epilepsy are termed as epileptogenic regions. The focal EEG signals are generated from epileptogenic areas, and the nonfocal signals are obtained from other regions of the brain. Thus, the classification of the focal and non-focal EEG signals are necessary for locating the epileptogenic areas during surgery for epilepsy. In this paper, we propose a novel method for the automated classification of focal and non-focal EEG signals. The method is based on the use of the synchrosqueezing transform (SST) and deep convolutional neural network (CNN) for the classification. The time-frequency matrices of EEG signal are evaluated using both Fourier SST (FSST) and wavelet SST (WSST). The two-dimensional (2D) deep CNN is used for the classification using the time-frequency matrix of EEG signals. The experimental results reveal that the proposed method attains the accuracy, sensitivity, and specificity values of more than 99% for the classification of focal and non-focal EEG signals. The method is compared with existing approaches for the discrimination of focal and non-focal categories of EEG signals.

Parkinson's disease (PD) is a progressive neurological disorder that mainly affects people in old age. Abnormality in the speech signals has been reported as a biomarker to detect PD. This study explores the use of Hilbert spectrum (HS) based features to model voice impairments in people affected by PD. The instantaneous energy deviation cepstral coefficient (IEDCC) is proposed. Statistical analyses show that the proposed feature is an effective and relevant biomarker for PD detection and evaluation of the dysarthria level in speech affected by PD. The capability of the proposed features to differentiate between PD and healthy people is evaluated upon five sustained vowels and ten isolated words from the standard PC-GITA database. The average accuracy of the proposed approach ranges from 82 % to 90 % with vowels, whereas for words the average accuracy ranges between 80 % and 91 %. Besides PD detection, the dysarthria level is evaluated according to the m-FDA scale. Spearman's correlation coefficients (rho) are computed between the estimated m-FDA values and the original scores. Correlations of up to 0.75 are obtained with vowel/o/, while 0.77 is the highest correlation obtained with the word/reina/. The developed models are further validated with a separate and independent dataset. The classification accuracy in these additional recordings ranges between 50 % and 80 % with vowels and from 50 % to 82 % with words. The promising results obtained on the additional test set indicate that the proposed method is suitable to perform the automatic detection of PD speakers in real-world conditions. (C) 2020 Elsevier Ltd. All rights reserved.