ABSTRACT:

Monitoring neonatal electroencephalogram (EEG) signal is useful in identifying neonatal convulsions which might be clinically invisible. Presence of burst suppression pattern in neonate EEG is a clear indication of epilepsy. Visual identification of burst patterns from recorded continuous raw EEG data is time consuming. On the other hand, automatic burst detection techniques mentioned in the standard literature mostly rely on comparison with respect to predefined static voltage or energy thresholds, thus becoming too specific. Burst detection using ratio information of quantitative feature values between burst segment and neighborhood background EEG segment is proposed in this paper. Features like ratio of mean nonlinear energy, power spectral density, variance and absolute voltage, when applied as an input to a support vector machine (SVM) classifier, provides high degree of separability between burst and normal (nonburst) EEG segments. Exhaustive simulation using various literature specified features and proposed feature combinations shows that the proposed feature set provides best classification accuracy compared to other reported burst detection methods. The results documented in this paper can be used as a reference of optimum quantitative EEG feature sets for distinguishing between burst and normal (nonburst) EEG segments.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE :Text Mining Based on Tax Comments as Big Data Analysis Using SVM and Feature Selection - 2018ABSTRACT:The tax provides an important role for the contributions of the economy and development of a rustic. The improvements
PROJECT TITLE :Distributed Feature Selection for Efficient Economic Big Data Analysis - 2018ABSTRACT:With the rapidly increasing popularity of economic activities, a large amount of economic data is being collected. Although
PROJECT TITLE :Automatic Feature Selection Technique for Next Generation Self-Organizing Networks - 2018ABSTRACT:Despite self-organizing networks (SONs) pursue the automation of management tasks in current cellular networks, the
PROJECT TITLE :Large-Scale Kernel-Based Feature Extraction via Low-Rank Subspace Tracking on a Budget - 2018ABSTRACT:Kernel-primarily based ways get pleasure from powerful generalization capabilities in learning a selection of
PROJECT TITLE :Feature Map Quality Score Estimation Through Regression - 2018ABSTRACT:Understanding the visual quality of a feature map plays a important role in many active vision applications. Previous works mostly rely on object-level

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry