Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Epilepsy is a chronic neurological disorder that affects the function of the brain in people of all ages. It manifests in the electroencephalogram (EEG) signal which records the electrical activity of the brain. Various image processing, signal processing, and machine-learning based techniques are employed to analyze epilepsy, using spatial and temporal features. The nervous system that generates the EEG signal is considered nonlinear and the EEG signals exhibit chaotic behavior. In order to capture these nonlinear dynamics, we use reconstructed phase space (RPS) representation of the signal. Earlier studies have primarily addressed seizure detection as a binary classification (normal vs. ictal) problem and rarely as a ternary class (normal vs. interictal vs. ictal) problem. We employ transfer learning on a pre-trained deep neural network model and retrain it using RPS images of the EEG signal. The classification accuracy of the model for the binary classes is (98.5±1.5)% and (95±2)% for the ternary classes. The performance of the convolution neural network (CNN) model is better than the other existing statistical approach for all performance indicators such as accuracy, sensitivity, and specificity. The result of the proposed approach shows the prospect of employing RPS images with CNN for predicting epileptic seizures. © 2020 by the Journal of Biomedical Research.

Computer Science

School of Computer Science and Engineering

Chennai Campus

World University root node

Fulltext

Journal of Biomedical Research

Deep learning approach to detect seizure using reconstructed phase space images

Question Answering is one of the most common applications for data acquisition. Although the majority of text-mining applications strive to improve the user experience and the tools used to find appropriate answers, the problems still exist because the web content is constantly increasing. The Questions Classification (QC) task is one of the main tasks in improving the classification system is to classify types of questions in the text mining application. A large number of QC methods are introduced to help resolve classification problems, most of which are bag of words approaches. In this project, we propose a QC system that uses Parts of Speech (POS) Tagger and Named Entity Recognition (NER) Tagger from the Stanford core Natural Language Processing (NLP) to classify the questions correctly. We started by cleaning the data by removing the available labels in the questions then we proceed by tagging the questions by splitting words and tagging each and every words in the input question with the POS Tagger. After this step, we will convert them into a pattern without changing the structure of the question. Then we proceed by tagging the question with NER Tagger. Finally, we will do confirmation process for certain question types which is performed by confirming question type module to make the system work efficiently.

Department of Communication Engineering

School of Electronics Engineering

Vellore Campus

International Journal of Innovative Technology and Exploring Engineering Regular Issue

Question Classification using a Rule based Model

Epilepsy is a group of neurological disorders identifiable by infrequent but recurrent seizures. Seizure prediction is widely recognized as a significant problem in the neuroscience domain. Developing a Brain-Computer Interface (BCI) for seizure prediction can provide an alert to the patient, providing a buffer time to get the necessary emergency medication or at least be able to call for help, thus improving the quality of life of the patients. A considerable number of clinical studies presented evidence of symptoms (patterns) before seizure episodes and thus, there is large research on seizure prediction, however, there is very little existing literature that illustrates the use of structured processes in machine learning for predicting seizures. Limited training data and class imbalance (EEG segments corresponding to preictal phase, the duration just before the seizure, to about an hour prior to the episode, are usually in a tiny minority) are a few challenges that need to be addressed when employing machine learning for this task. In this paper we present a comparative study of various machine learning approaches that can be used for classification of EEG signals into preictal and interictal (Interictal is the time between seizures) using the features extracted from the intracranial EEG. Publicly available data has been used for this purpose for both human and canine subjects. After data pre-processing and extensive feature extraction, different models are trained and are effectively used to analyze the temporal dynamics of the brain (interictal and preictal) in affected subjects. We present the improved results for various classification algorithms, with AUROC values of best classification models at 0.99. © BEIESP.

International Journal of Engineering and Advanced Technology

Epileptic seizure prediction through machine learning and spatio-temporal features based time series analysis of intracranial electroencephalogram data

This study aims to classify the scene and object using brain waves signal. The dataset captured by the electroencephalograph (EEG) device by placing the electrodes on scalp to measure brain signals are used. Using captured EEG dataset, classifying the scene and object by decoding the changes in the EEG signals. In this study, independent component analysis, event-related potentials, and grand mean are used to analyze the signal. Machine learning algorithms such as decision tree, random forest, and support vector machine are used to classify the data. This technique is useful in forensic as well as in artificial intelligence for developing future technology. © 2017 The Authors.

Asian Journal of Pharmaceutical and Clinical Research

Scene and object classification using brain waves signal

According to the Road Accident Statistics in India report presented by the NDTV in 2016. One death for every 4 minutes because of a road accident in India. The major reasons for the following accidents are happening due to the Drowsiness and driver fatigue nature during the driving. Lack of sleep is one of the major reasons for drowsiness. The boundary between feeling sleepy and established sleep is known as drowsiness. Driver Fatigue is one major factor for 20% global accidents. There are many system attempted to identify the drowsiness of the driver in a driving simulation by recording the Non-visual signals and some systems concentrated on the developing a face recognition technique while driving and alerting the driver by rising the alarm in computer software application based interface. This research is aimed to build a real-time application which can be used to detect the fatigue or drowsiness in driving conditions and alert the driver whenever drowsiness is detected. The application is able Predict the behavior of driver by measuring the visual measurements in the driving conditions in a feasible model which can be used by the all types of commercial and personal vehicles. © IAEME Publication.