No abstract

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.

The innovation of digital medical images has led to the requirement of rich descriptors and efficient retrieval tool. Thus, the Content Based Image Retrieval (CBIR) technique is essential in the domain of image retrieval. Due to the growing medical image data, the searching or retrieving a relevant image from the dataset is a major problem. To address this problem, this paper propose a new medical image retrieval technique, namely Multiple Kernel Scale Invariant Feature Transform-based Deep Recurrent Neural Network (MKSIFT-Deep RNN) using the image contents. The goal is to present an effective tool that can be utilized for effective retrieval of image from huge medical image database. Here, MKSIFT is adapted for extracting the relevant features obtained from acquired input image. Moreover, MKSIFT evaluates the key point descriptor using kernels functions, wherein the weights are allocated to kernels. The feature vectors are employed in the Deep RNN for classifying the images by training the classifier, which is considered as training phase. In testing phase, a set of query images is given to the classifier which adapts Tanimoto similarity for retrieving the images. The proposed MKSIFT-Deep RNN outperformed other methods with maximal precision of 93.723%, maximal recall of 93.652% and maximal F-measure of 93.687%.