Multichannel interictal spike activity detection using time–frequency entropy measure

Abstract

Localization of interictal spikes is an important clinical step in the pre-surgical assessment of pharmacoresistant epileptic patients. The manual selection of interictal spike periods is cumbersome and involves a considerable amount of analysis workload for the physician. The primary focus of this paper is to automate the detection of interictal spikes for clinical applications in epilepsy localization. The epilepsy localization procedure involves detection of spikes in a multichannel EEG epoch. Therefore, a multichannel Time-Frequency (T-F) entropy measure is proposed to extract features related to the interictal spike activity. Least squares support vector machine is used to train the proposed feature to classify the EEG epochs as either normal or interictal spike period. The proposed T-F entropy measure, when validated with epilepsy dataset of 15 patients, shows an interictal spike classification accuracy of 91.20%, sensitivity of 100% and specificity of 84.23%. Moreover, the area under the curve of Receiver Operating Characteristics plot of 0.9339 shows the superior classification performance of the proposed T-F entropy measure. The results of this paper show a good spike detection accuracy without any prior information about the spike morphology.

An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis

“…where, I gt expresses to the ground truth (GT) and I ROI points for the segmented image with the proposed strategy. Other related works implemented on brain MRI can be found in [41][42][43][44][45][46][47][48][49].…”

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

“…Earlier research confirms that imaging based activities offer more apparent information on brain irregularity compared to the signal supported assessment [1][2][3]. Mapping of brain signal along with the brain image is also a flourishing research field [4]. To have superior perceptive on brain irregularity, it is essential to consider imaging procedures, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Positron Emission Tomography (PET), which are widely adopted in clinics [5][6][7].…”

A Cognitive Approach for Flower Image Classification Based on Convolution Neural Networks.

“…A feature classifier is selected for identification of images. Some of the commonly used classifiers are Linear Discriminant Analysis (LDA), Decision Tree, Bayes classifier, and Support vector machine [3]. Literature survey on applications of Machine Learning (ML) for image classification reports high classification accuracy when the dataset size is less.…”