Low-Complexity Seizure Prediction From iEEG/sEEG Using Spectral Power and Ratios of Spectral Power


Prediction of seizures may be a troublesome problem as the EEG patterns aren't wide-sense stationary and change from seizure to seizure, electrode to electrode, and from patient to patient. This paper presents a novel patient-specific algorithm for prediction of seizures in epileptic patients from either one or 2 single-channel or bipolar channel intra-cranial or scalp electroencephalogram (EEG) recordings with low hardware complexity. Spectral power features are extracted and their ratios are computed. For each channel, a complete of forty four features together with eight absolute spectral powers, eight relative spectral powers and 28 spectral power ratios are extracted each two seconds using a 4-second window with a 50% overlap. These options are then ranked and selected in a very patient-specific manner employing a two-step feature selection. Selected features are any processed by a second-order Kalman filter and then input to a linear support vector machine (SVM) classifier. The algorithm is tested on the intra-cranial EEG (iEEG) from the Freiburg database and scalp EEG (sEEG) from the MIT Physionet database. The Freiburg database contains eighty seizures among 18 patients in 427 hours of recordings. The MIT EEG database contains seventy eight seizures from seventeen youngsters in 647 hours of recordings. It is shown that the proposed algorithm can achieve a sensitivity of 100p.c and a median false positive rate (FPR) of zero.0324 per hour for the iEEG (Freiburg) database and a sensitivity of 98.68percent and an average FPR of 0.0465 per hour for the sEEG (MIT) database. These results are obtained with leave-one-out cross-validation where the seizure being tested is often unseen from the training set. The proposed algorithm also incorporates a low complexity because the spectral powers will be computed using FFT. The area and power consumption of the proposed linear SVM are two to 3 orders of magnitude but a radial basis operate kernel SVM (RBF-SVM) classifier. Furthermore, the entire energy consumption of a system using linear - VM is reduced by 8p.c to twenty three% compared to system using RBF-SVM.

Did you like this research project?

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

PROJECT TITLE :Improved Low-Complexity Sphere Decoding for Generalized Spatial Modulation - 2018ABSTRACT:During this letter, two types of improved sphere decoding (SD) algorithms for generalized spatial modulation (GSM), termed
PROJECT TITLE :Algorithm and Architecture of a Low-Complexity and High-Parallelism Preprocessing-Based K -Best Detector for Large-Scale MIMO Systems - 2018ABSTRACT:As a branch of sphere decoding, the K-best method has played an
PROJECT TITLE :Compressive Channel Estimation and Multi-User Detection in C-RAN With Low-Complexity Methods - 2018ABSTRACT:This Project considers the channel estimation (CE) and multi-user detection (MUD) problems in cloud radio
PROJECT TITLE :A Learning Approach for Low-Complexity Optimization of Energy Efficiency in Multicarrier Wireless Networks - 2018ABSTRACT:This Project proposes computationally efficient algorithms to maximise the energy efficiency
PROJECT TITLE :Low-Complexity VLSI Design of Large Integer Multipliers for Fully Homomorphic Encryption - 2018ABSTRACT:Giant integer multiplication has been widely employed in fully homomorphic encryption (FHE). Implementing possible

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

Project Enquiry