56 research outputs found
Efficient Seizure Prediction and EEG Channel Selection Based on Multi-Objective Optimization
No full textEpileptic seizures are unpredictable events due to sudden abnormal electrical activities in the brain of epilepsy patients. A seizure can be predicted by analyzing the EEG signals to prevent unwanted life risks. The goal of this paper is to implement a method that will apply to design a lightweight, wearable, and efficient seizure prediction device. The proposed method will satisfy two objectives. The first objective is relevant feature extraction for the classification of EEG signals with excellent accuracy. The second objective is the use of fewer EEG channels. In this paper, one 1D-CNN is applied for feature extraction and classification of raw EEG signals for early prediction of seizure events. The 1D-CNN is faster compared to 2D-CNN, which uses fewer trainable parameters. Hence, it is suitable to implement a low-power energy-efficient seizure prediction device. In this paper, the NSGA-II algorithm is applied to get the optimum set of EEG channels for seizure prediction. The NSGA-II algorithm identifies a set of three EEG channels from twenty-two channels as the optimum channel set. The proposed method optimizes the EEG channels from 22 to 3, i.e., 86.36% channel reduction. It provides the classification accuracy, sensitivity, and specificity of 0.9651, 0.9655, and 0.9647, respectively. The proposed method is better than the state-of-the-art works under the condition of using three channels. The proposed method provides excellent performance using only three EEG channels, which will be applicable to design a lightweight, low-power, and wearable seizure prediction device
A novel double layered image steganography in spatial domain using triangular number system
No full textHigh capacity secure dynamic multi-bit data hiding using Fibonacci Energetic pixels
No full textSteganography and Steganalysis are becoming increasingly relevant in information forensics and hiding data in the higher bitplanes without keeping any perceptible signature into the image is a challenging problem in this area. In this paper, we propose a unique solution to this problem using Fibonacci numbers as base. The pixels are selected from the busy part of the image where noticeable changes in pixel intensities occur. The business of the pixels is determined by their Fibonacci energy. The pixels values are converted into Fibonacci base and their corresponding Fibonacci energies are estimated by the Fibonacci expansion of pixel intensities. The set of energetic pixels are considered according to the descending order of their energy values. The binary data are concealed into higher bitplanes (up to 5) of the Fibonacci base of the pixel intensities. We theoretically derive some nice combinatorial properties related to distortion of pixel intensities and also experimentally show that our algorithm withstands against visual, structural and statistical attacks. The average embedding capacity is 3.98 bpp and average PSNR is 39.59 dB. We also demonstrate that our method is capable of resisting from the series of benchmark tests provided by StirMark 4.0
Video watermarking for persistent and robust tracking of entertainment content (PARTEC)
No full textThe exploitation of film and video content on physical media, broadcast and Internet involves working with many large media files. The move to file-based workflows necessitates the copying and transfer of digital assets amongst many parties, but the detachment of assets and their metadata leads to issues of reliability, quality and security. This paper proposes a novel watermarking-based approach to deliver a unique solution to enable digital media assets to be maintained with their metadata persistently and robustly. Watermarking-based solution for entertainment content manifests new challenges, including maintaining high quality of the media content, robustness to compression and file format changes and synchronisation against scene editing. The proposed work addresses these challenges and demonstrates interoperability with an existing industrial software framework for media asset management (MAM) systems
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