Seizure Prediction using Hilbert Huang Transform on Field Programmable Gate Array
Abstract : The Hilbert Huang Transform (HHT) has been used extensively in the time-frequency analysis of electroencephalography (EEG) signals and Brain-Computer Interfaces. Most studies utilizing the HHT for extracting features in seizure prediction have used intracranial EEG recordings. Invasive implants in the cortex have unknown long term consequences and pose the risk of complications during surgery. This added risk dimension makes them unsuitable for continuous monitoring as would be the requirement in a Body Area Network. We present an HHTbased system on Field Programmable Gate Array (FPGA) for predicting epileptic seizures using scalp EEG. We use bandwidth features of Intrinsic Mode Functions and obtain a classification accuracy of close to 100% using patient-specific classifiers in software. Details of FPGA implementation are also given.
? It is prospective to replace the existing bulk-optical EOM with photonic integrated chips, where sophisticated and tailored nonlinear transformations can be realized using nested optical circuits on a single chip.
? It takes advantage of electro-optic nonlinear transformation in EOM, and flexible signal generation and controllable timing, highly programmable signal filtering, and stable delay logic implementation in FPGA.
? However, such training is usually energy and time consuming, and its efficiency varies by the complexity of task, the size of the network, the nonlinearity and connectivity between the nodes.
? The detection of seizures using binary classifiers to distinguish between different EEG-based features in the ictal and interictal periods may be considered a largely solved problem.
? Despite many promising Brain-Computer Interfaces (BCIs) being proposed in the literature, most have been implemented in software and remain confined to research laboratories - the necessity of a bulky computer being one of the primary drawbacks in bringing them to the everyday user.
? With more and more problems being identified as computationally complex and/or excessively time consuming, alternative computational paradigms are actively explored to solve these problems as they increasingly emerge.
• This work proposes an efficient approach to extract the features of epileptic seizures by decomposing EEG into band limited signals termed as IMF’s by empirical decomposition EMD.
• For detecting and classifying epileptic seizures various time-frequency methods have been proposed most importantly the wavelet transform and all these are non- stationary methods.
• The proposed technique provides a tool for neuro physicians for identifying brain abnormalities due to epilepsy and also can also be developed as ASIC.
• The proposed technique essentially provides benefits like fast diagnosis, high accuracy with good sensitivity and specificity.
? The HHT has been a popular tool used in the study of EEG with its ability to analyze both nonlinear and non-stationary biomedical signals by decomposing them into data-dependent basis functions.
? A bandpass FIR filter with cutoffs set to 0.5 Hz and 100 Hz and a 60 Hz notch filter were used at the preprocessing stage to remove baseline wander and suppress powerline noise.
? The design utilizes sawtooth interpolation instead of the cubic spline interpolation used in software in order to facilitate continuously processing incoming data.
? An Snumber termination criteria is used instead of the conventional convergence criteria involving the number of zero crossings and the number of local extrema of the signal.
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