Automatic recognition of epileptic seizure in EEG via support vector machine and dimension fractal

Schneider, Mauro; Mustaro, Pollyana Notargiacomo; Lima, Clodoaldo A. M.

doi:10.1109/ijcnn.2009.5179059

Cited by 15 publications

(7 citation statements)

References 20 publications

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“…The traditional procedure of analysing an EEG scan requires expensive manpower where a specialist is needed to review the whole EEG recording. As part of the ongoing research into epilepsy detection, automatic seizure identification methods have been considered, such as Wavelet Transform [5] and Autoregressive (AR) modelling [6] . These methods present a better resolution for short data segments, and they can be used when real-time data processing is required.…”

Section: Background Researchmentioning

confidence: 99%

Low-Power and Low-Cost Dedicated Bit-Serial Hardware Neural Network for Epileptic Seizure Prediction System

Kueh

Kázmierski

2018

IEEE J. Transl. Eng. Health Med.

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This paper presents results of using a simple bit-serial architecture as a method of designing an extremely low-power and low-cost neural network processor for epilepsy seizure prediction. The proposed concept is based on a novel bit-serial data processing unit (DPU) which implements the functionality of a complete neuron and uses bit-serial arithmetic. Arrays of DPUs are controlled by simple finite state machines. We show that epilepsy detection through such dedicated neural hardware is feasible and may facilitate development of wearable, low-cost and low-energy personalized seizure prediction equipment. The proposed processor extracts epileptic seizure characteristics from electroencephalogram (EEG) waveforms. In order to facilitate the classification of EEG waveforms, we develop a dedicated feature extraction hardware that provides inputs to the neural network. This approach has been tested using various network configurations and has been compared with related work. A complete system which can predict epileptic seizures with high accuracy has been implemented on an ALTERA Cyclone V FPGA using 3931 ALMs which constitutes about 7% of the Cyclone V A7 capacity. The design has a prediction accuracy of 90%.

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Section: Background Researchmentioning

confidence: 99%

Low-Power and Low-Cost Dedicated Bit-Serial Hardware Neural Network for Epileptic Seizure Prediction System

Kueh

Kázmierski

2018

IEEE J. Transl. Eng. Health Med.

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“…From these exponents, different features can be extracted, which are used for the classification [23]. In other studies, methods based on a nonlinear dynamics theory were used to extract features based on the fractal dimension, along with a SVM classifier [24].…”

Section: Signal Classification and Eeg Processingmentioning

confidence: 99%

Using genetic algorithms and k-nearest neighbour for automatic frequency band selection for signal classification

Rivero

Guo

Seoane

et al. 2012

IET Signal Process.

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The classification of signals is usually based on the extraction of various features that subsequently will be used as an input to a classifier. These features are extracted as a result of the experts' prior knowledge, which may often involve a lack of the information necessary for an accurate classification in all cases. This study proposes a new technique, in which a genetic algorithm is used to automatically extract frequency-domain features from a set of signals, with no need of prior knowledge. This allows, first, to achieve greater accuracy in the classification of signals, and, secondly, to discover new data on the signals to be classified. This system was used to solve a well-known problem: classification of electroencephalogram (EEG) signals, and its results show a better performance in comparison with other works on the same problem.

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“…Alternatively, other works within chaotic analysis uses fractal dimensions to analyze the signal. Then, those fractal features are used in combination with a SVM to provide the classification of the signal [14].…”

Section: Introductionmentioning

confidence: 99%

Classification of Two-channel Signals by Means of Genetic Programming

Rivero

Fernández-Blanco

Dorado

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Traditionally, signal classification is a process in which previous knowledge of the signals is needed. Human experts decide which features are extracted from the signals, and used as inputs to the classification system. This requirement can make significant unknown information of the signal be missed by the experts and not be included in the features. This paper proposes a new method that automatically analyses the signals and extracts the features without any human participation. Therefore, there is no need of previous knowledge about the signals to be classified. The proposed method is based on Genetic Programming and, in order to test this method, it has been applied to a well-known EEG database related to epilepsy, a disease suffered by millions of people. As the results section shows, high accuracies in classification are obtained.

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Automatic recognition of epileptic seizure in EEG via support vector machine and dimension fractal

Cited by 15 publications

References 20 publications

Low-Power and Low-Cost Dedicated Bit-Serial Hardware Neural Network for Epileptic Seizure Prediction System

Low-Power and Low-Cost Dedicated Bit-Serial Hardware Neural Network for Epileptic Seizure Prediction System

Using genetic algorithms and k-nearest neighbour for automatic frequency band selection for signal classification

Classification of Two-channel Signals by Means of Genetic Programming

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