Selecting Statistical Characteristics of Brain Signals to Detect Epileptic Seizures using Discrete Wavelet Transform and Perceptron Neural Network

Abbasi, Rezvan; Esmaeilpour, Mansour

doi:10.9781/ijimai.2017.456

Cited by 18 publications

(9 citation statements)

References 27 publications

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“…Abbasi and Esmaeilpour [ 32 ] The objective of this paper was improving the precision of prediction and classifying different states of EEG signals into healthy, convulsive, and epileptic states. In this approach, they divide the signal into 5 levels.…”

Section: Sectionmentioning

confidence: 99%

Various epileptic seizure detection techniques using biomedical signals: a review

Paul

2018

Brain Inf.

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Epilepsy is a chronic chaos of the central nervous system that influences individual’s daily life by putting it at risk due to repeated seizures. Epilepsy affects more than 2% people worldwide of which developing countries are affected worse. A seizure is a transient irregularity in the brain’s electrical activity that produces disturbing physical symptoms such as a lapse in attention and memory, a sensory illusion, etc. Approximately one out of every three patients have frequent seizures, despite treatment with multiple anti-epileptic drugs. According to a survey, population aged 65 or above in European Union is predicted to rise from 16.4% (2004) to 29.9% (2050) and also this tremendous increase in aged population is also predicted for other countries by 2050. In this paper, seizure detection techniques are classified as time, frequency, wavelet (time–frequency), empirical mode decomposition and rational function techniques. The aim of this review paper is to present state-of-the-art methods and ideas that will lead to valid future research direction in the field of seizure detection.

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Section: Sectionmentioning

confidence: 99%

Various epileptic seizure detection techniques using biomedical signals: a review

Paul

2018

Brain Inf.

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“…In interictal recordings, epileptic seizures are usually activated with photostimulation, hyperventilation, and other methods. However, the drawback is that the behavior of provoked epileptic seizures is not necessarily the same as natural ones [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…Hierarchical EEG classification system using best basis-based wavelet packet entropy method was proposed [ 14 ]. Abbasi and Esmaeilpour [ 4 ] proposed a study to choose statistical characteristics of brain signals for detection of epileptic seizures using DWT and perceptron neural network. Their study used University of Bonn database and DWT as a feature extraction method.…”

Section: Introductionmentioning

confidence: 99%

Automated epileptic seizures detection using multi-features and multilayer perceptron neural network

et al. 2018

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Detection of epileptic seizure activities from long-term multi-channel electroencephalogram (EEG) signals plays a significant role in the timely treatment of the patients with epilepsy. Visual identification of epileptic seizure in long-term EEG is cumbersome and tedious for neurologists, which might also lead to human error. Therefore, an automated tool for accurate detection of seizures in a long-term multi-channel EEG is essential for the clinical diagnosis. This study proposes an algorithm using multi-features and multilayer perceptron neural network (MLPNN) classifier. After appropriate approval from the ethical committee, recordings of EEG data were collected from the Institute of Neurosciences, Ramaiah Memorial College and Hospital, Bengaluru. Initially, preprocessing was performed to remove the power-line noise and motion artifacts. Four features, namely power spectral density (Yule–Walker), entropy (Shannon and Renyi), and Teager energy, were extracted. The Wilcoxon rank-sum test and descriptive analysis ensure the suitability of the proposed features for pattern classification. Single and multi-features were fed to the MLPNN classifier to evaluate the performance of the study. The simulation results showed sensitivity, specificity, and false detection rate of 97.1%, 97.8%, and 1 h−1, respectively, using multi-features. Further, the results indicate the proposed study is suitable for real-time seizure recognition from multi-channel EEG recording. The graphical user interface was developed in MATLAB to provide an automated biomarker for normal and epileptic EEG signals.

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“…A truly universal software solution would need to accurately model a user's tremor, recognize the patterns in the tremor to remove them, and function smoothly and continuously in time. Artificial neural networks have been shown to be effective at prediction, modeling, pattern matching tasks [12], and have been useful in time-series tasks [13][14] [15], making them a good choice for removing tremor-induced motion from standard computer mouse inputs. This paper describes the design of a multilayer artificial neural network that is capable of being trained by the backpropagation algorithm to remove the unwanted mouse cursor movements caused by essential tremors in an individual patient or a generalized group of patients.…”

Section: Introductionmentioning

confidence: 99%

Removing Unclassified Hand Tremor Motion from Computer Mouse Input with Neural Networks

Mack¹

2018

IJIMAI

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An artificial neural network based filter to remove unwanted tremor-induced motion in computer mouse input is presented and tested. A method to efficiently capture appropriate training data is shown to be important in the operation and training of the neural network filter. The architecture of the neural network as well as the numerous design choices are presented and explained. A simulation study proves the artificial neural network is successful at removing a simulated Parkinson's tremor from computer mouse movements even with minimal training data. Resulting tremor-free motion estimated by the artificial neural network is shown to be similar to normal tremor free computer mouse movements.

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Selecting Statistical Characteristics of Brain Signals to Detect Epileptic Seizures using Discrete Wavelet Transform and Perceptron Neural Network

Cited by 18 publications

References 27 publications

Various epileptic seizure detection techniques using biomedical signals: a review

Various epileptic seizure detection techniques using biomedical signals: a review

Automated epileptic seizures detection using multi-features and multilayer perceptron neural network

Removing Unclassified Hand Tremor Motion from Computer Mouse Input with Neural Networks

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