Epilepsy diagnosis using probability density functions of EEG signals

Orhan, Umut; Hekim, Mahmut; Özer, Mahmut; Provazník, Ivo

doi:10.1109/inista.2011.5946171

Cited by 17 publications

(8 citation statements)

References 19 publications

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“…The proposed method was repeated 100 times for each experiment, and the average of total correct classification (TCC) successes was taken for all experiments. TCC success is the combination of specificity and sensitivity parameters [19,27,28] and it is the proportion of the number of correctly classified decisions (TN + TP ) to the number of all cases (TN + FN + TP + FP ):…”

Section: Resultsmentioning

confidence: 99%

Mechanical fault detection in permanent magnet synchronous motors using equal width discretization-based probability distribution and a neural network model

Akar¹,

Hekim²,

Orhan³

2015

Turk J Elec Eng & Comp Sci

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This paper focuses on detecting the static eccentricity and bearing faults of a permanent magnet synchronous motor (PMSM) using probability distributions based on equal width discretization (EWD) and a multilayer perceptron neural network (MLPNN) model. In order to achieve this, the PMSM stator current values were measured in the cases of healthy, static eccentricity, and bearing faults for the conditions of three speeds and five loads. The data was discretized into several ranges through the EWD method, the probability distributions were computed according to the number of current values belonging to each range, and these distributions were then used as inputs to the MLPNN model.We conducted eighteen experiments to evaluate the performance of the proposed model in the detection of faults.The proposed method was very successful in full load and high speed for some experiments. As a result, we showed that the proposed model resulted in a satisfactory classification of accuracy rates.

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Section: Resultsmentioning

confidence: 99%

Mechanical fault detection in permanent magnet synchronous motors using equal width discretization-based probability distribution and a neural network model

Akar¹,

Hekim²,

Orhan³

2015

Turk J Elec Eng & Comp Sci

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“…DWT, a method commonly used to analyze EEGs, decomposes a signal into a set of sub-bands through successive high-pass and low-pass filtering of the time domain [4]. The sub-band decomposition using DWT, where filters h[n] and g[n] correspond to high-pass and low-pass filters respectively, is schematically shown in Figure 2.…”

Section: B Feature Extractionmentioning

confidence: 99%

“…The analysis of the large amounts of data generated using EEG recording systems [3] can, however, provide valuable information and save time. In addition, biomedical signals are highly subjective in the interpretation of the same record, so analysis of the EEGs using computers is very useful in diagnostics [4]. Furthermore, the characteristic of EEGs on epilepsy disease is the recurrent seizures that occur irregularly and unpredictably, therefore computer-based applications for seizure detection in EEG recordings are necessary [5].…”

Section: Introductionmentioning

confidence: 99%

“…Research on automatic seizure detection has advanced leading to several methods being studied and implemented [3][4][5][6][7][8][9][10][11][12]. In [3], the equal frequency discretization (EFD) based probability density was proposed for diagnosing epilepsy of EEGs.…”

Section: Introductionmentioning

confidence: 99%

“…In [3], the equal frequency discretization (EFD) based probability density was proposed for diagnosing epilepsy of EEGs. The Elman-Chaotic method was proposed in [4] to optimize risk level of epilepsy. Automatic epileptic seizure detection in EEG signals based on line length wavelet feature and neural network method were the main subject in [5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Principal component analysis-based neural network with fuzzy membership function for epileptic seizure detection

Fatichah

Iliyasu

Suciati

et al. 2014

2014 10th International Conference on Natural Computation (ICNC)

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A hybrid principal component analysis (PCA)-based neural network with fuzzy membership function (NEWFM) is proposed for epileptic seizure detection. By combining PCA and NEWFM, the proposed method improves the accuracy in epileptic seizure detection. The PCA is used for wavelet feature enhancement needed to eliminate the sensitivity of noise, electrode artifacts, or redundancy. NEWFM, a model of neural networks, is integrated to improve prediction results by updating weights of fuzzy membership functions. A dataset made up of 5 sets, each consisting 100 single EEGs segments, is employed to evaluate the proposed system's performance. Based on the experiments, the prediction results show an accuracy rate of 98.29% for epileptic seizure classification while in the best cases the accuracy reaches 99.5% for the 'normal' (Z-S) seizure classification task.

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Development of Patient Remote Monitoring System for Epilepsy Classification

Prabhakar¹,

Rajaguru²

2017

IFMBE Proceedings

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Epilepsy diagnosis using probability density functions of EEG signals

Cited by 17 publications

References 19 publications

Mechanical fault detection in permanent magnet synchronous motors using equal width discretization-based probability distribution and a neural network model

Mechanical fault detection in permanent magnet synchronous motors using equal width discretization-based probability distribution and a neural network model

Principal component analysis-based neural network with fuzzy membership function for epileptic seizure detection

Development of Patient Remote Monitoring System for Epilepsy Classification

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