# An Epileptic Seizure Detection Technique Using EEG Signals with Mobile Application Development

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## Abstract

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## 1. Introduction

#### 1.1. Background

#### 1.2. Contribution and Paper Organization

## 2. Structure and Materials

#### 2.1. General Structure of Epilepsy

#### 2.2. Electroencephalograph (EEG)

## 3. State of the Art

## 4. Proposed Method and Procedures

#### 4.1. Input EEG Signal

#### 4.2. Filtering

#### 4.3. Decomposition

#### 4.4. Feature Extraction

#### 4.4.1. Amplitude Range

#### 4.4.2. Band Power

#### 4.4.3. Proposed Crest Range

#### 4.5. Machine Learning and Classification

## 5. Results

#### 5.1. Dataset

#### 5.2. Pre-Processing

#### 5.3. Feature Extraction

#### 5.4. Classification

#### 5.5. Performance

## 6. Mobile Application Development

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

ANN | Artificial Neural Network |

CEEMD | Complementary ensemble empirical mode decomposition |

CNN | Convolutional Neural Network |

DFA | Detrended Fluctuation Analysis |

DT | Detection Rate |

DWT | Discrete Wavelet Transform |

EEG | Electroencephalograph |

FFac | Form Factor |

FF-ANN | Feedforward Artificial Neural Network |

FFNN | Feedforward Neural Network |

FFT | Fast Fourier transform |

FInfo | Fisher information |

FIR | Finite Impulse Response |

FMQAS | fuzzy measure-theoretic quantum approximation of an abstract system |

GMM | Gaussian mixture model |

HComp | Hjorth Parameters: Complexity |

HFD | Higuchi Fractal Dimension |

HMob | Hjorth Parameters: Mobility |

HuExp | Hurst Exponent |

IQR | Hounsfield units |

KNN | k-Nearest Neighbor |

Kurt | Kurtosis |

LAD | Latent Dirichlet Allocation |

LMBPPN | Levenberg–Marquardt back propagation network |

LR | Layer-wise Relevance |

LS-SVM | Least Squares Support Vector Machines |

MAD | Median Absolute Deviation |

MAX | Maximum |

MFD | Mandelbrot Fractal Dimension |

MIN | Minimum |

NB | Naïve Bayes |

NN | Neural Network |

PAPR | Peak-to-Average Power Ratio |

PeEn | Permutation Entropy |

PFD | Petrosian Fractal Dimension |

PNN | Probabilistic Neural Network |

PSI_RIR | Power Spectral Intensity, and the relative intensity Ratio |

RBFNN | Radial Basis Function Neural Network |

RBF-SVM | Radial Basis Function Support Vector Machine |

Recurrent NN | Recurrent Neural Network |

RMS | Root Mean Square |

SampEn | Sample Entropy |

Sen | Spectral Entropy |

Skw | Skewness |

STDHR | standard deviation of the heart rate |

SVDEn | SVD Entropy |

SVM | Support Vector Machine |

TotVar | Total Variation |

Trough | Minimum value |

Var | Variance |

XGBoost | eXtreme Gradient Boosting |

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Authors | Signal | Dataset | Features | Classifiers | Results |
---|---|---|---|---|---|

Sinha et al., 2004 [28] | EEG | Collected from 5 patients | Spectral Power | FMQAS | Satisfactory |

Arabi et al., 2009 [29] | EEG | Collected from 21 patients | Entropy, Dominant frequency, Average amplitude, and Rhythmicity | PNN | 98.7% ACC |

Bao et al., 2008 [30] | EEG | Bonn University | Power spectral features (PSF), Petrosian and Higuchi fractal dimensions (PFD, HFD), and Hjorth parameters | PNN | 96.7% ACC |

Bezobrazova and Golovko, 2007 [31] | EEG | Collected from 6 patients | ANN | 96.7% ACC | |

Fani and Azmi, 2011 [32] | EEG | Bonn University | Mean of IF, Mean of Kaiser energy, and Energy | ANN | 94% ACC |

Sivasankari and Thanushkodi, 2008 [33] | EEG | Bonn University | Mean, Absolute value, and Variance | ANN | 93.23% ACC |

Juarez-Guerra et al., 2015 [34] | EEG | Bonn University | Mean, Absolute median, and Variance | FF-ANN | 93.23% ACC |

Kumar et al., 2008 [35] | EEG | Bonn University | Wavelet entropy and Spectral entropy | Recurrent NN | 96.3% ACC |

Kiranmaji and Udayashankara, 2013 [36] | EEG | JSS medical hospital, Mysuru, India | Maximum, Minimum, Mean, and Standard deviation | FFNN | 81.67 ACC |

Ghosh-Dastidar et al., 2008 [37] | EEG | Bonn University | Standard deviation, Correlation dimension, and Largest Lyapunov exponent | K-Means Clustering, Discrimination analysis, LMBPPN, and RBFNN | 96.7% ACC |

Dawood Dilber et al., 2016 [38] | EEG | PhysioNet | Mean, Standard deviation, Variance FFT, and Wavelet transform | K-Means and Discrimination analysis | 70% ACC, 93% ACC |

Mihandoos et al., 2011 [39] | EEG | Bonn University | Fourth moment of wavelet coefficient divided by the second moment, Max–Min, Zero-crossing of wavelet coefficient, Variance of wavelet coefficient, Mean of wavelet coefficient | KNN and Bayesian learning machine | 96.8% ACC, 98% ACC |

Kumari and Jose, 2011 [40] | EEG | Bonn University | Variance, Energy, and Power spectral density | SVM | 98% ACC |

Panda et al., 2010 [41] | EEG | Bonn University | Energy, Entropy, and Standard deviation | SVM | 91.2% ACC |

Liu et al., 2012 [42] | EEG | University Hospital of Freiburg, Germany | Relative entropy, Relative amplitude, Coefficient of variation, and Fluctuation index | RBF-SVM | 95.33% ACC |

Murugavel et al., 2011 [43] | EEG | Bonn University | Maximum, Minimum, Mean, and Standard deviation | PNN, RBFNN, MSVM | 94% ACC, 93% ACC, 96% ACC |

Schneider et al., 2009 [44] | EEG | Bonn University | Higuchi algorithm, Katz algorithm, and Sevcik algorithm | SVM | Higuchi algorithm has the higher accuracy |

Shen et al., 2011 [45] | EEG | National Taiwan University Hospital | Total variation, Standard Deviation, and Energy | SVM | 98.9% ACC |

Seng et al., 2012 [46] | EEG | Bonn University | Mean, Coefficient of variation, Dominant frequency, Mean of power spectrum, and Variance | SVM | 98% ACC |

Yuan, 2010 [47] | EEG | Collected from epileptic patients | Cao’s method | PNN and SVM | 96.3% ACC |

Hadj-Youcef et al., 2013 [48] | EEG | Bonn University | Maximum, Minimum, Range standard deviation, Energy, and Entropy | SVM | 98% ACC |

Rafiuddin et al., 2011 [49] | EEG | AMU University | Energy, Coefficient of variation, IQR, and MAD | LAD | 96.5% ACC |

Chua et al., 2008 [50] | EEG | Bonn University | Mean of spectral magnitude, Entropy, and Power spectrum | GMM | 93.11% ACC |

Kumar et al., 2014 [51] | EEG | Bonn University | Normalized bispectral (NB) entropy, NB squared entropy, and NB cubed entropy, Bispectrum phase entropy, Mean bispectrum magnitude, and Moment of bispectrum | PNN, KNN, DT, and SVM | 96% ACC, 96% ACC, 95% ACC, 98% ACC |

Vijith et al., 2016 [52] | EEG | Government Medical College Thiruvananthapuram, Kerala | Approximate entropy, Sample entropy, and Hurst exponent | SVM | 91% ACC |

Rashid et al., 2017 [53] | EEG | Bonn University | Mean, Median, Maximum, Minimum, Range, Standard deviation, Median absolute deviation, Mean absolute deviation, 12 Norm, and Max Norm | NN | 80% ACC |

Vandecasteele et al., 2017 [54] | ECG | Collected from patient at UZ Leuven Gasthuisberg | HR peak, HR base, and STDHR base | SVM | 70% Sen |

Wang et al., 2017 [55] | EEG | Bonn University | Mean, Variance, Coefficient of variation, Total variation, Maximum, Minimum | SVM, KNN, LDA, NB, and LR | 99.25% ACC |

Gu et al., 2018 [56] | EEG | Collected from patients | Mean powers, and Peak frequency | SVM | 100% Sen, 94.5% Sen |

Wu, 2020 [57] | EEG | Bonn University and CHB-MIT dataset | Time domain, Frequency domain, Time–frequency domain, and Entropy-based features | CEEMD + XGBoost | 99% ACC, 95.7% ACC |

Maria et al., 2020 [58] | EEG, EMG, PPG | CAP Sleep Database | DWT | ANN | 91.1% ACC |

Molla et al., 2020 [59] | EEG | Bonn University | SODP, Squared coefficient of variation of the absolute series, Fluctuation index, Permutation entropy, Approximate entropy, and Renyi’s entropy | FFNN | 99.5% ACC |

Abiyev et al., 2020 [60] | EEG | Bonn University | Features obtained from the convolutional layers of CNN | CNN | 96.67% ACC |

Zhang et al., 2018 [61] | EEG | American Clinical Neurophysiology Society | PFD, MFD, FInfo, HComp, HMob, DFA, HuExp, TotVar, FFac, PAPR, RMS, Peak, Kurt, Skw, Var, Trough, Crest, Mean, HFD, SampEn, PeEn, SVDEn, SEn, and PSI_RIR | SVM | 99.40% ACC |

Mansouri et al., 2019 [62] | EEG | Physionet CHB-MIT | Power in band of interest | Adaptive threshold, Distance network | 83% Sen |

Shabarinath et al., 2019 [63] | EEG | Bonn University | Power spectral density and Wavelet coefficients | SVM | 90.1% ACC |

Adda et al., 2020 [64] | EEG | Bonn University | Amplitude and Kolmogorov complexity | SVM | 97% ACC |

Abedin et al., 2019 [65] | EEG | Bonn University | Mean, Standard deviation, Median, Kurtosis, Skewness, Variance, Maximum, Minimum, and Root mean square | ANN | 97.33% ACC |

Gupta et al., 2020 [66] | EEG | Bonn University | Mean, Standard deviation, Root mean square, Skew, Kurtosis, Maximum, Coefficient of variation, and Shannon entropy | CNN | 99.29% ACC |

Prasanna et al., 2019 [67] | EEG | Karunya EEG database | Mean and Standard deviation | SVM | 95.8% ACC |

Karim et al., 2020 [68] | EEG | Bonn University | Mean instantaneous frequencies | LS-SVM | 97.66% ACC |

Percentage Using Maximum–Minimum Feature | Classifiers | ||
---|---|---|---|

ANN | SVM | KNN | |

80% Training and 20% Testing | 96.49% | 94.13% | 98.61% |

70% Training and 30% Testing | 96.18% | 94.57% | 97.7% |

60% Training and 40% Testing | 95.77% | 93.27% | 96.54% |

50% Training and 50% Testing | 94.95% | 93.18% | 95.06% |

Percentage Using Band-Power Feature | Classifiers | ||
---|---|---|---|

ANN | SVM | KNN | |

80% Training and 20% Testing | 90.97% | 89.89% | 92.92% |

70% Training and 30% Testing | 88.41% | 89.99% | 91.60% |

60% Training and 40% Testing | 89.01% | 88.27% | 90.42% |

50% Training and 50% Testing | 86.21% | 88.27% | 89.23% |

Percentage Using Crest-Range Feature | Classifiers | ||
---|---|---|---|

ANN | SVM | KNN | |

80% Training and 20% Testing | 98.03% | 95.34% | 97.92% |

70% Training and 30% Testing | 96.05% | 93.56% | 97.93% |

60% Training and 40% Testing | 95.17% | 94.73% | 96.54% |

50% Training and 50% Testing | 94.48% | 94.92% | 95.68% |

Percentage Using Amplitude-Range and Band-Power Features | Classifiers | ||
---|---|---|---|

ANN | SVM | KNN | |

80% Training and 20% Testing | 97.87% | 96.40% | 96.72% |

70% Training and 30% Testing | 95.91% | 95.97% | 95.63% |

60% Training and 40% Testing | 94.72% | 93.53% | 93.62% |

50% Training and 50% Testing | 94.40% | 93.21% | 93.23% |

Percentage Using Band-Power and Crest-Range Features | Classifiers | ||
---|---|---|---|

ANN | SVM | KNN | |

80% Training and 20% Testing | 96.66% | 97.06% | 96% |

70% Training and 30% Testing | 96.02% | 96.20% | 95.51% |

60% Training and 40% Testing | 95.07% | 94.64% | 93.70% |

50% Training and 50% Testing | 94.24% | 93.57% | 92.71% |

Percentage Using Amplitude-Range and Crest-Range Features | Classifiers | ||
---|---|---|---|

ANN | SVM | KNN | |

80% Training and 20% Testing | 97.25% | 96.94% | 98.27% |

70% Training and 30% Testing | 96.44% | 95.40% | 97.36% |

60% Training and 40% Testing | 94.75% | 94.25% | 96.23% |

50% Training and 50% Testing | 94.17% | 93.82% | 95.76% |

Percentage Using Three Combined Features | Classifiers | ||
---|---|---|---|

ANN | SVM | KNN | |

80% Training and 20% Testing | 98.43% | 97.92% | 98.10% |

70% Training and 30% Testing | 96.38% | 97.20% | 96.52% |

60% Training and 40% Testing | 96.20% | 96.55% | 95.68% |

50% Training and 50% Testing | 96.06% | 94.32% | 93.83% |

No | Title | Author | Features | Classifier | Accuracy Results |
---|---|---|---|---|---|

1 | Automatic epilepsy detection using the instantaneous frequency and sub-band energies of the EEG signals (2011) | Fani and Azemi [32] | frequency and the energies of the EEG signals in different sub-bands | Artificial neural network (ANN) | 94% |

2 | Neural network classifier for the detection of epilepsy (2013) | Kiranmayi and Udayashankara [36] | maximum value in the non-redundant region, minimum value in the non-redundant region, mean value of the non-redundant region, maximum value along the principal diagonal, minimum value along the principal diagonal, standard deviation along the principal diagonal | ANN | 81.67% |

3 | Early detection of epilepsy using EEG signals (2014) | Kumar and Ajitha [51] | bispectral entropy, bispectral squared entropy, bispectrum phase entropy, mean bispectrum magnitude, weighted center of bispectrum | PNN, KNN, DT, SVM | 96% 96% 95% 98% |

4 | Epileptic seizure detection in EEG signals using wavelet transforms and neural networks (2015) | Juarez-Guerra et al. [34] | mean, absolute, median, variance | Feed-forward neural network | 93.23% |

5 | EEG based detection of Epilepsy by a mixed design approach (2016) | Dilber and Kaur [38] | mean, standard deviation, variance, FFT, wavelet transform | Support-vector machine Discriminant Analysis technique | 70%/93% |

6 | Epileptic seizure detection using nonlinear analysis of EEG (2016) | Vijith et al. [52] | approximate entropy, sample entropy, hurst exponent | Support-vector machine | 89%/91% |

7 | Epileptic seizure classification using statistical features of EEG signal (2017) | Ahmad et al. [53] | mean, median, maximum, minimum, range, standard deviation, median absolute deviation, mean absolute deviation, l2 norm, max norm, | Neural Network (NN) | 80.0% 78.7% 80.0% 79.3% |

8 | Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection (2007) | Ghosh-Dastidar and Adeli [72] | standard deviation, correlation dimension, largest Lyapunov exponent | unsupervised K-means clustering, linear and quadratic discriminant analysis, radial basis function neural network, Levenberg–Marquardt backpropagation neural network | 96.7% Using (LMBPNN) |

9 | Seizure detection using wavelet transform and a new statistical feature (2011) | Mihandoost et al. [39] | fourth moment divided by second moment, difference between maximum and minimum, zero-crossing of the wavelet coefficients | K-nearest neighbors (KNN), Bayesian | 96.83% 98.17% |

10 | Seizure detection in EEG using time-frequency analysis and SVM (2011) | Kumari and Jose [40] | variance, energy, power spectral density (PSD) | Support-vector machine (SVM) | 98.75% |

11 | Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction (2010) | Panda et al. [41] | energy, entropy, standard deviation | Support-vector machine (SVM) | 91.2% |

12 | Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG (2012) | Liu [42] | relative energy, relative amplitude, coefficient of variation, fluctuation index | Support-vector machine (SVM) | 95.33% |

13 | Lyapunov features based EEG signal classification by multi-class SVM (2011) | Murugavel et al. [43] | maximum, minimum, mean, standard deviation | Multi-class Support-vector machine (MSVM), Probabilistic Neural Network (PNN), Radial Basis Function Neural Network (RBFNN) | 96%/94%/93% |

14 | Seizure detection in EEG signals using support vector machines (2012) | Seng et al. [46] | mean, variance, dominant frequency, mean power spectrum | Support-vector machine (SVM) | 98% |

15 | Detection of epilepsy during seizure-free periods (2013) | Hadj-Youcef et al. [48] | maximum, minimum, range, standard deviation | Support-vector machine (SVM) | 98% |

16 | Feature extraction and classification of EEG for automatic seizure detection (2011) | Rafiuddin [49] | inter-quartile range (IQR), median absolute deviation (MAD), energy of variation, coefficient of variation | Linear Discriminate Analysis (LAD) | 96.5% |

17 | Automatic Identification of epilepsy by HOS and power spectrum parameters using EEG signals: a comparative study (2008) | Chua et al. [50] | mean of spectral magnitude for PSD, mean of spectral magnitude for HOS, entropy | Gaussian mixture model (GMM) | - (PSD) 88.78% - (HOS) 93.11% |

18 | Proposed method: (2023) | Lasefr et al. | amplitude range, band power, crest range | ANN, SVM, KNN | 96% 95% 98% |

Application | Author | Method | Features |
---|---|---|---|

Android application for neonatal colonic seizures detection | Cattani et al. [74] | Movement of some body parts, video and image processing | A laptop required for processing. No real users. |

Android application | DeVaul et al. [75] | Fall detection | Low accuracy. |

Android application | Madansingh et al. [76] | Analyzing body movement using existing embedded sensors | No notification. |

Fitbit devices | Fang et al. [77] | SMS and GPS alerts | Lack the compatibility. High cost. |

Android application | Yavuz et al. [78] | Fall detection | GPS locations/SMS. |

Proposed android application | Lasefr et al. | EEG signal processing | High accuracy. Immediate notification. Development for real-time processing. |

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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Lasefr, Z.; Elleithy, K.; Reddy, R.R.; Abdelfattah, E.; Faezipour, M.
An Epileptic Seizure Detection Technique Using EEG Signals with Mobile Application Development. *Appl. Sci.* **2023**, *13*, 9571.
https://doi.org/10.3390/app13179571

**AMA Style**

Lasefr Z, Elleithy K, Reddy RR, Abdelfattah E, Faezipour M.
An Epileptic Seizure Detection Technique Using EEG Signals with Mobile Application Development. *Applied Sciences*. 2023; 13(17):9571.
https://doi.org/10.3390/app13179571

**Chicago/Turabian Style**

Lasefr, Zakareya, Khaled Elleithy, Ramasani Rakesh Reddy, Eman Abdelfattah, and Miad Faezipour.
2023. "An Epileptic Seizure Detection Technique Using EEG Signals with Mobile Application Development" *Applied Sciences* 13, no. 17: 9571.
https://doi.org/10.3390/app13179571