# Statistical-Hypothesis-Aided Tests for Epilepsy Classification

^{1}

^{2}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

## 2. Related Work

#### 2.1. Nonlinear Feature-Based Epilepsy Seizure Detection

#### 2.2. Fourier-Based Epilepsy Seizure Detection

#### 2.3. Wavelet-Based Epilepsy Seizure Detection

## 3. Proposed Work

- Step 1: Signal preprocessing:The signal is initially preprocessed to remove the effects of any artifacts and noise in the data.
- Step 2: Probability Density Function (PDF) fitting:A PDF fitting method is performed.
- Step 3: Hypothesis tests:The resulting PDF function with the preprocessed data is used as the input to a set of statistical hypothesis tests.
- Step 4: Machine-learning methods:The output of the hypothesis tests forms a structured dataset that is employed in machine-learning algorithms, including feature selection and classification methods.

- The PDF fitting method, which is responsible for forming an associated normal distribution, is applied;
- The divergence between the input data and associated distribution is calculated using statistical (hypothesis) tests; and
- The machine-learning algorithm is employed for classification and detection purposes.

#### 3.1. Step 1: Signal Preprocessing

#### 3.2. Step 2: PDF Fitting

- A normal distribution is characterized by two parameters, the mean $\mu $ and standard deviation $\sigma $. Therefore, the fitting process for such distribution is relatively uncomplicated and does not consume a significant amount of processing time.
- A normal distribution is used with data that tend to take a central value. In EEG signals, it is assumed that signal values are centralized at a specific wave type. In other words, a normal distribution is used with data that tend to have equal positive and negative values from the central value, which is the case in EEG signals. Although this assumption might be weak, it is supported by two facts. First, the distribution itself is used to model the data and not the features. Second, the normality assumption is evaluated at each piece of sample data in the next step. Other distributions require harsh assumptions to be made, which cannot be risked in the proposed approach.

#### 3.3. Step 3: Hypothesis Tests

#### 3.4. Step 4: Machine-Learning Methods

#### 3.4.1. Dimensionality Reduction and Feature Selection

#### 3.4.2. Classification

## 4. Experiment Tests

#### 4.1. Dataset

#### 4.2. Normality Test

#### 4.3. Feature Selection

## 5. Results, Discussion, and Comparisons

#### Comparison

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Fisher, R.S.; van Emde Boas, W.; Blume, W.; Elger, C.; Genton, P.; Lee, P.; Jerome Engel, J. Epileptic Seizures and Epilepsy: Definitions Proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia
**2005**, 46, 470–472. [Google Scholar] [CrossRef] [PubMed] - WHO. Epilepsy: A Public Health Imperative: Summary. 2019. Available online: https://www.who.int/mental_health/neurology/epilepsy/report_2019/en/ (accessed on 1 July 2019).
- Van Mierlo, P.; Papadopoulou, M.; Carrette, E.; Boon, P.; Vandenberghe, S.; Vonck, K.; Marinazzo, D. Functional brain connectivity from EEG in epilepsy: Seizure prediction and epileptogenic focus localization. Prog. Neurobiol.
**2014**, 121, 19–35. [Google Scholar] [CrossRef] - Coito, A.; Genetti, M.; Pittau, F.; Iannotti, G.; Thomschewski, A.; Hller, Y.; Trinka, E.; Wiest, R.; Seeck, M.; Michel, C.; et al. Altered directed functional connectivity in temporal lobe epilepsy in the absence of interictal spikes: A high density EEG study. Epilepsia
**2016**. [Google Scholar] [CrossRef] [PubMed] - Acharya, U.R.; Chua, C.K.; Lim, T.C.; Dorithy; Suri, J.S. Automatic identification of epileptic EEG signals using nonlinear parameters. J. Mech. Med. Biol.
**2009**, 9, 539–553. [Google Scholar] [CrossRef] - Kannathal, N.; Choo, M.L.; Acharya, U.R.; Sadasivan, P.K. Entropies for Detection of Epilepsy in EEG. Comput. Methods Prog. Biomed.
**2005**, 80, 187–194. [Google Scholar] [CrossRef] [PubMed] - Acharya, U.R.; Molinari, F.; Sree, S.V.; Chattopadhyay, S.; Ng, K.H.; Suri, J.S. Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control.
**2012**, 7, 401–408. [Google Scholar] [CrossRef] - Yang, Z.; Wang, Y.; Gaoxiang, O. Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls. Sci. World J.
**2014**, 2014, 140863. [Google Scholar] [CrossRef] [PubMed] - Vijith, V.S.; Jacob, J.E.; Iype, T.; Gopakumar, K.; Yohannan, D.G. Epileptic seizure detection using non linear analysis of EEG. In Proceedings of the 2016 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 26–27 August 2016; pp. 1–6. [Google Scholar] [CrossRef]
- Thilagaraj, M.; Pallikonda Rajasekaran, M.; Arun Kumar, N. Tsallis entropy: As a new single feature with the least computation time for classification of epileptic seizures. Clust. Comput.
**2018**. [Google Scholar] [CrossRef] - Li, P.; Karmakar, C.; Yearwood, J.; Venkatesh, S.; Palaniswami, M.; Liu, C. Detection of epileptic seizure based on entropy analysis of short-term EEG. PLoS ONE
**2018**, 13, e0193691. [Google Scholar] [CrossRef] [PubMed] - Nijsen, T.M.; Cluitmans, P.J.; Griep, P.A.; Aarts, R.M. Short Time Fourier and Wavelet Transform for Accelerometric Detection of Myoclonic Seizures. In Proceedings of the 1st IEEE/EMBS Benelux Symposium, Brussels, Belgium, 7–8 December 2006. [Google Scholar]
- Krishnakumar, S.; Thanushkodi, K. An improved EEG signal classification using Neural Network with the consequence of ICA and STFT. J. Electr. Eng. Technol.
**2014**, 9, 1060–1071. [Google Scholar] [CrossRef] - Kovcs, P.; Samiee, K.; Gabbouj, M. On application of rational Discrete Short Time Fourier Transform in epileptic seizure classification. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, 4–9 May 2014; pp. 5839–5843. [Google Scholar] [CrossRef]
- Samiee, K.; Kovcs, P.; Gabbouj, M. Epileptic Seizure Classification of EEG Time-Series Using Rational Discrete Short–Time Fourier Transform. IEEE Trans. Biomed. Eng.
**2014**, 62, 541–552. [Google Scholar] [CrossRef] [PubMed] - Szuflitowska, B.; Orlowski, P. Comparison of the EEG Signal Classifiers LDA, NBC and GNBC Based on Time-Frequency Features. Pomiary Autom. Robot.
**2017**, 21, 39–45. [Google Scholar] [CrossRef] - Übeyli, E.D.; Cvetkovic, D.; Holland, G.; Cosic, I. Adaptive Neuro-fuzzy Inference System Employing Wavelet Coefficients for Detection of Alterations in Sleep EEG Activity During Hypopnoea Episodes. Digit. Signal Process.
**2010**, 20, 678–691. [Google Scholar] [CrossRef] - Sadati, N.; Mohseni, H.; Maghsoudi, A. Epileptic Seizure Detection Using Neural Fuzzy Networks. In Proceedings of the 2006 IEEE International Conference on Fuzzy Systems, Vancouver, BC, Canada, 16–21 July 2006; pp. 596–600. [Google Scholar] [CrossRef]
- Jahankhani, P.; Kodogiannis, V.; Revett, K. EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks. In Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA’06), Sofia, Bulgaria, 3–6 October 2006; pp. 120–124. [Google Scholar] [CrossRef]
- Subasi, A. Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction. Comput. Biol. Med.
**2007**, 37, 22–44. [Google Scholar] [CrossRef] [PubMed] - Subasi, A. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst. Appl.
**2007**, 32, 1084–1093. [Google Scholar] [CrossRef] - Costa, R.P.; Oliveira, P.; Rodrigues, G.; Leitao, B.; Dourado, A. Epileptic Seizure Classification Using Neural Networks with 14 Features. In Proceedings of the Knowledge-Based Intelligent Information and Engineering Systems, Zagreb, Croatia, 3–5 September 2008; Springer: Berlin/Heidelberg, Germany, 2008; pp. 281–288. [Google Scholar]
- Guo, L.; Rivero, D.; Dorado, J.; Rabual, J.; Pazos, A. Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J. Neurosci. Methods
**2010**, 191, 101–109. [Google Scholar] [CrossRef] [PubMed] - Orhan, U.; Hekim, M.; Ozer, M. EEG signals classification using the K means clustering and a multilayer perceptron neural network model. Expert Syst. Appl.
**2011**, 38, 13475–13481. [Google Scholar] [CrossRef] - Khan, Y.; Farooq, O.; Sharma, P. Automatic Detection of Seizure ONSET in Pediatric EEG. IJESA
**2012**, 2, 81–89. [Google Scholar] [CrossRef] - Rioul, O.; Vetterli, M. Wavelets and signal processing. IEEE Signal Process. Mag.
**1991**, 8, 14–38. [Google Scholar] [CrossRef] - Winterhalder, M.; Maiwald, T.; Voss, H.; Aschenbrenner-Scheibe, R.; Timmer, J.; Schulze-Bonhage, A. The seizure prediction characteristic: A general framework to assess and compare seizure prediction methods. Epilepsy Behav. EB
**2003**, 4, 318–325. [Google Scholar] [CrossRef] - MATLAB; Version 8.5 (R2015a); The MathWorks Inc.: Natick, MA, USA, 2015.

**Figure 3.**Band-pass filter [26].

**Figure 4.**Accuracy comparison between classification algorithms for the detection task before and after the PCA.

**Figure 5.**Accuracy comparison between classification algorithms for the classification task before and after the PCA.

**Figure 6.**Time comparison between the proposed feature extraction method versus state-of-the-art feature extraction methods.

Test | Parameters |
---|---|

Chi-Square | h, p, degree of freedom |

Durbin–Watson | h, p |

Run test | h, p, R, ${n}^{+}$, ${n}^{-}$, Std(R) |

Kruskal–Wallis | h, p, ${S}_{1}$, ${S}_{2}$, ${S}_{total}$, $D{f}_{1}$, $D{f}_{2}$, $MS{E}_{1}$, $MS{E}_{2}$ |

z test | h, p, upper confidence, lower confidence, z value |

No. | Algorithm | Type |
---|---|---|

1 | Logistic model tree (LMT) | Decision tree |

2 | J48 | Decision tree |

3 | Random forest | Decision tree |

4 | KNN | Instance-based |

5 | SVM | Support vector machine |

6 | Naive Bayesian | Probability-based |

8 | Feed-forward NN | Neural network |

**Table 3.**Statistics of parameter values with highlighted features not considered in the classification and detection step.

No. | Features | Value Range | Min | Max | Mean | Std |
---|---|---|---|---|---|---|

Chi Square | ||||||

1 | h | $0/1$ | 0 | 0 | 0 | 0 |

2 | p | $[0$–$1]$ | 0.994 | 0.994 | 0.994 | 0 |

3 | freedom | $[1$–$\infty )$ | 4096 | 4096 | 4096 | 0 |

Durbin–Watson | ||||||

4 | h | $0/1$ | 0 | 0 | 0 | 0 |

5 | p | $[0$–$1]$ | 0.003 | 0.366 | 0.093 | 0.064 |

Run–Test | ||||||

6 | h | $0/1$ | 1 | 1 | 1 | 0 |

7 | p | $[0$–$1]$ | 0 | 0 | 0 | 0 |

8 | R | $[0$–$\infty )$ | 63 | 876 | 38,532 | 142,716 |

9 | ${n}^{+}$ | $[1$–$\infty )$ | 1292 | 2718 | 2,058,052 | 174,186 |

10 | ${n}^{+}$ | $[1$–$\infty )$ | 1379 | 2805 | 2,038,948 | 174,186 |

11 | Std(R) | $(\infty $–$\infty )$ | −61,757 | −36,656 | −51,883 | 4,438 |

Kruskal–Wallis | ||||||

12 | h | 0/1 | 0 | 0 | 0 | 0 |

13 | p | $[0$–$1]$ | 0 | 1 | 0.48 | 0.314 |

14 | ${S}_{1}$ | $[1$–$\infty )$ | 677 | 4096 | 2,418,56 | 1,089,515 |

15 | ${S}_{2}$ | $[1$–$\infty )$ | 1,119,929 | 1,755,959 | 1,409,027 | 29,217 |

16 | ${S}_{total}$ | $[1$–$\infty )$ | 789,905,055 | 5,730,814,242 | 3,396,122,003 | 151,838,272 |

17 | $D{f}_{1}$ | $[1$–$\infty )$ | 564,712 | 4096 | 2,427,396 | 1,085,198 |

18 | $D{f}_{2}$ | $[1$–$\infty )$ | 0 | 1 | 0.48 | 0.314 |

19 | $MS{E}_{1}$ | $[1$–$\infty )$ | 0 | 4,939,480,042 | 2,334,411,002 | 151,816,665 |

20 | $MS{E}_{2}$ | $[1$–$\infty )$ | 57,290,636,765 | 57,308,142,425 | 5,730,533,006,017 | 284,775,365 |

z-test | ||||||

21 | h | 0/1 | 0 | 0 | 0 | 0 |

22 | p | $[0$–$1]$ | 1 | 1 | 1 | 0 |

23 | upper confidence | $(\infty $–$\infty )$ | −77,813 | 56,191 | −10,936 | 26,044 |

24 | lower confidence | $(\infty $–$\infty )$ | −75,751 | 67,962 | −457 | 26,973 |

25 | z-value | $(\infty $–$\infty )$ | 0 | 0 | 0 | 0 |

No. | Min | Max | Mean | Std |
---|---|---|---|---|

1 | −3.849 | 4.939 | 0 | 2.202 |

2 | −5.213 | 4.449 | 0 | 1.805 |

3 | −4.37 | 4.974 | 0 | 1.524 |

4 | −5.738 | 5.254 | 0 | 1.443 |

5 | −4.145 | 4.592 | 0 | 1.359 |

**Table 5.**Comparison between the proposed approach and state-of-the-art methods for the detection task.

Ref. | Features | Classifier | Data | Classes | Acc. |
---|---|---|---|---|---|

Proposed | Hypothesis Features | Random Forest | Bonn | Ictal vs. others | 98% |

Guo et al. [23] | Wavelet and line-length | ANN | Bonn | Ictal vs. others | 97.77% |

**Table 6.**Comparison between the proposed approach and state-of-the-art methods for the classification task.

Ref. | Features | Classification | Dataset | Results |
---|---|---|---|---|

S. Vijith et al. [9] | Approximate entropy | SVM | Bonn | 89%, 91% |

The proposed approach | Hypothesis Features | Random Forest | Bonn | 94.0% |

Orhan et al. [24] | Wavelet coefficients | ANN | Bonn | 95.6% |

Acharya et al. [5] | Non–linear | SVM and GMM | Bonn | 96.1% |

Krishnakumar and Thanushkodi [13] | STFT and non-linear | ANN | Bonn | 96.2% |

Acharya et al. [7] | Non-linear | SVM, KNN, FC, ANN, DT, GMM, and NBC | Bonn | 98.1% |

© 2019 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 (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Alqatawneh, A.; Alhalaseh, R.; Hassanat, A.; Abbadi, M.
Statistical-Hypothesis-Aided Tests for Epilepsy Classification. *Computers* **2019**, *8*, 84.
https://doi.org/10.3390/computers8040084

**AMA Style**

Alqatawneh A, Alhalaseh R, Hassanat A, Abbadi M.
Statistical-Hypothesis-Aided Tests for Epilepsy Classification. *Computers*. 2019; 8(4):84.
https://doi.org/10.3390/computers8040084

**Chicago/Turabian Style**

Alqatawneh, Alaa, Rania Alhalaseh, Ahmad Hassanat, and Mohammad Abbadi.
2019. "Statistical-Hypothesis-Aided Tests for Epilepsy Classification" *Computers* 8, no. 4: 84.
https://doi.org/10.3390/computers8040084