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Article

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

1
Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA
2
School of Computer Science & Engineering, Sacred Heart University, Fairfield, CT 06825, USA
3
School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(17), 9571; https://doi.org/10.3390/app13179571
Submission received: 18 June 2023 / Revised: 21 August 2023 / Accepted: 22 August 2023 / Published: 24 August 2023
(This article belongs to the Section Computing and Artificial Intelligence)

Abstract

:
Epileptic seizure detection classification distinguishes between epileptic and non-epileptic signals and is an important step that can aid doctors in diagnosing and treating epileptic seizures. In this paper, we studied the existing epileptic seizure detection methods in terms of challenges and processes developed based on electroencephalograph (EEG) signals. To identify the research deficiencies and provide a feasible solution, we surveyed the existing techniques at each phase, including signal acquisition, pre-processing, feature extraction, and classification. Most previous and current research efforts have used traditional features and decomposing techniques. Therefore, in this paper, we introduced an enhanced and efficient epileptic seizure technique using EEG signals, for which we also developed a mobile application for monitoring the classification of EEG signals. The application triggers notifications to all associated users and sends a visual notification should an EEG signal be classified as epileptic. In this research, we have used publicly available EEG data from the University of Bonn. Our proposed method achieved an average accuracy of 98% by utilizing different machine-learning algorithms for classification, and it has outperformed recently published studies. Though there have been other mobile applications for epileptic seizure detection, they have been based on motion and falling detection, as opposed to ours, which was developed based on EEG classification. Our proposed method will have an impact in the medical field, particularly for epilepsy seizure monitoring as well as in the Human–Computer Interaction fields, majorly in the Brain–Computer Interaction (BCI) applications.

1. Introduction

1.1. Background

Epilepsy is a brain disorder that leads to seizures, which refers to abnormal or excessive neural activities in the brain [1,2]. Epilepsy is a serious neurological disease that is referred to as a disorder of the central nervous system and is characterized by the loss of consciousness and convulsions [3,4]. Epilepsy is one of the most known neurological disorders that affect young children and older adults between the ages of 65 and 70 years old. More than 1% of the world’s population has been diagnosed with epilepsy [5,6]. Most epileptic cases can be controlled by anti-epileptic medication or surgery, but a minority of epilepsy patients do not respond to typical therapeutic treatments and surgical interventions [7,8,9]. Major causes of epilepsy include head injuries, genetic and infectious illnesses, maldevelopment of the brain, brain tumors, stroke, metabolic problems, and lead poisoning, though, for some patients, the cause of their epilepsy is unknown [7]. According to a survey, at least 65 million people worldwide live with epilepsy, and each year, approximately 2.4 million people are diagnosed with epilepsy, mainly in developing countries; 150,000 of these new epileptic patients are in the United States [3]. According to studies, one out of twenty-six people worldwide are affected by epilepsy [10]. Therefore, developing an automated technique that can be implemented on a smart device would be of great importance in detecting and analyzing epileptic seizures as well as other epilepsy-related conditions.
There are several anti-epileptic medications on the market that help control the incidence of seizures but cannot cure the disease. In addition, there are side effects associated with these medications, such as tiredness, headaches, reoccurring thoughts, memory loss, and daydreaming. Surgical intervention is another approach for epilepsy treatment; however, there are significant risks involved as well as critical factors to consider, such as being able to identify the focus of the seizure so that the area of the brain involved can be safely removed without resulting in a significant deficit [10].
There are many ways to measure the activity of the human brain, and the methods can be intrusive or non-intrusive. While some techniques study the behavior of the patient, others measure the signals of the eyes, eyelid movements, the face, and even polysomnographic (PSG) recordings. However, epileptic seizure detection has typically been conducted based on physiological recordings, which include electroencephalograms (EEG), electrooculograms (EOG), electromyograms (EMG), and electrocardiograms (ECG) [11,12].
The recording of seizures and spikes is of primary importance in the evaluation of epileptic patients. Since epileptic seizure detection by PSG requires the patient to attach electrodes to their scalp and remain in the hospital for several hours to record the signals, the process is complex as well as expensive [11].
Considering these challenges, an automatic detection method for epileptic seizures would be of great assistance during the long-term monitoring of epileptic patients. In addition, it could reduce the complexity and improve classification accuracy in epileptic seizure detection. Furthermore, it could also improve the diagnosis and disease management of brain disorders. According to a survey, EEGs are a critical tool for research and diagnosis, especially in detecting epileptic seizures and brain disorder analysis. EEGs are non-invasive and do not involve X-rays, radiation, or injections. EEGs have been used in detecting and analyzing brain signals for many years and are considered safe [6]. The electrodes record EEG signals from the brain without producing any sensation. Minimal side effects include slight redness/skin irritation at the contact site of the electrode, but this typically wears off in a few hours [12]. As a result, EEGs have been widely used as a clinical tool to measure brain activity [13]. In this paper, we have not only devised methods that outperform many existing techniques in epileptic seizure detection from EEG signal analysis but also implemented the ideas as a mobile development application and a functional framework.
The computer-based monitoring systems can be effectively enhanced by utilizing the application of Internet of Things (IoT) technology [14]. Jozsef Katona et al. developed a technique in 2019 in the field of BCI. They were able to develop and control a wireless robot utilizing an EEG signal that was recorded directly from the brain via a MindWave EEG headset in relation to attention. The EEG went through pre-processing, feature extraction and classification, which was then communicated to the App using TCP/IP. Feedback was provided from the robot to the BCI App [15]. In 2020, Attila Kovari developed a BCI method proving that a relationship can be found between problem-solving requiring algorithmic thinking and executive function. The Hanoi-tower test and Flowchart-based debugging test were used, respectively. Based on the method results, a positive correlation was indicated between the level of Executive Functions and Algorithmic Problem-solving [16]. Christina Costescu et al. utilized a GP3 eye tracker to conduct research in the field of BCI in 2019 on school-age children to assess visual attention using the OpenGazeAndMouseAnalyzer (OGAMA) application. They proposed a technology-based paradigm to accurately measure psychological outcomes [17]. Tibor Ujbanyi et al. studied and tested eye-hand coordination in relation to the movement of a computer mouse cursor on the screen. The research was tested using an eye tracker and a leap motion controller in 2021 [18]. Their results are useful for developing future HCI research. In 2020, Jozsef Katona et al. were able to test an HCI method based on eye-tracking that involved the determination of the outcome of two different queries where various programming syntaxes were compared by eye parameters [19].
Due to the large amount of data recorded by EEGs, visual analysis of the data has become challenging. Moreover, epilepsy diagnosis by EEG signals requires a human evaluator and is laborious and expensive, as it requires long hours of expert analysis, which means it can be prone to human error. Therefore, many studies have focused on developing an automated, computerized model for the analysis of EEG signals [1,13].
Previously proposed methods typically perform feature extraction of the EEG signal for analysis. These features include time, frequency, and the combination of both time and frequency. Classification can then be performed utilizing a variety of widely used machine-learning algorithms such as artificial neural networks (ANNs), K-nearest neighbor, and Support-vector machines (SVMs) to distinguish between epileptic and non-epileptic EEG signals. Despite the many epileptic seizure detection techniques, achieving high accuracy, specificity, and sensitivity has always been a challenge. The complexity of most theoretical techniques has resulted in poor practical, real-time implementation. In addition, developing a method of epileptic seizure detection that can be used daily has also been a challenge. The development and implementation of a wearable device for detecting epileptic seizures based on EEG signals would be a valuable contribution to the research community [20].

1.2. Contribution and Paper Organization

In this article, we have summarized epileptic seizure detection techniques in the literature that have relied on EEG signals. We have provided an overview of the required processing factors involved in epileptic seizure detection, which includes data acquisition, pre-processing, decomposing, feature extraction, feature selection, and classification. The purpose of this overview was to provide the epilepsy research community with guidelines for the different methods that have been used for epileptic seizure detection, which may also aid in selecting the most appropriate technique for their research. Furthermore, this study focused on developing an efficient, automated epileptic seizure detection technique using EEG signals that could be easily implemented via hardware or a mobile application. In this approach, we used filter design to accommodate our technique, applied an enhanced decomposition method, and developed a new combined feature for classification and analysis. The finite impulse response (FIR) filter was designed for pre-processing; the Butterworth filter was applied and then followed by a fast Fourier transform (FFT) as the final step of filtering. Then, we applied an enhanced wavelet analysis decomposition technique using an appropriate filter for each decomposed level based on the study of each decomposed signal. The next step was feature extraction, and several features were utilized for further EEG signal processing, one of which was a new combined feature, crest range. After that, a seizure detection classification was performed using the most used machine-learning algorithms, such as neural networks (NNs), K-nearest neighbors (KNN), and multiclass Support-vector machines (SVMs). The average accuracy of 98.61% was achieved by the proposed method when KNN was applied. Finally, we developed and tested a mobile application that monitored the epilepsy classification process and provided notifications based on the results. Our research would add value to existing and future research in the medical field in regard to epilepsy seizure monitoring. Furthermore, it will have an impact on the Human–Computer Interaction field, especially in the field of Brain–Computer Interaction (BCI).
This paper is structured as follows: General structure and material summary of epilepsy and EEG signals are provided in Section 2. Section 3 describes the literature review. The proposed method is explained in Section 4. Section 5 provides a variety of techniques utilized in epileptic seizure detection. Section 6 details the results and experiments. Mobile application development is presented in Section 6. Lastly, the conclusion of this work is provided in Section 7.

2. Structure and Materials

2.1. General Structure of Epilepsy

Normally, the process of epileptic seizure detection begins by recording the patient’s EEG signals. The recording is pre-processed using different techniques such as sampling, filtering, and artifact removal. Once the signal has been pre-processed, it undergoes feature extraction to obtain important information for classification. The final stage is classification, where the signal will be analyzed by machine-learning methods to determine whether the signal is epileptic. The process is shown in Figure 1 [20].

2.2. Electroencephalograph (EEG)

An electroencephalograph (EEG) is a clinical tool that is used to image the brain while it is performing a cognitive task [21,22,23]. EEGs record the electrical activity of the brain via electrodes placed on the scalp [24]. EEGs measure the voltage fluctuations that result from the ionic flow in brain neurons over a short period of time, which could be between 20–40 min. The EEG is one of the main methods for detecting epilepsy when the abnormalities of epileptic activity are clear in the EEG recording [25,26,27], and it is an important tool for research and diagnosis, especially when the use of millisecond-range temporal resolution is required. The range of EEG signal frequencies is very wide. However, we reduced the range to 0.5–30 Hz for physiological and clinical interests [13]. This simplified the detection of the location and the magnitude of the brain activity.

3. State of the Art

Table 1 provides a summary of reported techniques in literature between 2004 and 2020. Newer techniques are presented in other tables.

4. Proposed Method and Procedures

We proposed an enhanced, efficient automated technique for epileptic seizure detection using EEG signals that could be implemented using existing hardware to assist physicians in monitoring epilepsy patients. We also introduced the development of a mobile application for monitoring epileptic seizure activities based on the classification of EEG signals. Our proposed method, as shown in Figure 2, begins with data acquisition, signal pre-processing and filtering, signal decomposition, feature extraction including one newly designed feature, and classification using some of the well-known machine-learning techniques to determine whether the EEG signal was epileptic or non-epileptic. Furthermore, in the post-processing step, we developed a mobile application for monitoring epileptic seizures based on the result of the classification.
The proposed method for epileptic seizure detection was a fully automated technique based on EEG signal classification.

4.1. Input EEG Signal

The EEG signals used in this research were acquired from a well-known dataset available from the University of Bonn, Germany [55]. This dataset consisted of five subsets. Subsets A and B both contained normal EEG signals from healthy patients with their eyes open and closed, respectively. Subsets C and D contained seizure-free EEG signals from epileptic patients, and E contained the EEG signals of epileptic patients during seizure occurrence. Each set consisted of 100 signals. Each EEG signal consisted of 4097 samples, which were sampled at a frequency of 173.61 Hz. Each signal had a time duration of 23.6 s. Figure 3 and Figure 4 illustrate normal and epileptic EEG signals, respectively. Figure 5 illustrates seizure-free epileptic signal.

4.2. Filtering

For pre-processing EEG signals, we first utilized a finite impulse response (FIR) filter using Kaiser windows. Then, to reduce the ripples in the signal, we passed the output signal from a Butterworth filter. We used a low-pass FIR filter that accepted the default parameters with a cutoff frequency of 60 Hz and a sampling frequency of 178.6 Hz. We declared the pass-band frequency at 0.25, the stop-band frequency at 0.3, and the stop-band attenuation at 50. We then applied a Butterworth band-pass filter to the signal to obtain a maximum flat pass band for the decomposition stage.
w [ n ] = I ( β 1 n α α 2 ) I β   0 n M , 0   elsewhere
where α = M/2, M is the length of the signal, β is the shape parameter that will smooth the window, n is the window length, and I is the zeroth-order Bessel function:
I = i = 0 x [ ( x 2 ) k k ! ]
Initially, we declared the value of the stop-band ripple, pass-band ripple, pass band, and stop-band coefficients to calculate the order of the filter and then calculated the filter coefficients using these filter parameters. In the final pre-processing stage, fast Fourier transform (FFT) was employed to perform signal overlap. Filter parameters such as FFT size and block length, as well as filter coefficients, were predefined to improve spectral efficiency [69].

4.3. Decomposition

In this stage, we utilized wavelet analysis to decompose the filtered EEG signal into N levels based on the frequency composition of the signal. We tested different transforms for decomposition. However, they failed to provide better results than wavelets. Furthermore, by using wavelet decomposition, we retrieved more details from the signal. We applied a five-level decomposition on the EEG signal using a different filter at each level of decomposition, as shown in Figure 6. Furthermore, our technique applied different filters at each stage of decomposition, rather than using the same filter, based on our analysis of the decomposed signal using a spectrum analyzer. After applying these filters to the signals, we observed that they had more identifiers and more indicators. Moreover, we achieved better classification accuracy when using different filters in the decomposition stages [69].
As shown in Figure 7, representing the decomposed EEG normal signal, and Figure 8, representing the decomposed EEG epileptic signal, as well as the power spectrums of Figure 9 and Figure 10, the frequency responses showed that the frequencies from 0 to 133 Hz were impacting the signal at a high rate [11]. This range of the signal was suitable as it could discriminate between epileptic and normal EEGs.

4.4. Feature Extraction

We presented three features in order to measure the characteristic parameters of the filtered EEG signal, one of which was our designed combined feature. In this study, feature extraction was carefully studied. Both time- and frequency-domain features were extracted and used for classification. However, we selected three non-traditional time-domain features with respect to our analysis of the EEG signal [20,69].

4.4.1. Amplitude Range

To calculate the amplitude range, first, the maximum and minimum values were found, along with their indices. Then, the amplitude range was defined as the difference between the maximum and the minimum values in the EEG signal.
R = max (signal) − min (signal)

4.4.2. Band Power

Band power is defined as the average power of the given signal within a band of frequencies:
P b = 1 / ( 133 0 )   ×   ( lim T 1 2 π 0 133 | x t | 2   d t )
where x(t) is the given signal, and the band is from 0 to 133.

4.4.3. Proposed Crest Range

The crest range is a combination of the crest factor and the amplitude range. We attempted to combine several features to reach the best possible accuracy. The crest range provided outstanding performance and obtained the best accuracy results among all other features.
The crest range feature is based on the calculation of peak to peak of the signal. Then, we calculate the root mean square value of the signal so that we can calculate the crest factor.
crest   factor = peck   to   peck   of   the   signal RMS   value   of   the   signal
RMS   value   of   the   signal = 1 T 0 133 x 2 ( t ) d t
Finally, to calculate the crest range, we add the crest factor to the amplitude range of the EEG signal.
crest range = Crest Factor + Amplitude Range

4.5. Machine Learning and Classification

In the classification step, we applied three of the well-known classifiers to evaluate the classification performance of epileptic or non-epileptic signals for epileptic seizure detection using EEG signals. SVM, KNN, and ANN classifiers were used in this study. For the SVM, we used a one-versus-all multiclass SVM combined with a linear kernel function. For KNN, the Mahalanobis distance metric was applied with 1, 2, 3, 4, and 5 k neighbors, and we achieved the best results when k was equal to 5. Finally, the ANN classification using a feed-forward neural network with two layers and one hidden layer that had ten neurons was also applied. In the output layer of ANN, the number of neurons was equal to the number of classes. After the careful random division of data, experiments with different training and testing percentages of 50% and 50%, 60% and 40%, 70% and 30%, and 80% and 20%, respectively, were performed. Each percentage was trained and tested randomly ten times with each classifier, and then the average accuracy was recorded.

5. Results

5.1. Dataset

Our proposed method was developed based on EEG data that were publicly available from the University of Bonn, Germany (http://epileptologie-bonn.de/cms/front_content.php?idcat=193&lang=3) accessed on 9 January 2021.

5.2. Pre-Processing

Three different filters were utilized in the pre-processing stage to eliminate any artifacts. The signal was then decomposed using enhanced wavelet analysis, as explained previously.

5.3. Feature Extraction

Ten features were extracted from the EEG data. However, the features were reduced, and three features, including amplitude range, band power, and our proposed feature crest range, were selected for classification.

5.4. Classification

Some classifiers performed better than others, and we tested many classifiers to compare their performances. However, the researchers in [70] concluded that there was no singular “best” machine-learning classifier, as one that performed well in a specific research environment might perform poorly in another. Therefore, we established our classifiers’ performances based on their accuracy, sensitivity, and specificity.

5.5. Performance

As shown in Table 2, Table 3, Table 4, Table 5, Table 6, Table 7 and Table 8, as a combination of more discriminative features were used, the performance accuracy improved.
When a system or an instrument completes the measurement that it was developed to carry out with the required accuracy, the validity of the system can be confirmed, whereas reliability can be obtained when the system sustains reproducibility of values when repeated tests are applied [71]. According to the study in [71], and by adapting the recommendations for reliability to measures of validity, validity is rated as good (<5%), moderate (5–10%), or poor (>10%) based on error rate. Based on our eight tables of classification results, we can see that at least two out of the three classifiers meet good validity at each level of testing, except for Table 3, where the validity would be poor–moderate. Therefore, our system’s outcome relay on the classification of selected features, which all have good validity results.
Figure 11, Figure 12 and Figure 13 illustrate the performance of all three classifiers and the combination of different features used, demonstrating the overall accuracy that was achieved by the three classifiers at different percentage levels.
Table 9 shows a comparison between our method and several other methods using the same EEG dataset, with similar as well as different techniques, for epileptic seizure detection. As evident in Table 9, our proposed method achieved a high accuracy rate, as compared to previous research. Furthermore, the filter design, the decomposition technique, and the new features of our proposed method provided significant improvements, as shown by our results.

6. Mobile Application Development

We developed an Android-based mobile application that could monitor the behavior of an epileptic user. Our mobile application operated based on the processing and classification of the EEG signal and provided features such as sending an immediate notification once an epileptic seizure was detected. Furthermore, the application displayed the EEG spectrum of the user’s state and provided an alarm notification sent to the user as well as to family members and caretakers during the occurrence of an epileptic seizure. The application was connected to the EEG classification process via a wireless internet connection and displayed instant results. We tested the application using real-time EEG signals, as collecting epileptic signals would require clinical trials, which was beyond the scope of this paper.
The application development consisted of three modules, namely a login/registration, EEG signal, and visual and alarm notification. As shown in Figure 14 and Figure 15, the communication between the application and the server was established using socket events, and the three components were notified through an “HTTP POST” request notification to a DigitalOcean (DO) server. Then, the HTTP POST response interface was utilized by the DO server to obtain the notification from both the client and the device, as well as to relay the HTTP GET request between the application and the server [73].
Once a connection was established between the application and the server, the client was required to register and log in to the application. Then, the application informed the server that it was ready for an update event. If an epileptic signal was detected, the server would immediately send an update event to the application. After that, the application would send an HTTP GET request to the server. Furthermore, an alarm notification would be triggered as soon as an epileptic signal had been detected. Therefore, once the classification was complete, the application would receive a notification from the server and show the results on a smart device. It would also trigger an alarm.
The application required the user to initially register and then activate their account using a verification link. This enabled immediate notification and alarms to be sent to all registered clients via email ID notification. The login/registration screen is shown in Figure 16.
During the registration process, the application would track users who had requested to be notified by an alarm when an epileptic seizure had been detected. The alarm notification is shown in Figure 17.
There has not been a significant amount of published research that has involved the use of a mobile application for epileptic seizure detection. Therefore, we compared our proposed application to the most relevant methods available. As shown in Table 10, our proposed method outperformed the existing methods due to its use of EEG signals as well as the features that it provided.

7. Conclusions

In this study, we developed an efficient method for epileptic seizure detection that could be easily implemented in a microcontroller device as well as a mobile application for real-time detection. We used an EEG dataset, which was the best publicly available from the University of Bonn in Germany. The noise and artifacts of the signal were eliminated by applying three different filters, and in the next step, we decomposed the signal using enhanced wavelet analysis. Furthermore, out of the many features extracted, three features were selected for the classification procedure. One of these features was our newly designed feature, the crest range. In the final stage, three of the most widely used machine-learning techniques in epilepsy classification, namely ANN, KNN, and SVM, were used for training and testing. After random classification experiments with different testing percentages, including 20%, 30%, 40%, and 50%, and after calculating the average accuracy of at least ten classifications at each percentage level, we achieved the best accuracy when using the KNN classifier.
We developed a mobile application that monitored the behavior of users based on EEG processing and classification. The application was connected to the classification procedure through a cloud server and updated the results instantly whenever a new signal was processed. An alarm would be triggered should an epileptic seizure be detected. Furthermore, we have also tested real-time seizure detection using a Neurosky Mindwave headset that utilizes Bluetooth to exchange data with other devices. The device reads the EEG signal from the forehead and sends it via Bluetooth for processing. However, at this stage, we could only test the normal EEG signal since using the device on epileptic patients is out of our scope.
In future work, we will collect data from epileptic patients so that we can use and test the Mindwave device to collect epileptic EEG signals in real time. As well as trying to detect different types of seizures.

Author Contributions

Participated in this research: Z.L., K.E., R.R.R., M.F. and E.A. Conducted and designed the experiments: Z.L., K.E. and R.R.R. Performed data analysis: Z.L. and K.E. The manuscript was written by Z.L. and K.E. R.R.R. and E.A. contributed to the intellectual merit of the new investigation. All authors have read and agreed to the published version of the manuscript.

Funding

CT Next-UB grant number UB-18-102 funded this research.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

ANNArtificial Neural Network
CEEMDComplementary ensemble empirical mode decomposition
CNNConvolutional Neural Network
DFADetrended Fluctuation Analysis
DTDetection Rate
DWTDiscrete Wavelet Transform
EEGElectroencephalograph
FFacForm Factor
FF-ANNFeedforward Artificial Neural Network
FFNNFeedforward Neural Network
FFTFast Fourier transform
FInfoFisher information
FIRFinite Impulse Response
FMQASfuzzy measure-theoretic quantum approximation of an abstract system
GMMGaussian mixture model
HCompHjorth Parameters: Complexity
HFDHiguchi Fractal Dimension
HMobHjorth Parameters: Mobility
HuExpHurst Exponent
IQRHounsfield units
KNNk-Nearest Neighbor
KurtKurtosis
LADLatent Dirichlet Allocation
LMBPPNLevenberg–Marquardt back propagation network
LRLayer-wise Relevance
LS-SVMLeast Squares Support Vector Machines
MADMedian Absolute Deviation
MAXMaximum
MFDMandelbrot Fractal Dimension
MINMinimum
NBNaïve Bayes
NNNeural Network
PAPRPeak-to-Average Power Ratio
PeEnPermutation Entropy
PFDPetrosian Fractal Dimension
PNNProbabilistic Neural Network
PSI_RIRPower Spectral Intensity, and the relative intensity Ratio
RBFNNRadial Basis Function Neural Network
RBF-SVMRadial Basis Function Support Vector Machine
Recurrent NNRecurrent Neural Network
RMSRoot Mean Square
SampEnSample Entropy
SenSpectral Entropy
SkwSkewness
STDHRstandard deviation of the heart rate
SVDEnSVD Entropy
SVMSupport Vector Machine
TotVarTotal Variation
TroughMinimum value
VarVariance
XGBoosteXtreme Gradient Boosting

References

  1. Orhan, U.; Hekim, M.; Ozer, M.; Provaznik, I. Epilepsy diagnosis using probability density functions of EEG signals. In Proceedings of the 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), Istanbul, Turkey, 15–18 June 2011; pp. 626–630. [Google Scholar]
  2. World Health Organization. Epilepsy; World Health Organization: Geneva, Switzerland, 2018.
  3. Acharya, U.R.; Sree, S.V.; Swapna, G.; Martis, R.J.; Suri, J.S. Automated EEG analysis of epilepsy: A review. Knowl. Based Syst. 2013, 45, 147–165. [Google Scholar] [CrossRef]
  4. Sanz-García, A.; Vega-Zelaya, L.; Pastor, J.; Sola, R.G.; Ortega, G.J. Towards Operational Definition of Postictal Stage: Spectral Entropy as a Marker of Seizure Ending. Entropy 2017, 19, 81. [Google Scholar] [CrossRef]
  5. Veisi, I.; Pariz, N.; Karimpour, A. Fast and Robust Detection of Epilepsy in Noisy EEG Signals Using Permutation Entropy. In Proceedings of the 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering, Boston, MA, USA, 14–17 October 2007; pp. 200–203. [Google Scholar]
  6. Li, J.; Yan, J.; Liu, X.; Ouyang, G. Using permutation entropy to measure the changes in EEG signals during absence seizures. Entropy 2014, 16, 3049–3061. [Google Scholar] [CrossRef]
  7. Prince, P.G.K.; Hemamalini, R. A survey on soft computing techniques in epileptic seizure detection. In Proceedings of the International Conference on Emerging Trends in Robotics and Communication Technologies (INTERACT), Chennai, India, 3–5 December 2010; pp. 377–380. [Google Scholar]
  8. Orosco, L.; Laciar, E.; Correa, A.G.G.; Torres, A.; Graffigna, J.P. An epileptic seizures detection algorithm based on the empirical mode decomposition of EEG. In Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA, 3–6 September 2009; pp. 2651–2654. [Google Scholar]
  9. Shalbaf, R.; Hosseini, P.T.; Analoui, M. Epilepsy detection using detrended fluctuation analysis. In Proceedings of the 2009 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2009, Baoding, China, 12–15 July 2009; pp. 235–240. [Google Scholar]
  10. England, M.J.; Liverman, C.T.; Schultz, A.M.; Strawbridge, L.M. Epilepsy across the spectrum: Promoting health and understanding.: A summary of the Institute of Medicine report. Epilepsy Behav. 2012, 25, 266–276. [Google Scholar] [CrossRef] [PubMed]
  11. Lasefr, Z.; Ayyalasomayajula, S.S.V.; Elleithy, K. Epilepsy seizure detection using EEG signals. In Proceedings of the 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York, NY, USA, 19–21 October 2017; pp. 162–167. [Google Scholar]
  12. Weissman, D. What is EEG? Available online: https://lsa.umich.edu/psych/danielweissmanlab/whatiseeg.htm (accessed on 15 May 2017).
  13. Ba-Karait, N.O.S.; Shamsuddin, S.M.; Sudirman, R. Swarm negative selection algorithm for electroencephalogram signals classification. J. Comput. Sci. 2009, 5, 995. [Google Scholar] [CrossRef]
  14. Zhao, Z.; Hu, Q. The application of a computer monitoring system using IoT technology. Comput. Intell. Neurosci. 2022, 2022, 4033886. [Google Scholar] [CrossRef]
  15. Katona, J.; Ujbanyi, T.; Sziladi, G.; Kovari, A. Electroencephalogram-based brain-computer interface for internet of robotic things. In Cognitive Infocommunications, Theory and Applications; Springer: Berlin/Heidelberg, Germany, 2019; pp. 253–275. [Google Scholar]
  16. Kovari, A. Study of algorithmic problem-solving and executive function. Acta Polytech. Hung 2020, 17, 241–256. [Google Scholar] [CrossRef]
  17. Costescu, C.; Rosan, A.; Brigitta, N.; Hathazi, A.; Kovari, A.; Katona, J.; Demeter, R.; Heldal, I.; Helgesen, C.; Thill, S. Assessing visual attention in children using gp3 eye tracker. In Proceedings of the 2019 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), Naples, Italy, 23–25 October 2019; pp. 343–348. [Google Scholar]
  18. Ujbányi, T.; Kővári, A.; Sziládi, G.; Katona, J. Examination of the eye-hand coordination related to computer mouse movement. Infocommun. J. 2020, 12, 26–31. [Google Scholar] [CrossRef]
  19. Katona, J.; Kovari, A.; Heldal, I.; Costescu, C.; Rosan, A.; Demeter, R.; Thill, S.; Stefanut, T. Using eye-tracking to examine query syntax and method syntax comprehension in LINQ. In Proceedings of the 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), Mariehamn, Finland, 23–25 September 2020; pp. 000437–000444. [Google Scholar]
  20. Aboalayon, K.A.I.; Faezipour, M.; Almuhammadi, W.S.; Moslehpour, S. Sleep stage classification using EEG signal analysis: A comprehensive survey and new investigation. Entropy 2016, 18, 272. [Google Scholar] [CrossRef]
  21. Gonzalez-Vellon, B.; Sanei, S.; Chambers, J.A. Support vector machines for seizure detection. In Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003, Darmstadt, Germany, 17 December 2003; pp. 126–129. [Google Scholar]
  22. Joel, J. Detection of seizure precursors from depth EEG using a sign periodogram transform. IEEE Trans. Bio Med. Eng. 2004, 51, 449–458. [Google Scholar]
  23. Mammone, N.; Duun-Henriksen, J.; Kjaer, T.W.; Morabito, F.C. Differentiating interictal and ictal states in childhood absence epilepsy through permutation Rényi entropy. Entropy 2015, 17, 4627–4643. [Google Scholar] [CrossRef]
  24. Martín, M.T.; Plastino, A.; Vampa, V. A maximum entropy approach for predicting epileptic tonic-clonic seizure. Entropy 2014, 16, 4603–4611. [Google Scholar] [CrossRef]
  25. Sharanreddy, M.; Kulkarni, P. Review of significant research on EEG based automated detection of epilepsy seizures and brain tumor. Int. J. Sci. Eng. Res. 2011, 2, 1–9. [Google Scholar]
  26. Harikumar, R.; Sukanesh, R.; Bharathi, P.A. Genetic algorithm optimization of fuzzy outputs for classification of epilepsy risk levels from EEG signals. In Proceedings of the Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 7–10 November 2004; pp. 1585–1589. [Google Scholar]
  27. Sharma, R.; Pachori, R.B.; Acharya, U.R. An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures. Entropy 2015, 17, 5218–5240. [Google Scholar] [CrossRef]
  28. Sinha, A.K.; Loparo, K.A.; Richoux, W.J. A new system theoretic classifier for detection and prediction of epileptic seizures. In Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Francisco, CA, USA, 1–5 September 2004; pp. 415–418. [Google Scholar] [PubMed]
  29. Aarabi, A.; Fazel-Rezai, R.; Aghakhani, Y. Seizure detection in intracranial EEG using a fuzzy inference system. In Proceedings of the Engineering in Medicine and Biology Society, EMBC 2009, Minneapolis, MN, USA, 3–6 September 2009; pp. 1860–1863. [Google Scholar]
  30. Bao, F.S.; Lie, D.Y.-C.; Zhang, Y. A new approach to automated epileptic diagnosis using EEG and probabilistic neural network. In Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence, Dayton, OH, USA, 3–5 November 2008; pp. 482–486. [Google Scholar]
  31. Bezobrazova, S.; Golovko, V. Comparative Analysis of Forecasting Neural Networks in the Application for Epilepsy Detection. In Proceedings of the 2007 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Dortmund, Germany, 6–8 September 2007; pp. 202–206. [Google Scholar]
  32. Fani, M.; Azemi, G. Automatic epilepsy detection using the instantaneous frequency and sub-band energies of the EEG signals. In Proceedings of the 2011 19th Iranian Conference on Electrical Engineering (ICEE), Tehran, Iran, 17–19 May 2011; pp. 1–5. [Google Scholar]
  33. Sivasankari, N.; Thanushkodi, K. Epileptic seizure detection on eeg signal using statistical signal processing and neural networks. In Proceedings of the 1st WSEAS International Conference on Sensors and Signals, Stevens Point, WI, USA, 7–9 November 2008; pp. 98–102. [Google Scholar]
  34. Juarez-Guerra, E.; Alarcon-Aquino, V.; Gomez-Gil, P. Epilepsy seizure detection in EEG signals using wavelet transforms and neural networks. In New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering; Springer: Berlin/Heidelberg, Germany, 2015; pp. 261–269. [Google Scholar]
  35. Kumar, S.P.; Sriraam, N.; Benakop, P. Automated detection of epileptic seizures using wavelet entropy feature with recurrent neural network classifier. In Proceedings of the TENCON 2008-2008 IEEE Region 10 Conference, Hyderabad, India, 19–21 November 2008; pp. 1–5. [Google Scholar]
  36. Kiranmayi, G.; Udayashankara, V. Neural network classifier for the detection of epilepsy. In Proceedings of the 2013 International conference on Circuits, Controls and Communications (CCUBE), Bengaluru, India, 27–28 December 2013; pp. 1–4. [Google Scholar]
  37. Ghosh-Dastidar, S.; Adeli, H.; Dadmehr, N. Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Trans. Biomed. Eng. 2008, 55, 512–518. [Google Scholar] [CrossRef] [PubMed]
  38. Dilber, D.; Kaur, J. EEG based detection of epilepsy by a mixed design approach. In Proceedings of the 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, 20–21 May 2016; pp. 1425–1428. [Google Scholar]
  39. Mihandoost, S.; Amirani, M.C.; Varghahan, B.Z. Seizure detection using wavelet transform and a new statistical feature. In Proceedings of the 2011 5th International Conference on Application of Information and Communication Technologies (AICT), Azerbaijan, Baku, 12–14 October 2011; pp. 1–5. [Google Scholar]
  40. Kumari, R.S.S.; Jose, J.P. Seizure detection in EEG using time frequency analysis and SVM. In Proceedings of the 2011 International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), Nagercoil, India, 23–24 March 2011; pp. 626–630. [Google Scholar]
  41. Panda, R.; Khobragade, P.S.; Jambhule, P.D.; Jengthe, S.N.; Pal, P.R.; Gandhi, T.K. Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction. In Proceedings of the 2010 International Conference on Systems in Medicine and Biology, Kharagpur, India, 16–18 December 2010; pp. 405–408. [Google Scholar]
  42. Liu, Y.; Zhou, W.; Yuan, Q.; Chen, S. Automatic Seizure Detection Using Wavelet Transform and SVM in Long-Term Intracranial EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 2012, 20, 749–755. [Google Scholar] [CrossRef]
  43. Murugavel, A.M.; Ramakrishnan, S.; Balasamy, K.; Gopalakrishnan, T. Lyapunov features based EEG signal classification by multi-class SVM. In Proceedings of the 2011 World Congress on Information and Communication Technologies, Mumbai, India, 11–14 December 2011; pp. 197–201. [Google Scholar]
  44. Schneider, M.; Mustaro, P.N.; Lima, C.A. Automatic recognition of epileptic seizure in EEG via support vector machine and dimension fractal. In Proceedings of the 2009 International Joint Conference on Neural Networks, Atlanta, GA, USA, 14–19 June 2009; pp. 2841–2845. [Google Scholar]
  45. Shen, C.-P.; Chan, C.-M.; Lin, F.-S.; Chiu, M.-J.; Lin, J.-W.; Kao, J.-H.; Chen, C.-P.; Lai, F. Epileptic seizure detection for multichannel EEG signals with support vector machines. In Proceedings of the 2011 IEEE 11th International Conference on Bioinformatics and Bioengineering, Taichung, Taiwan, 24–26 October 2011; pp. 39–43. [Google Scholar]
  46. Seng, C.H.; Demirli, R.; Khuon, L.; Bolger, D. Seizure detection in EEG signals using support vector machines. In Proceedings of the Bioengineering Conference (NEBEC), 2012 38th Annual Northeast, Philadelphia, PA, USA, 16–18 March 2012; pp. 231–232. [Google Scholar]
  47. Yuan, Y. Detection of epileptic seizure based on EEG signals. In Proceedings of the 2010 3rd International Congress on Image and Signal Processing (CISP), Yantai, China, 16–18 October 2010; pp. 4209–4211. [Google Scholar]
  48. Hadj-Youcef, M.; Adnane, M.; Bousbia-Salah, A. Detection of epileptics during seizure free periods. In Proceedings of the 2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA), Algiers, Algeria, 12–15 May 2013; pp. 209–213. [Google Scholar]
  49. Rafiuddin, N.; Khan, Y.U.; Farooq, O. Feature extraction and classification of EEG for automatic seizure detection. In Proceedings of the 2011 International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), Aligarh, India, 17–19 December 2011; pp. 184–187. [Google Scholar]
  50. Chua, K.C.; Chandran, V.; Acharya, R.; Lim, C.M. Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals: A comparative study. In Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2008, Vancouver, BC, Canada, 20–25 August 2008; pp. 3824–3827. [Google Scholar]
  51. Kumar, S.S.P.; Ajitha, L. Early detection of epilepsy using EEG signals. In Proceedings of the 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kanyakumari, India, 10–11 July 2014; pp. 1509–1514. [Google Scholar]
  52. 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]
  53. Rashid, M.M.o.; Ahmad, M. Epileptic seizure classification using statistical features of EEG signal. In Proceedings of the 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox’s Bazar, Bangladesh, 16–18 February 2017; pp. 308–312. [Google Scholar]
  54. Vandecasteele, K.; De Cooman, T.; Gu, Y.; Cleeren, E.; Claes, K.; Paesschen, W.V.; Huffel, S.V.; Hunyadi, B. Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment. Sensors 2017, 17, 2338. [Google Scholar] [CrossRef]
  55. Wang, L.; Xue, W.; Li, Y.; Luo, M.; Huang, J.; Cui, W.; Huang, C. Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis. Entropy 2017, 19, 222. [Google Scholar] [CrossRef]
  56. Gu, Y.; Cleeren, E.; Dan, J.; Claes, K.; Van Paesschen, W.; Van Huffel, S.; Hunyadi, B. Comparison between Scalp EEG and Behind-the-Ear EEG for Development of a Wearable Seizure Detection System for Patients with Focal Epilepsy. Sensors 2018, 18, 29. [Google Scholar] [CrossRef]
  57. Wu, J.; Zhou, T.; Li, T. Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting. Entropy 2020, 22, 140. [Google Scholar] [CrossRef]
  58. Aileni, R.M.; Pasca, S.; Florescu, A. EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks. Sensors 2020, 20, 3346. [Google Scholar] [CrossRef]
  59. Molla, M.K.I.; Hassan, K.M.; Islam, M.R.; Tanaka, T. Graph Eigen Decomposition-Based Feature-Selection Method for Epileptic Seizure Detection Using Electroencephalography. Sensors 2020, 20, 4639. [Google Scholar] [CrossRef] [PubMed]
  60. Abiyev, R.; Arslan, M.; Bush Idoko, J.; Sekeroglu, B.; Ilhan, A. Identification of Epileptic EEG Signals Using Convolutional Neural Networks. Appl. Sci. 2020, 10, 4089. [Google Scholar] [CrossRef]
  61. Zhang, Y.; Yang, S.; Liu, Y.; Zhang, Y.; Han, B.; Zhou, F. Integration of 24 Feature Types to Accurately Detect and Predict Seizures Using Scalp EEG Signals. Sensors 2018, 18, 1372. [Google Scholar] [CrossRef]
  62. Mansouri, A.; Singh, S.P.; Sayood, K. Online EEG Seizure Detection and Localization. Algorithms 2019, 12, 176. [Google Scholar] [CrossRef]
  63. Shabarinath, B.; Challagulla, K.; Visodhan, M.R. A Comparative Study of Epileptic Seizure Detection Framework using SVM and ELM. In Proceedings of the 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India, 15–17 May 2019; pp. 302–306. [Google Scholar]
  64. Ahmed, A.; Benoudnine, H. A Novel Blending Hilbert-Kolmogorov Approach for Epileptic Seizures detection. In Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 19–24 July 2020; pp. 1–7. [Google Scholar]
  65. Abedin, M.Z.; Akther, S.; Hossain, M.S. An artificial neural network model for epilepsy seizure detection. In Proceedings of the 2019 5th International Conference on Advances in Electrical Engineering (ICAEE), Dhaka, Bangladesh, 26–28 September 2019; pp. 860–865. [Google Scholar]
  66. Gupta, S.; Bagga, S.; Maheshkar, V.; Bhatia, M. Detection of Epileptic Seizures using EEG Signals. In Proceedings of the 2020 International Conference on Artificial Intelligence and Signal Processing (AISP), Amaravati, India, 10–12 January 2020; pp. 1–5. [Google Scholar]
  67. Prasanna, J.; Thomas, G.S.; Subathra, M.; Sairamya, N. Automatic Epileptic Seizure Classification using MODWT and SVM. In Proceedings of the 2019 2nd International Conference on Signal Processing and Communication (ICSPC), Coimbatore, India, 29–30 March 2019; pp. 113–116. [Google Scholar]
  68. Karim, A.Z.; Bashar, S.S.; Miah, M.S.; Al Mahmud, M.A.; Al Amin, M.A. Identification of seizure from single channel EEG using Support Vector Machine & Hilbert Vibration Decomposition. In Proceedings of the 2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA), Kuala Lumpur, Malaysia, 17–18 July 2020; pp. 1–6. [Google Scholar]
  69. Lasefr, Z.; Ayyalasomayajula, S.S.V.; Elleithy, K. An efficient automated technique for epilepsy seizure detection using EEG signals. In Proceedings of the 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York, NY, USA, 26–29 October 2017; pp. 76–82. [Google Scholar]
  70. Hassan, A.R.; Bhuiyan, M.I.H. Automatic sleep scoring using statistical features in the EMD domain and ensemble methods. Biocybern. Biomed. Eng. 2016, 36, 248–255. [Google Scholar] [CrossRef]
  71. Scott, M.T.; Scott, T.J.; Kelly, V.G. The validity and reliability of global positioning systems in team sport: A brief review. J. Strength Cond. Res. 2016, 30, 1470–1490. [Google Scholar] [CrossRef]
  72. Ghosh-Dastidar, S.; Adeli, H.; Dadmehr, N. Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans. Biomed. Eng. 2007, 54, 1545–1551. [Google Scholar] [CrossRef]
  73. Lasefr, Z.; Reddy, R.R.; Elleithy, K. Smart phone application development for monitoring epilepsy seizure detection based on EEG signal classification. In Proceedings of the 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York, NY, USA, 19–21 October 2017; pp. 83–87. [Google Scholar]
  74. Cattani, L.; Saini, H.P.; Ferrari, G.; Pisani, F.; Raheli, R. SmartCED: An Android application for neonatal seizures detection. In Proceedings of the 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Benevento, Italy, 15–18 May 2016; pp. 1–6. [Google Scholar]
  75. DeVaul, R.; Barkalow, D.; Carlton-Foss, J.; Elledge, C. Method and System for Fall Detection and Motion Analysis. U.S. Patent Application No. 11/372,843, 14 December 2006. [Google Scholar]
  76. Madansingh, S.; Thrasher, T.A.; Layne, C.S.; Lee, B.-C. Smartphone based fall detection system. In Proceedings of the 2015 15th International Conference on Control, Automation and Systems (ICCAS), Busan, South Korea, 13–16 October 2015; pp. 370–374. [Google Scholar]
  77. Fang, S.H.; Liang, Y.C.; Chiu, K.M. Developing a mobile phone-based fall detection system on Android platform. In Proceedings of the 2012 Computing, Communications and Applications Conference, Hong Kong, China, 11–13 January 2012; pp. 143–146. [Google Scholar]
  78. Yavuz, G.; Kocak, M.; Ergun, G.; Alemdar, H.O.; Yalcin, H.; Incel, O.D.; Ersoy, C. A smartphone based fall detector with online location support. In Proceedings of the International Workshop on Sensing for App Phones, Zurich, Switzerland, 2 November 2010; pp. 31–35. [Google Scholar]
Figure 1. Epilepsy Seizure Detection Process.
Figure 1. Epilepsy Seizure Detection Process.
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Figure 2. Research Plan.
Figure 2. Research Plan.
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Figure 3. Normal Signal.
Figure 3. Normal Signal.
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Figure 4. Epileptic Signal.
Figure 4. Epileptic Signal.
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Figure 5. Epileptic Signal with no seizure.
Figure 5. Epileptic Signal with no seizure.
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Figure 6. Decomposition Technique.
Figure 6. Decomposition Technique.
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Figure 7. Decomposed Normal Signal.
Figure 7. Decomposed Normal Signal.
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Figure 8. Decomposed Epileptic Signal.
Figure 8. Decomposed Epileptic Signal.
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Figure 9. Power Spectral for Normal Signal.
Figure 9. Power Spectral for Normal Signal.
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Figure 10. Power Spectral for Epileptic Signal.
Figure 10. Power Spectral for Epileptic Signal.
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Figure 11. Classification Performance of SVM.
Figure 11. Classification Performance of SVM.
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Figure 12. Classification Performance of ANN.
Figure 12. Classification Performance of ANN.
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Figure 13. Classification Performance of KNN.
Figure 13. Classification Performance of KNN.
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Figure 14. Architecture Diagram.
Figure 14. Architecture Diagram.
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Figure 15. Application, Server, and Device Connection.
Figure 15. Application, Server, and Device Connection.
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Figure 16. Login/Registration Screen.
Figure 16. Login/Registration Screen.
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Figure 17. Epilepsy Detection Notification.
Figure 17. Epilepsy Detection Notification.
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Table 1. Previous Methods.
Table 1. Previous Methods.
AuthorsSignalDatasetFeaturesClassifiersResults
Sinha et al., 2004 [28] EEGCollected from 5 patientsSpectral PowerFMQASSatisfactory
Arabi et al., 2009 [29]EEGCollected from 21 patientsEntropy, Dominant frequency, Average amplitude, and Rhythmicity PNN98.7% ACC
Bao et al., 2008 [30]EEGBonn UniversityPower spectral features (PSF), Petrosian and Higuchi fractal dimensions (PFD, HFD), and Hjorth parametersPNN96.7% ACC
Bezobrazova and Golovko, 2007 [31]EEGCollected from 6 patients ANN96.7% ACC
Fani and Azmi, 2011 [32]EEGBonn UniversityMean of IF, Mean of Kaiser energy, and EnergyANN94% ACC
Sivasankari and Thanushkodi, 2008 [33]EEGBonn UniversityMean, Absolute value, and VarianceANN93.23% ACC
Juarez-Guerra et al., 2015 [34]EEGBonn UniversityMean, Absolute median, and VarianceFF-ANN93.23% ACC
Kumar et al., 2008 [35]EEGBonn UniversityWavelet entropy and Spectral entropyRecurrent NN96.3% ACC
Kiranmaji and Udayashankara, 2013 [36]EEGJSS medical hospital, Mysuru, IndiaMaximum, Minimum, Mean, and Standard deviation FFNN81.67 ACC
Ghosh-Dastidar et al., 2008 [37]EEGBonn UniversityStandard deviation, Correlation dimension, and Largest Lyapunov exponentK-Means Clustering, Discrimination analysis, LMBPPN, and RBFNN96.7% ACC
Dawood Dilber et al., 2016 [38]EEGPhysioNetMean, Standard deviation, Variance FFT, and Wavelet transformK-Means and Discrimination analysis70% ACC, 93% ACC
Mihandoos et al., 2011 [39]EEGBonn UniversityFourth moment of wavelet coefficient divided by the second moment, Max–Min, Zero-crossing of wavelet coefficient, Variance of wavelet coefficient, Mean of wavelet coefficientKNN and Bayesian learning machine96.8% ACC, 98% ACC
Kumari and Jose, 2011 [40]EEGBonn UniversityVariance, Energy, and Power spectral densitySVM98% ACC
Panda et al., 2010 [41]EEGBonn UniversityEnergy, Entropy, and Standard deviation SVM91.2% ACC
Liu et al., 2012 [42]EEGUniversity Hospital of Freiburg, GermanyRelative entropy, Relative amplitude, Coefficient of variation, and Fluctuation indexRBF-SVM 95.33% ACC
Murugavel et al., 2011 [43]EEGBonn UniversityMaximum, Minimum, Mean, and Standard deviationPNN, RBFNN, MSVM94% ACC, 93% ACC, 96% ACC
Schneider et al., 2009 [44]EEGBonn UniversityHiguchi algorithm, Katz algorithm, and Sevcik algorithmSVMHiguchi algorithm has the higher accuracy
Shen et al., 2011 [45]EEGNational Taiwan University HospitalTotal variation, Standard Deviation, and EnergySVM98.9% ACC
Seng et al., 2012 [46]EEGBonn UniversityMean, Coefficient of variation, Dominant frequency, Mean of power spectrum, and VarianceSVM98% ACC
Yuan, 2010 [47]EEGCollected from epileptic patientsCao’s methodPNN and SVM96.3% ACC
Hadj-Youcef et al., 2013 [48]EEGBonn UniversityMaximum, Minimum, Range standard deviation, Energy, and EntropySVM98% ACC
Rafiuddin et al., 2011 [49]EEGAMU UniversityEnergy, Coefficient of variation, IQR, and MADLAD96.5% ACC
Chua et al., 2008 [50]EEGBonn UniversityMean of spectral magnitude, Entropy, and Power spectrum GMM93.11% ACC
Kumar et al., 2014 [51]EEGBonn UniversityNormalized bispectral (NB) entropy, NB squared entropy, and NB cubed entropy, Bispectrum phase entropy, Mean bispectrum magnitude, and Moment of bispectrum PNN, KNN, DT, and SVM96% ACC, 96% ACC, 95% ACC, 98% ACC
Vijith et al., 2016 [52]EEGGovernment Medical College Thiruvananthapuram, Kerala Approximate entropy, Sample entropy, and Hurst exponentSVM91% ACC
Rashid et al., 2017 [53]EEGBonn UniversityMean, Median, Maximum, Minimum, Range, Standard deviation, Median absolute deviation, Mean absolute deviation, 12 Norm, and Max NormNN80% ACC
Vandecasteele et al., 2017 [54]ECGCollected from patient at UZ Leuven GasthuisbergHR peak, HR base, and STDHR baseSVM70% Sen
Wang et al., 2017 [55]EEGBonn UniversityMean, Variance, Coefficient of variation, Total variation, Maximum, MinimumSVM, KNN, LDA, NB, and LR99.25% ACC
Gu et al., 2018 [56]EEGCollected from patientsMean powers, and Peak frequencySVM100% Sen, 94.5% Sen
Wu, 2020 [57]EEGBonn University and CHB-MIT datasetTime domain, Frequency domain, Time–frequency domain, and Entropy-based featuresCEEMD + XGBoost99% ACC, 95.7% ACC
Maria et al., 2020 [58]EEG, EMG, PPGCAP Sleep DatabaseDWTANN91.1% ACC
Molla et al., 2020 [59]EEGBonn UniversitySODP, Squared coefficient of variation of the absolute series, Fluctuation index, Permutation entropy, Approximate entropy, and Renyi’s entropyFFNN99.5% ACC
Abiyev et al., 2020 [60]EEGBonn UniversityFeatures obtained from the convolutional layers of CNNCNN96.67% ACC
Zhang et al., 2018 [61]EEGAmerican Clinical Neurophysiology SocietyPFD, MFD, FInfo, HComp, HMob, DFA, HuExp, TotVar, FFac, PAPR, RMS, Peak, Kurt, Skw, Var, Trough, Crest, Mean, HFD, SampEn, PeEn, SVDEn, SEn, and PSI_RIRSVM99.40% ACC
Mansouri et al., 2019 [62]EEGPhysionet CHB-MITPower in band of interestAdaptive threshold, Distance network83% Sen
Shabarinath et al., 2019 [63]EEGBonn UniversityPower spectral density and Wavelet coefficientsSVM90.1% ACC
Adda et al., 2020 [64]EEGBonn UniversityAmplitude and Kolmogorov complexitySVM97% ACC
Abedin et al., 2019 [65]EEGBonn UniversityMean, Standard deviation, Median, Kurtosis, Skewness, Variance, Maximum, Minimum, and Root mean square ANN97.33% ACC
Gupta et al., 2020 [66]EEGBonn UniversityMean, Standard deviation, Root mean square, Skew, Kurtosis, Maximum, Coefficient of variation, and Shannon entropyCNN99.29% ACC
Prasanna et al., 2019 [67]EEGKarunya EEG databaseMean and Standard deviationSVM95.8% ACC
Karim et al., 2020 [68]EEGBonn UniversityMean instantaneous frequenciesLS-SVM97.66% ACC
Table 2. Results for Max–Min Feature.
Table 2. Results for Max–Min Feature.
Percentage Using Maximum–Minimum FeatureClassifiers
ANNSVMKNN
80% Training and 20% Testing96.49%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% Testing94.95%93.18%95.06%
Table 3. Results for Band-Power Feature.
Table 3. Results for Band-Power Feature.
Percentage Using Band-Power FeatureClassifiers
ANNSVMKNN
80% Training and 20% Testing90.97%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% Testing86.21%88.27%89.23%
Table 4. Results for Crest-Range Feature.
Table 4. Results for Crest-Range Feature.
Percentage Using Crest-Range Feature Classifiers
ANNSVMKNN
80% Training and 20% Testing98.03%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% Testing94.48%94.92%95.68%
Table 5. Results for Amplitude-Range and Band-Power Features.
Table 5. Results for Amplitude-Range and Band-Power Features.
Percentage Using Amplitude-Range and Band-Power FeaturesClassifiers
ANNSVMKNN
80% Training and 20% Testing97.87%96.40%96.72%
70% Training and 30% Testing95.91%95.97%95.63%
60% Training and 40% Testing94.72%93.53%93.62%
50% Training and 50% Testing94.40%93.21%93.23%
Table 6. Results for Band-Power and Crest-Range Features.
Table 6. Results for Band-Power and Crest-Range Features.
Percentage Using Band-Power and Crest-Range FeaturesClassifiers
ANNSVMKNN
80% Training and 20% Testing96.66%97.06%96%
70% Training and 30% Testing96.02%96.20%95.51%
60% Training and 40% Testing95.07%94.64%93.70%
50% Training and 50% Testing94.24%93.57%92.71%
Table 7. Results for Amplitude-Range and Crest-Range Features.
Table 7. Results for Amplitude-Range and Crest-Range Features.
Percentage Using Amplitude-Range and Crest-Range FeaturesClassifiers
ANNSVMKNN
80% Training and 20% Testing97.25%96.94%98.27%
70% Training and 30% Testing96.44%95.40%97.36%
60% Training and 40% Testing94.75%94.25%96.23%
50% Training and 50% Testing94.17%93.82%95.76%
Table 8. Results for Three Combined Features.
Table 8. Results for Three Combined Features.
Percentage Using Three Combined FeaturesClassifiers
ANNSVMKNN
80% Training and 20% Testing98.43%97.92%98.10%
70% Training and 30% Testing96.38%97.20%96.52%
60% Training and 40% Testing96.20%96.55%95.68%
50% Training and 50% Testing96.06%94.32%93.83%
Table 9. Table of Comparison.
Table 9. Table of Comparison.
NoTitleAuthorFeaturesClassifierAccuracy Results
1Automatic 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-bandsArtificial neural network (ANN)94%
2Neural 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
ANN81.67%
3Early 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%
4Epileptic seizure detection in EEG signals using wavelet transforms and neural networks (2015)Juarez-Guerra
et al. [34]
mean,
absolute,
median,
variance
Feed-forward neural network93.23%
5EEG 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%
6Epileptic seizure detection using nonlinear analysis of EEG (2016)Vijith et al. [52]approximate entropy,
sample entropy,
hurst exponent
Support-vector machine89%/91%
7Epileptic 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%
8Mixed-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)
9Seizure 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%
10Seizure 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%
11Classification 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%
12Automatic 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%
13Lyapunov 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%
14Seizure 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%
15Detection of epilepsy during seizure-free periods (2013)Hadj-Youcef et al. [48]maximum,
minimum,
range,
standard deviation
Support-vector machine (SVM)98%
16Feature 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%
17Automatic 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%
18Proposed method: (2023)Lasefr et al.amplitude range,
band power,
crest range
ANN,
SVM,
KNN
96% 95% 98%
Table 10. Table of Comparison of Related Mobile Applications.
Table 10. Table of Comparison of Related Mobile Applications.
ApplicationAuthorMethodFeatures
Android application for neonatal colonic seizures detectionCattani et al. [74]Movement of some body parts, video and image processingA laptop required for processing. No real users.
Android applicationDeVaul et al. [75]Fall detectionLow accuracy.
Android applicationMadansingh et al. [76]Analyzing body movement using existing embedded sensorsNo notification.
Fitbit devicesFang et al. [77]SMS and GPS alertsLack the compatibility. High cost.
Android applicationYavuz et al. [78]Fall detectionGPS locations/SMS.
Proposed android applicationLasefr et al.EEG signal processing High accuracy. Immediate notification. Development for real-time processing.
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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

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