Epileptic Seizure Detection and Prediction Using Deep Learning Technique

Nanthini, K.; Tamilarasi, A.; Pyingkodi, M.; Dishanthi, M.; Kaviya, S. M.; Mohideen, P. Aslam

doi:10.1109/iccci54379.2022.9740802

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…The purpose of this research was to launch a small and low-cost gadget that can be wearable. Normal activities named Tumor, Hstumor, Eyec, and Eyeo and abnormal activities named Es on the Epileptic seizure dataset were used in this paper, achieving an accuracy of 99% [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method

et al. 2023

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The Human Activity Recognition (HAR) system is the hottest research area in clinical research. The HAR plays a vital role in learning about a patient’s abnormal activities; based upon this information, the patient’s psychological state can be estimated. An epileptic seizure is a neurological disorder of the human brain and affects millions of people worldwide. If epilepsy is diagnosed correctly and in an early stage, then up to 70% of people can be seizure-free. There is a need for intelligent automatic HAR systems that help clinicians diagnose neurological disorders accurately. In this research, we proposed a Deep Learning (DL) model that enables the detection of epileptic seizures in an automated way, addressing a need in clinical research. To recognize epileptic seizures from brain activities, EEG is a raw but good source of information. In previous studies, many techniques used raw data from EEG to help recognize epileptic patient activities; however, the applied method of extracting features required much intensive expertise from clinical aspects such as radiology and clinical methods. The image data are also used to diagnose epileptic seizures, but applying Machine Learning (ML) methods could address the overfitting problem. In this research, we mainly focused on classifying epilepsy through physical epileptic activities instead of feature engineering and performed the detection of epileptic seizures in three steps. In the first step, we used the open-source numerical dataset of epilepsy of Bonn university from the UCI Machine Learning repository. In the second step, data were fed to the proposed ELM model for training in different training and testing ratios with a little bit of rescaling because the dataset was already pre-processed, normalized, and restructured. In the third step, epileptic and non-epileptic activity was recognized, and in this step, EEG signal feature extraction was automatically performed by a DL model named ELM; features were selected by a Feature Selection (FS) algorithm based on ELM and the final classification was performed using the ELM classifier. In our presented research, seven different ML algorithms were applied for the binary classification of epileptic activities, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), Stochastic Gradient Boosting Classifier (SGDC), Gradient Boosting Classifier (GB), Decision Trees (DT), and three deep learning models named Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN). After deep analysis, it is observed that the best results were obtained by our proposed DL model, Extreme Learning Machine (ELM), with an accuracy of 100% accuracy and a 0.99 AUC. Such high performance has not attained in previous research. The proposed model’s performance was checked with other models in terms of performance parameters, namely confusion matrix, accuracy, precision, recall, F1-score, specificity, sensitivity, and the ROC curve.

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Section: Introductionmentioning

confidence: 99%

Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method

et al. 2023

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“…These features are taken as input to classifiers, deep learning methods, or different machine learning methods. These methods have been used to optimize feature subsets such as PSD and to train the models [26,27,28,29,30,31,32,33,34]. In a study, spectral power was calculated in nine bands and used as a feature set for seizure prediction [35].…”

Section: Introductionmentioning

confidence: 99%

Epileptic seizure detection combining power spectral density and high-frequency oscillations

Tutuk,

Zengin

2023

International Journal of Applied Mathematics Electronics and Computers

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Detection of pre-seizure signs in epileptic signals may help patients to survive the seizure with minimal damage. This study aims to detect epileptic seizure patterns using EEG datasets of five patients. The signals' maximum power spectral density (PSD) and high-frequency oscillations (HFOs) signals are investigated. The PSDs of all patients' signals are calculated, and the subbands of the maximum PSD are examined. It is observed that 95%, 85%, 85%, 75%, and 80% of the channels of the five patients are in the sum of delta and theta subbands of the maximum PSD, respectively. All patients' maximum power frequency subbands of F4 and T3 channels included only delta and theta subbands. Furthermore, frequency increase rates of pre-ictal and ictal signals are investigated, and increasing PSDs of ripples and fast ripples are then calculated. A much higher-frequency ripple follows the low-frequency ripple in 75%, 75%, 65%, 85%, and 75% of the channels of the first, second, third, fourth, and fifth patients, respectively. For the pre-ictal data, a much higher frequency ripple is not seen, followed by a low-frequency ripple in 90%, 85%, 75%, 90%, and 90% of all channels of five patients, respectively. In addition, in this study, the frequency of signals is observed to be 80 Hz and above in the Fp2, C4, P4, O2, and Pz channels, which are common to all patients. Consequently, examining PSD and HFO signals ensures the detection of the differences between the data sets and detects the epileptic seizure patterns in all five patients.

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Target-vs-One and Target-vs-All Classification of Epilepsy Using Deep Learning Technique

Amin,

Al-Obeidat,

Algeelani

et al. 2024

Lecture Notes in Networks and Systems

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Epileptic Seizure Detection and Prediction Using Deep Learning Technique

Cited by 6 publications

References 15 publications

Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method

Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method

Epileptic seizure detection combining power spectral density and high-frequency oscillations

Target-vs-One and Target-vs-All Classification of Epilepsy Using Deep Learning Technique

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