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FPGA based real-time epileptic seizure prediction system

Cosgun Ercan, Çelebi Anil

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

278--292

EN

The development of systems that can predict epileptic seizures in real-time offers great hope for epilepsy patients. These systems aim to prevent accidents that patients may experience caused by the loss of consciousness during seizures. Therefore, patients must use real-time epileptic seizure prediction systems that do not interfere with their daily activities. In this study, using the unipolar EEG data from a surface electrode, a patient-specific estimation system is implemented in real-time on a system on chip (SoC) that contains an embedded processor and programmable logic blocks. The European epilepsy database EPILEPSIAE is used in the scope of this work. In the proposed system, pre-processing is applied to the EEG data. Then, the features of the data in the frequency domain are extracted. The classifier model is trained with the RusBoosted Tree cluster classifier, which is a machine learning algorithm. Testing is carried out using the proposed classification model. Threshold values are determined, and then false alarms and erroneous classifications are prevented by post-processing. At the end of the tests, prediction success, sensitivity (SEN), Specificity (SPE), False Prediction Rate (FPR), and prediction times are obtained as 77.30%, 95.94%, 0.041 h_1, and 33.23 min, respectively. The proposed system outperforms other studies in the liter-ature in the number of electrodes, real-time operation, hardware/software architecture, and FPR performance. A wearable seizure prediction system seems to be commercialized according to the results achieved in this study.

2

Epileptic seizure prediction using scalp electroencephalogram signals

Usman Syed Muhammad, Khalid Shehzad, Bashir Zafar

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

211--220

EN

Epilepsy is a brain disorder in which patients undergo frequent seizures. Around 30% of patients affected with epilepsy cannot be treated with medicines/surgical procedures. Abnormal activity, known as the preictal state starts few minutes before the seizure actually occurs. Therefore, it may be possible to deliver medication prior to the occurrence of a seizure if initiation of the preictal state can predicted before the seizure onset. We propose an epileptic seizure prediction method that predicts the preictal state before the seizure onset using electroencephalogram (EEG) monitoring of brain activity. It involves three steps including preprocessing of EEG signals, feature extraction classification of preictal and interictal states. In our proposed method, we have used (i) Empirical model decomposition to remove noise from the EEG signals and Generative Adversarial Networks to generate preictal samples to deal with the class imbalance problem; (ii) Automated features have been extracted with three layer Convolutional Neural Networks and (iii) Classification between preictal and interictal states is done with Long Short Term Memory units. In this study, we have used CHBMIT dataset of scalp EEG signals and have validated our proposed method on 22 subjects of dataset. Our proposed seizure prediction method is able to achieve 93% sensitivity and 92.5% specificity with average time of 32 min to predict the seizure's onset. Results obtained from our method have been compared with recent state-of-the-art epileptic seizure prediction methods. Our proposed method performs better in terms of sensitivity, specificity and average anticipation time.

3

Automated detection of the preseizure state in EEG signal using neural networks

Sudalaimani C., Sivakumaran N., Elizabeth Thomas T., Rominus Valsalam S.

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 1

160--175

EN

The life-threatening neural syndrome epilepsy is elicited by seizure which affects over 50 million people in the universal. A seizure is a brain condition made by excessive, unusual exoneration by nerve cells of the brain. Contemporary seizure forecast research works exhibited worthy results in both undersized and lengthy electroencephalography (EEG) signal; however it is essential to formulate superior epileptic seizure forecast system; that shall be steady, constant and less resource intensive for effectively employed to heading for evolving a convenient and easily manageable ictal or seizure forewarning prearrangement or devices. Based on our exploration, we have found a novel seizure prediction method which we evaluated by producing ten sub-frequency EEG data from initially recorded signal. Simple, robust and computationally less-intense EEG characteristics are mined using the generated sub-frequency signals and applied the extracted features to computationally less intense generalized regression neural network (GRNN) to segregate EEG signal clips into normal or preseizure files. In this research work, we have engendered 10 sub-frequency bands of signals from original EEG recordings, extracted various meaningful features from those sub-frequency band signals, created 10 GRNN neural networks to categorize feature files as normal or preseizure, and then applied post-processing techniques with 10 thresholding mechanisms to each classifier output. As such, we determined that seizure fore-warning may function better in various sub-frequency bands for many patients in a subject-specific manner. We also found that epileptic-seizure forecast performed superior at '60 Hz high pass' filtered sub-frequency band EEG signal for all subjects or canines data.