Discrimination of Preictal and Interictal Brain States from Long-Term EEG Data

Tsiouris, Κostas Μ.; Pezoulas, Vasileios C.; Koutsouris, Dimitrios; Zervakis, Michalis; Fotiadis, Dimitrios I.

doi:10.1109/cbms.2017.33

Cited by 27 publications

(26 citation statements)

References 11 publications

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“…This duration was determined since it is highly important to give an effective time for the physician to intervene in the patient before the seizure onset [16]. Moreover, the preictal interval is an arbitrary choice of investigators since there is no precise time limit for the preictal state [2]. Therefore, in this study, the preictal window of 30 min was chosen.…”

Section: Eeg Datasetmentioning

confidence: 99%

“…There were various seizures that occurred over a short interval in the CHB-MIT EEG data set, of which the seizure that occurred first was included in the analysis There were 20 cases that fulfilled the inclusion criteria (see Table 1). The time periods other than the preictal, ictal, and postictal periods were accepted as interictal, which occurred at least two hours away from the seizure [2]. In addition, segments were chosen as non-overlapping 5 sec as in the study [2].…”

Section: Eeg Datasetmentioning

confidence: 99%

“…Epilepsy is a neurological disease of the central nervous system (CNS), affecting approximately 1% of the world population [1]. The disease occurs by unpredictable seizures due to sudden malfunction during normal activities of neurons [1,2]. An electroencephalogram (EEG) testing is used to examine changes in the electrical brain activity causing epilepsy and to identify any seizure that may occur.…”

Section: Introductionmentioning

confidence: 99%

“…The important point in seizure prediction is to determine the preictal state in which the changes in brain activity begin to occur. With a strong seizure prediction system, the disease can be controlled easily [2]. Before the seizure begins, taking the necessary precautions can easily predict and even prevent the seizure.…”

Section: Introductionmentioning

confidence: 99%

“…Cho et al used phase-locking values to classify EEG signals as interictal or preictal [8]. Tsiouris et al classified preictal and interictal states using the features obtained by the frequency domain, time domain, and graph theory [2]. Subaşı et al divided EEG signals into sub-bands by wavelet transform, whereby the features were extracted by PCA, LDA, and IDA and were classified accordingly [9].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Preictal and Interictal Recognition for Epileptic Seizure Prediction Using Pre-trained 2D-CNN Models

Toraman¹

2020

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Epilepsy is a neurological disease affecting almost 1% of world population. Predicting a possible seizure will make a significant contribution to improving the quality of life of patients suffering from this disease. One of the most important steps in seizure prediction studies is the preictal activity recognition stage. In many previous studies, the preictal state was determined to end at the onset of the seizure, which makes it difficult for the physician to intervene in the patient in a possible seizure. In the proposed method, unlike previous studies, the preictal state was determined as the 30-minute interval ending 30 minutes before the onset of an epileptic seizure. The method consisted of three stages; (I) preictal and interictal activities were divided into five-second segments, (ii) the separated signals were converted into spectrograms, and (iii) the spectrogram images were classified using three different pre-trained CNN models (VGG19, ResNet, DenseNet) and the results were compared among these models. Classification was performed separately using the predetermined four EEG channels for 20 cases in the CHB-MIT dataset. The best classification accuracy value in preictal/interictal discrimination (91.05%) was obtained on channel 8 (P3-O1). An important contribution of this study was that the proposed approach provided important information about the preictal and interictal discrimination of the section 30 minutes before the onset of seizures. In addition, by examining the four channels separately, channel-based information on preictal/interictal discrimination was also obtained. Based on these results, we consider that the proposed method will bring a different perspective to seizure prediction studies.

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Section: Eeg Datasetmentioning

confidence: 99%

Section: Eeg Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Preictal and Interictal Recognition for Epileptic Seizure Prediction Using Pre-trained 2D-CNN Models

Toraman¹

2020

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Deep learning‐based seizure prediction using EEG signals: A comparative analysis of classification methods on the CHB‐MIT dataset

Esmaeilpour,

Tabarestani,

Niazi

2024

Engineering Reports

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Epilepsy is a brain disorder that causes patients to have multiple seizures. About 30% of patients with epilepsy are not treated with medication or surgery. The abnormal activity of brain before occurring of a seizure (about a few minutes before a seizure occurs) are known as the preictal area. Therefore, if we can predict this state, we can control possible seizures by using appropriate medications. In this study, we present a method for predicting epileptic seizures using electroencephalogram (EEG) signals. The method can identify the preictal region that occurs before the onset of seizures. In our proposed method, first the noise removal of EEG signals is performed, and then the necessary features are extracted using a convolution neural network. Finally, we use the feature vectors in order to train multiple classifiers, fully connected layer, random forest, and support vector machines with linear kernel. Additionally, we apply maximum voting, which is an ensemble method, to classify preictal segments from interictal ones. In this study, using EEG signals of patients from CHB‐MIT dataset, we were able to achieve sensitivity of 90.76%.

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Epileptic Seizure Prediction Based on Convolutional Recurrent Neural Network with Multi-Timescale

Duan

Hou

Qiao

et al. 2019

Intelligence Science and Big Data Engineering. Big Data and Machine Learning

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Discrimination of Preictal and Interictal Brain States from Long-Term EEG Data

Cited by 27 publications

References 11 publications

Preictal and Interictal Recognition for Epileptic Seizure Prediction Using Pre-trained 2D-CNN Models

Preictal and Interictal Recognition for Epileptic Seizure Prediction Using Pre-trained 2D-CNN Models

Deep learning‐based seizure prediction using EEG signals: A comparative analysis of classification methods on the CHB‐MIT dataset

Epileptic Seizure Prediction Based on Convolutional Recurrent Neural Network with Multi-Timescale

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