A Channel Selection Method for Epilepsy Seizure Prediction

Coşgun, Ercan; Çelebi, Anıl; Güllü, M. Kemal

doi:10.1109/inista52262.2021.9548583

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Therefore, one drawback of the pre-specified strategy is that the ignored channel cases may contain the real best channel case. In studies [49], [50], [51], [52] using the statistical criteria strategy, the authors finally attain a sensitivity of 70.9-97.83% with a FPR of 0.031-0.076/h. For the statistical criteria strategy, extracted features or classification rates from single or multiple channels are statistically evaluated to select the significant channels.…”

Section: A Compared To the Studies Using The Freiburg Database For Se...mentioning

confidence: 96%

“…EEG channel selection, as an important feature selection method in the spatial domain, was also effectively applied in seizure detection [43], [44], [45], [46], [47], seizure prediction [48], [49], [50], [51], [52] and other fields [53]. However, most of these studies focused on the combination of channel selection and conventional machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Channel Increment Strategy-Based 1D Convolutional Neural Networks for Seizure Prediction Using Intracranial EEG

Wang

Zhang

Kärkkäinen

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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The application of intracranial electroencephalogram (iEEG) to predict seizures remains challenging. Although channel selection has been utilized in seizure prediction and detection studies, most of them focus on the combination with conventional machine learning methods. Thus, channel selection combined with deep learning methods can be further analyzed in the field of seizure prediction. Given this, in this work, a novel iEEG-based deep learning method of One-Dimensional Convolutional Neural Networks (1D-CNN) combined with channel increment strategy was proposed for the effective seizure prediction. First, we used 4-sec sliding windows without overlap to segment iEEG signals. Then, 4-sec iEEG segments with an increasing number of channels (channel increment strategy, from one channel to all channels) were sequentially fed into the constructed 1D-CNN model. Next, the patient-specific model was trained for classification. Finally, according to the classification results in different channel cases, the channel case with the best classification rate was selected for each patient. Our method was tested on the Freiburg iEEG database, and the system performances were evaluated at two levels (segment-and event-based levels). Two model training strategies (Strategy-1 and Strategy-2) based on the K-fold cross validation (K-CV) were discussed in our work. (1) For the Strategy-1, a basic K-CV, a sensitivity of 90.18%, specificity of 94.81%, and accuracy of 94.42% were achieved at the segment-based level. At the event-based level, an eventbased sensitivity of 100%, and false prediction rate (FPR)

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