One-Dimensional Convolutional Neural Networks Combined with Channel Selection Strategy for Seizure Prediction Using Long-Term Intracranial EEG

Abstract

Seizure prediction using intracranial electroencephalogram (iEEG) has attracted an increasing attention during recent years. iEEG signals are commonly recorded in the form of multiple channels. Many previous studies generally used the iEEG signals of all channels to predict seizures, ignoring the consideration of channel selection. In this study, a method of one-dimensional convolutional neural networks (1D-CNN) combined with channel selection strategy was proposed for seizure prediction. First, we used 30-s sliding windows to segment the raw iEEG signals. Then, the 30-s iEEG segments, which were in three channel forms (single channel, channels only from seizure onset or free zone and all channels from seizure onset and free zones), were used as the inputs of 1D-CNN for classification, and the patient-specific model was trained. Finally, the channel form with the best classification was selected for each patient. The proposed method was evaluated on the Freiburg Hospital iEEG dataset. In the situation of seizure occurrence period (SOP) of 30[Formula: see text]min and seizure prediction horizon (SPH) of 5[Formula: see text]min, 98.60[Formula: see text] accuracy, 98.85[Formula: see text] sensitivity and 0.01/h false prediction rate (FPR) were achieved. In the situation of SOP of 60[Formula: see text]min and SPH of 5[Formula: see text]min, 98.32[Formula: see text] accuracy, 98.48[Formula: see text] sensitivity and 0.01/h FPR were attained. Compared with the many existing methods using the same iEEG dataset, our method showed a better performance.

Clinical neuroscience and neurotechnology: An amazing symbiosis

“…For this reason, clinicians and scientists are focusing increasingly their attention on these invasive recordings to develop new treatments for focal epilepsy. For example, researchers used SEEG delivered stimulation to investigate the semiology of temporal lobe epilepsy ( Mariani et al., 2021 ), study uncommon forms of epilepsy such as musicogenic epilepsy ( Pelliccia et al., 2019 ), or predict seizure onset ( Wang et al., 2022 ), type ( Sanz-García et al., 2022 ), duration ( Liu et al., 2022 ), and termination ( Salami et al., 2022 ). Results from these studies pave the way to closed-loop treatment strategies that could minimize the clinical impact of seizures.…”

Seizure Prediction Using EEG Channel Selection Method

“…Recently, deep learning techniques have also been widely used for seizure prediction. Deep learning meth-ods, including One-Demensional Convolutional Neural Networks (1D-CNN) [9], Two-Dimensional Convolutional Neural Networks (2D-CNN) [10][11][12][13], Three-Dimensional Convolutional Neural Networks (3D-CNN) [14], Long Short-Term Memory (LSTM) [15][16][17], Deep Recurrent Neural Network (DRNN) [18] and Generative Adversarial Networks (GAN) [19], were utilized to effectively predict seizures.…”

“…In our previous study [9], we mentioned that many seizure prediction studies commonly used EEG signals of all channels, ignoring the consideration of channel selection. Feature selection in the spatial domain (i.e., channel selection) has been largely ignored in this field.…”

“…Feature selection in the spatial domain (i.e., channel selection) has been largely ignored in this field. Hence, our previous study [9] presented a method of channel selection strategy combined with 1D-CNN to forecast seizures, and the proposed method was tested on the Freiburg intracranial electroencephalogram (iEEG) dataset [20], in which each patient has six channels. There are 63 channel cases (|C…”

“…) that can be analyzed for each patient. However, we only considered 9 channel cases for the anlysis of seizure prediction for each patient in the study [9]. Consequently, in this study with the same dataset, all channel cases are analyzed and discussed to select the best channel case with the best classification rate for each patient.…”