Patient-specific method for predicting epileptic seizures based on DRSN-GRU. (March 2023)

• Record Type: Journal Article
• Authors: Xu, Xin; Zhang, Yin; Zhang, Ruhao; Xu, Tingting
• Is Part Of: Biomedical signal processing and control. Volume 81(2023)
• Publication Date: 2023-03
• Subjects: Scalp EEG -- Preictal state -- Epilepsy prediction -- DRSN-GRU -- Deep learning -- Patient specificity; DRSN deep residual shrinkage network -- GRU gated recurrent unit -- EEG Electroencephalography; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104449
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: An efficient seizure prediction method was proposed that predicts seizures from raw EEG signals. A time window was delineated for the pre-seizure period, and the prediction within different time windows provides detailed information about the time when the seizure will occur. A time series-based seizure prediction algorithm was designed from the time series window analysis. An epilepsy prediction network based on deep residual contraction neural network and gated recurrent units is proposed. Abstract: Epilepsy is one of the most common neurological disorders worldwide and can cause the brain to stop working properly or even endanger the life of the patient. Epilepsy prediction is a prerequisite for seizure control, allowing preventive measures to mitigate damage or control seizures. It has been found that abnormal brain activity begins some time before a seizure, which is known as the pre-ictal state. In this study, we reconsidered the temporal scope of the pre-ictal period and divided it into multiple temporal windows. A patient-specific seizure prediction method based on deep residual shrinkage network (DRSN) and gated recurrent unit (GRU) was then proposed. The temporal dependency of the signal of different time windows in the pre-ictal period is modeled by introducing GRU into a DRSN. In addition, automatic feature extraction is achieved by applying soft threshold denoising and attention mechanism inside the neural network. The proposed method was tested on four patients reasonably selected from the CHB-MIT scalp EEG dataset, which achieved a sensitivity of 90.54%, an AUC value of 0.88, and a false prediction rate of 0.11/h. The results obtained by our method are compared with the recent epilepsy prediction methods. Compared with the being among the best method, our method still has a little gap, but it also shows a new idea and some advantages.