The proposed method achieved a of 99.24% and of 99.51% in seizure detection.
A new method for seizure detection and prediction was developed using a convolutional neural network-gated recurrent unit-attention mechanism.
(EEG) signals were processed through wavelet decomposition, resulting in six subbands for analysis.
Features were extracted from the time-frequency domain and nonlinear characteristics of each subband.
The method demonstrated an accuracy of 99.35% in detecting seizures and 95.16% in predicting them.
Tenfold cross-validation on the CHB-MIT dataset validated the effectiveness of this approach.
Simplified
Epilepsy is one of the most well-known neurological disorders globally, leading to individuals experiencing sudden seizures and significantly impacting their quality of life. Hence, there is an urgent necessity for an efficient method to detect and predict seizures in order to mitigate the risks faced by epilepsy patients. In this paper, a new method for seizure detection and prediction is proposed, which is based on multi-class feature fusion and the convolutional neural network-gated recurrent unit-attention mechanism (CNN-GRU-AM) model. Initially, the (EEG) signal undergoes wavelet decomposition through the Discrete Wavelet Transform (DWT), resulting in six subbands. Subsequently, time-frequency domain and nonlinear features are extracted from each subband. Finally, the CNN-GRU-AM further extracts features and performs classification. The CHB-MIT dataset is used to validate the proposed approach. The results of tenfold cross validation show that our method achieved a of 99.24% and 95.47%, of 99.51% and 94.93%, accuracy of 99.35% and 95.16%, and an AUC of 99.34% and 95.15% in seizure detection and prediction tasks, respectively. The results show that the method proposed in this paper can effectively achieve high-precision detection and prediction of seizures, so as to remind patients and doctors to take timely protective measures.
Key numbers
99.24%
for Seizure Detection
Average achieved in seizure detection across 24 cases.
99.51%
for Seizure Detection
Average achieved in seizure detection across 24 cases.
95.47%
for Seizure Prediction
Average achieved in seizure prediction across 24 cases.
Full Text
We can’t show the full text here under this license.
