In Journal of neural engineering ; h5-index 52.0
OBJECTIVE : Patient-dependent seizure detection based on intracranial electroencephalography (iEEG) has made significant progress. However, due to the difference in the location and number of iEEG electrodes used for each patient, patient-independent seizure detection based on iEEG has not been carried out. Additionally, current seizure detection algorithms based on deep learning have outperformed traditional machine learning algorithms in many performance metrics, but they still have shortcomings of large memory footprints and slow inference speed.
APPROACH : To solve the above problems of the current study, we propose a novel lightweight convolutional neural network (CNN) model combining a convolutional block attention module (CBAM). Its performance for patient-independent seizure detection is evaluated on two long-term continuous iEEG datasets: SWEC-ETHZ and TJU-HH. Finally, we reproduce four other patient-independent methods to compare with our method and calculate the memory footprints and inference speed for all methods.
MAIN RESULTS : Our method achieves 83.81% sensitivity and 85.4% specificity on the SWEC-ETHZ dataset and 86.63% sensitivity and 92.21% specificity on the TJU-HH dataset. In particular, it takes only 11ms to infer 10 minutes of 128 channels of iEEG data, and its memory footprint is only 22 kB. Compared to baseline methods, our method not only achieves better patient-independent seizure detection performance but also has a smaller memory footprint and faster inference speed.
SIGNIFICANCE : To our knowledge, this is the first iEEG-based patient-independent seizure detection study. This facilitates the application of seizure detection algorithms to the future clinic.
Si Xiaopeng, Yang Zhuobin, Zhang Xingjian, Sun Yulin, Jin Weipeng, Wang Le, Yin Shaoya, Ming Dong
2023-Jan-10
convolutional neural network, iEEG, lightweight, patient-independent, seizure detection
