Identification of Epileptogenic and Non-epileptogenic High-Frequency Oscillations Using a Multi-Feature Convolutional Neural Network Model - Ren, Guoping; Sun, Yueqian; Wang, Dan; Ren, Jiechuan; Dai, Jindong; Mei, Shanshan; Li, Yunlin; Wang, Xiaofei; Yang, Xiaofeng; Yan, Jiaqing; Wang, Qun (Frontiers in Neurology, 2021)
Accurately identifying epileptogenic zone (EZ) using high frequency oscillations (HFOs) is a challenge that must be mastered to transfer HFOs into clinical use. We analyzed the ability of a convolutional neural network (CNN) model to distinguish EZ and non-EZ HFOs. Nineteen medically intractable epilepsy patients with good surgical outcomes two years after surgery were studied. Five-minute interictal intracranial electroencephalogram epochs of slow-wave sleep were selected randomly. Then five-second segments of ripples (80-200 Hz) and fast ripples (FRs, 200-500 Hz) were detected automatically. The EZs and non-EZs were identified using the surgery resection range. We innovatively converted all epochs into four types of images using two scales: original waveforms, filtered waveforms, wavelet spectrum images, and smoothed pseudo Wigner-Ville distribution (SPWVD) spectrum images. We then used a CNN model to classify the HFOs into EZ and non-EZ categories. As a result, 7000 epochs of ripples and 2000 epochs of FRs were randomly selected from the EZ and non-EZ data for analysis. Our CNN model can distinguish EZ and non-EZ HFOs successfully. Except for original ripple waveforms, the results from CNN models that are trained using fixed-scale images are significantly better than those from models trained using fitted-scale images（P<0.05）. Of the four fixed-scale transformations, the CNN based on the adjusted SPWVD (ASPWVD) produced the best accuracies (80.89±1.43% and 77.85±1.61% for ripples and FRs, P<0.05). The CNN using ASPWVD transformation images is an effective deep learning method that can be used to classify EZ and non-EZ HFOs.