Automatic and Accurate Epilepsy Ripple and Fast Ripple Detection via Virtual Sample Generation and Attention Neural Networks

About 1% of the population around the world suffers from epilepsy. The success of epilepsy surgery depends critically on pre-operative localization of epileptogenic zones. High frequency oscillations including ripples (80-250 Hz) and fast ripples (250-500 Hz) are commonly used as biomarkers to localize epileptogenic zones. Recent literature demonstrated that fast ripples indicate epileptogenic zones better than ripples. Thus, it is crucial to accurately detect fast ripples from ripples signals of magnetoencephalography for improving outcome of epilepsy surgery. This paper proposes an automatic and accurate ripple and fast ripple detection method that employs virtual sample generation and neural networks with an attention mechanism. We evaluate our proposed detector on patient data with 50 ripples and 50 fast ripples labeled by two experts. The experimental results show that our new detector outperforms multiple traditional machine learning models. In particular, our method can achieve a mean accuracy of 89.3% and an average area under the receiver operating characteristic curve of 0.88 in 50 repeats of random subsampling validation. In addition, we experimentally demonstrate the effectiveness of virtual sample generation, attention mechanism, and architecture of neural network models.