A Feature Fusion Framework and Its Application to Automatic Seizure Detection

Automatic analysis of biomedical signals plays an important role in the auxiliary diagnosis of diseases. Traditional methods extract hand-crafted features by imitating doctors' experience, while recent methods focus on extracting deep features automatically by designing the architectures of deep neural networks (DNNs). Combining these two kinds of features can not only take advantage of doctors' experience but also mine the hidden information in the raw data. But directly combining these features by fully connected layers may cause complex optimization hyper-planes. To better integrate doctors' experience and deep features that doctors can hardly describe, we propose a feature fusion framework named hybrid plus framework (HPF) and apply this framework to seizure detection. HPF mainly consists of two parts: (1) the FET module, where hand-crafted features are extracted and transformed to sparse categorical features; (2) the enhanced DNN, which contains a carefully designed neural network structure with the input being original signals and sparse categorical features. Experiments on the dataset of CHB-MIT show that HPF outperforms the state-of-the-art methods. Further experiments indicate that HPF is very flexible as many of its modules can be replaced.