Dual-Modal Information Bottleneck Network for Seizure Detection

Abstract

In recent years, deep learning has shown very competitive performance in seizure detection. However, most of the currently used methods either convert electroencephalogram (EEG) signals into spectral images and employ 2D-CNNs, or split the one-dimensional (1D) features of EEG signals into many segments and employ 1D-CNNs. Moreover, these investigations are further constrained by the absence of consideration for temporal links between time series segments or spectrogram images. Therefore, we propose a Dual-Modal Information Bottleneck (Dual-modal IB) network for EEG seizure detection. The network extracts EEG features from both time series and spectrogram dimensions, allowing information from different modalities to pass through the Dual-modal IB, requiring the model to gather and condense the most pertinent information in each modality and only share what is necessary. Specifically, we make full use of the information shared between the two modality representations to obtain key information for seizure detection and to remove irrelevant feature between the two modalities. In addition, to explore the intrinsic temporal dependencies, we further introduce a bidirectional long-short-term memory (BiLSTM) for Dual-modal IB model, which is used to model the temporal relationships between the information after each modality is extracted by convolutional neural network (CNN). For CHB-MIT dataset, the proposed framework can achieve an average segment-based sensitivity of 97.42%, specificity of 99.32%, accuracy of 98.29%, and an average event-based sensitivity of 96.02%, false detection rate (FDR) of 0.70/h. We release our code at https:/[Formula: see text]/github.com/LLLL1021/Dual-modal-IB.

A Novel SE-TCN-BiGRU Hybrid Network for Automatic Seizure Detection

“…For more information, see https://creativecommons.org/licenses/by-nc-nd/4 with a linear-based classifier was used to predict between seizure and non-seizure events in epileptic EEG signals [7]. In recent years , with the development of deep learning, many deep learning-based classifiers have been proposed, such as convolutional neural network(CNN) and recurrent neural networks(RNN), which have a better performance than traditional classifiers in seizure detection [8]. For example, Shyu et al proposed an end-to-end CNN model with less parameters, which reduced the complexity of calculations and was suitable to deployment on wearable devices [1].…”