ARNN: Attentive Recurrent Neural Network for Multi-channel EEG Signals to Identify Epileptic Seizures
This work addresses the need for efficient and accurate seizure detection from large EEG datasets, which is crucial for reducing manual workload in clinical settings, though it appears incremental as it builds on existing attention and LSTM methods.
The paper tackles the problem of automatic EEG interpretation for epilepsy diagnosis by proposing an Attentive Recurrent Neural Network (ARNN) that processes multi-channel EEG signals efficiently, achieving high accuracy in identifying seizures across datasets like CHB-MIT and UPenn and Mayo's Clinic.
Electroencephalography (EEG) is a widely used tool for diagnosing brain disorders due to its high temporal resolution, non-invasive nature, and affordability. Manual analysis of EEG is labor-intensive and requires expertise, making automatic EEG interpretation crucial for reducing workload and accurately assessing seizures. In epilepsy diagnosis, prolonged EEG monitoring generates extensive data, often spanning hours, days, or even weeks. While machine learning techniques for automatic EEG interpretation have advanced significantly in recent decades, there remains a gap in its ability to efficiently analyze large datasets with a balance of accuracy and computational efficiency. To address the challenges mentioned above, an Attention Recurrent Neural Network (ARNN) is proposed that can process a large amount of data efficiently and accurately. This ARNN cell recurrently applies attention layers along a sequence and has linear complexity with the sequence length and leverages parallel computation by processing multi-channel EEG signals rather than single-channel signals. In this architecture, the attention layer is a computational unit that efficiently applies self-attention and cross-attention mechanisms to compute a recurrent function over a wide number of state vectors and input signals. This framework is inspired in part by the attention layer and long short-term memory (LSTM) cells, but it scales this typical cell up by several orders to parallelize for multi-channel EEG signals. It inherits the advantages of attention layers and LSTM gate while avoiding their respective drawbacks. The model's effectiveness is evaluated through extensive experiments with heterogeneous datasets, including the CHB-MIT and UPenn and Mayo's Clinic datasets.