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A Seizure Warning System Based on Multidimensional Attention Entropy and Improved Binary Mantis Search Algorithm
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DOI：10.1109/JIOT.2026.3675694.png)
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Abstract
En 中文
Seizure warning system (SWS) based on electroencephalography (EEG) is a prominent research focus in the Internet of Medical Things (IoMT). An effective SWS enables epilepsy patients to proactively implement interventions before seizures. Effective feature representation of EEG reduces communication load from IoMT processing devices to cloud servers and enhances subsequent machine/deep learning classification performance. This article proposes a seizure prediction system based on multidimensional attention entropy (MAE) and an improved binary mantis search algorithm (IBMSA). First, the MAE is calculated at the edge computing gateway by extracting attention entropy (AE) from each subband of a single channel (AE-ESSC). Based on this, the algorithm further computes the AE of a single channel with multiple subbands (AE-SCMSs), the AE of a single subband with multiple channels (AE-SSMCs), and the AE of the original signal in a single channel (AE-OSSC). Second, the IBMSA, which is deployed on cloud servers, uses a composite S-shaped and V-shaped function (CSVF) and dynamic stage selection strategy (DSSS) to find the optimal feature subset. The proposed algorithm is evaluated on data from the CHB-MIT dataset via leave-one-out cross-validation, with experimental results demonstrating an average sensitivity of 94.74% and a false prediction rate of 0.045/h. Additionally, edge deployment testing on Raspberry Pi 4 verifies the lightweight feature of the proposed system.
Keywords:
Compound function
feature engineering
mantis search algorithm (MSA)
multidimensional attention entropy (MAE)
Seizure prediction
Journal
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8.9
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Citations:
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