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Manual electroencephalography (EEG) signals analysis is time and energy consuming, making automatic detection using EEG signals particularly important. Many deep learning algorithms have thus been proposed to detect seizures. These methods rely on expensive and bulky hardware, which makes them unsuitable for deployment on devices with limited resources due to their high demands on computer resources. In this paper, we propose a novel lightweight neural network for seizure detection using pure convolutions, which is composed of inverted residual structure and multi-scale channel attention mechanism. Compared with other methods, our approach significantly reduces the computational complexity, making it possible to deploy on low-cost portable devices for seizures detection. We conduct experiments on the CHB-MIT dataset and achieves 98.7% accuracy, 98.3% sensitivity and 99.1% specificity with 2.68[Formula: see text]M multiply-accumulate operations (MACs) and only 88[Formula: see text]K parameters. <\/jats:p>","DOI":"10.1142\/s0129065723500612","type":"journal-article","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T04:47:10Z","timestamp":1694839630000},"source":"Crossref","is-referenced-by-count":12,"title":["Lightweight Seizure Detection Based on Multi-Scale Channel Attention"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0081-9456","authenticated-orcid":false,"given":"Ziwei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. 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