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On the CHB-MIT dataset DSCNN_Net reaches 89.58% sensitivity with 11,714 parameters and 45.75 KB of weight memory \u2013 roughly an order of magnitude fewer parameters than comparable CNN baselines at similar sensitivity. Replacing standard 3D convolution with its depthwise separable form reduces the per-layer multiply \u2013 accumulate cost by approximately 10\u00d7 without a loss of predictive performance, supporting real-time operation on low-power edge platforms.<\/jats:p>","DOI":"10.55056\/jec.1172","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T19:48:32Z","timestamp":1779220112000},"page":"173-188","source":"Crossref","is-referenced-by-count":1,"title":["Optimising seizure prediction with reduced computational resources using depthwise CNN"],"prefix":"10.55056","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7622-8611","authenticated-orcid":false,"given":"Ritesh Dhananjay","family":"Nikose","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2766-7820","authenticated-orcid":false,"given":"Suchismita","family":"Chinara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"33647","published-online":{"date-parts":[[2026,5,21]]},"reference":[{"key":"126844","doi-asserted-by":"crossref","unstructured":"Abdelhameed, A. and Bayoumi, M., 2021. 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