Epilepsy,　a　neurological　disorder　marked　by　recurrent　seizures,　demands　precise　classification　of　electroencephalogram　(EEG)　signals　for　effective　diagnosis　and　monitoring.　This　paper　introduces　a　novel　methodology　that　integrates　the　Adaptive　Local　Iterative　Filtering　(ALIF)　for　signal　decomposition　and　leverages　cascaded　deep　neural　networks　for　robust　signal　classification.　The　motivation　arises　from　the　critical　need　for　heightened　accuracy　in　epilepsy-related　EEG　signal　analysis.　The　research　methodology　begins　with　the　application　of　ALIF　to　decompose　EEG　signals.　This　process　generates　multiple　Intrinsic　Mode　Functions　(IMFs)　that　capture　the　inherent　oscillatory　components　of　the　signals.　The　subsequent　step　involves　inputting　these　IMFs　into　a　cascaded　deep　neural　network.　Within　the　cascaded　deep　neural　network　(CDNN),　a　feature　extraction　module　utilizes　the　SEblock　channel　attention　mechanism　to　automatically　extract　salient　features　from　the　IMFs.　This　attention　mechanism　enhances　the　network＇s　ability　to　focus　on　essential　information,　improving　the　overall　discriminative　power　of　the　model.　Following　feature　extraction,　the　classification　module　of　the　CDNN　is　employed　to　categorize　the　EEG　signals　into　their　respective　classes.　The　entire　process　is　subjected　to　rigorous　validation　using　a　10　-fold　cross-validation　strategy.　The　proposed　methodology　demonstrates　exceptional　performance　when　tested　on　both　the　Bonn　and　EEG　Epilepsy　databases.　Achieving　a　classification　accuracy　of　100%　in　both　cases　highlights　the　efficacy　of　the　approach　in　accurately　identifying　epileptic　EEG　signals,　showcasing　its　potential　for　reliable　clinical　applications.　©　2024　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.