This　study　aims　to　develop　a　new　approach　for　detecting　OSA　automatically　with　ECG　system　dynamics　disparity.　First,　tunable　quality　factor　(Q-factor)　wavelet　transform　(TQWT),　variational　mode　decomposition　(VMD)　and　phase　space　of　three-dimension　(3D)　are　combined　for　the　feature　extraction,　which　can　carry　clinically　relevant　information　about　the　anomalies　present　in　the　OSA　ECG　recordings.　Second,　dynamic　modeling　and　identification　of　ECG　systems　are　carried　out　using　neural　networks.　By　constructing　on　a　bank　of　dynamical　estimators,　normal　and　OSA　ECG　signals　will　be　classified　based　on　their　differences　in　dynamics.　Finally,　a　physionet　apnea-ECG　database　consisting　of　70　overnight　recordings　from　70　participants　is　used　to　evaluate　the　outcomes.　By　using　a　cross-validation　scheme　of　10　folds,　experimental　results　indicate　that　neural　network　based　classifier　along　with　proposed　features　achieves　higher　accuracy,　sensitivity,　and　specificity　values　of　98.27%,　97.68%,　and　98.63%,　respectively.　Results　validate　that　the　proposed　method　may　serve　as　an　alternative　to　PSG　to　detect　OSA　automatically　in　clinical　settings.　©　2023　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.