Our　study　proposes　an　automatic　technique　for　the　detection　of　MI　using　hybrid　signal　processing　tools　and　deterministic　learning　theory.　To　begin　with,　the　characteristic　envelope　and　first　derivative　of　a　single-lead　ECG　signal　are　extracted　using　Shannon　energy.　By　incorporating　the　Shannon　energy　envelope　(SEE)　in　the　phase　portrait　of　an　ECG　signal,　the　non-linear　system　dynamics　can　be　captured.　In　the　second　step,　using　fast　and　adaptive　multivariate　empirical　mode　decomposition　(FA-MVEMD)　with　the　SEE　of　the　ECG　and　its　derivative,　scale-aligned　intrinsic　mode　components　(IMFs)　are　generated.　The　two　initial　IMFs,　which　contain　the　most　energy　in　the　ECG　signals　and　their　derivatives,　are　considered　the　predominant　IMFs　and　employed　as　features.　There　are　significant　differences　between　ECG　signals　produced　by　normal　(healthy)　and　MI-related　cardiac　systems.　The　third　step　involves　applying　deterministic　learning　theory　with　neural　networks　to　model,　identify　and　classify　ECG　signals　into　two　groups.　As　a　final　step,　an　evaluation　of　the　effectiveness　of　the　method　is　then　performed　on　the　PTB　diagnostic　ECG　database,　which　comprises　signals　from　148　MI　patients　and　52　healthy　controls.　The　average　classification　accuracy　achieved　with　a　cross-validation　scheme　of　10　folds　is　reported　as　99.21　%.　In　conclusion,　our　results　confirm　the　proposed　features　are　consistent　with　ECG　system　dynamics,　as　well　as　complementing　existing　ECG　features　for　automatic　MI　detection.　©　2023　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.