As　neural　networks　are　easy　to　converge　to　local　minimum,　the　ergodicity　of　chaotic　system　is　helpful　to　tackle　this　problem.　Besides,　the　real　parts　and　imaginary　parts　in　complex-valued　chaotic　systems　are　independent,　which　increase　the　ergodic　property　and　unpredictability　of　the　chaotic　signals.　Therefore,　we　propose　a　new　chaotic　neural　network　with　complex-valued　weight　for　electrocardiogram　classification.　Firstly,　a　complex　Logistic　chaotic　map　is　put　forward,　and　its　bifurcation　diagram,　Lyapunov　index,　and　chaotic　attractors　are　analyzed.　Secondly,　based　on　the　ergodicity　of　complex　Logistic　chaotic　map　and　a　novel　neuron　function,　the　learning　algorithm　including　complex-valued　weight　iteration　for　the　chaotic　neural　network　is　proposed.　Finally,　the　MIT-BIH　data-base　is　used　to　verify　the　proposed　method.　The　chaotic　neural　network　with　real　Logistic　map　and　other　classification　methods　are　also　adopted　for　comparison.　The　results　show　that　our　chaotic　neural　network　has　a　certain　improvement　in　the　accuracy　of　electrocardiogram　classification.