Vectorcardiogram (VCG) signals contain a wealth of dynamic information pertinent to space-time cardiac electrical activities. However, few, if any, previous investigations have studied disease-altered nonlinear dynamics in the spatiotemporal VCG signals. Most previous nonlinear dynamic methods considered the time-delay reconstructed state space from a single ECG trace. This paper presents a novel multiscale recurrence approach to not only explore VCG recurrence dynamics but also resolve the issue of recurrence computation for the large-scale datasets. As opposed to the traditional single-scale recurrence analysis, we characterize and quantify the recurrence behaviours in multiple wavelet scales. In addition, wavelet dyadic subsampling enables the large-scale recurrence analysis, but it is used to be highly expensive for a long-term time series. The classification experiments show that multiscale recurrence analysis detects the myocardial infarctions from 3-lead VCG with an average sensitivity of 96.8% and specificity of 92.8%, which show superior performance (i.e., 5.6% improvements) to the single-scale recurrence analysis.