Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject’s bio-signals. One of its most relevant applications is the acquisition and analysis of Electrocardiograms (ECGs). These low-power WBSN designs, while able to perform advanced signal processing to extract information on hearth conditions of subjects, are usually constrained in terms of computational power and transmission bandwidth. It is therefore beneficial to identify in the early stages of analysis which parts of an ECG acquisition are critical and activate only in these cases detailed (and computationally intensive) diagnosis algorithms. In this paper, we introduce and study the performance of a real-time optimized neuro-fuzzy classifier based on random projections, which is able to discern normal and pathological heartbeats on an embedded WBSN. Moreover, it exposes high confidence and low computational and memory requirements. Indeed, by focusing on abnormal heartbeats morphologies, we proved that a WBSN system can effectively enhance its efficiency, obtaining energy savings of as much as 63% in the signal processing stage and 68% in the subsequent wireless transmission when the proposed classifier is employed.