Body Area Sensor Networks (BASN) are expected to provide a way to improve medical care while reducing its costs. Reducing their energy consumption is a critical step before building reliable and durable systems. Acquisition and classification of Electrocardiogram (ECG) signals is a central task in medical BASNs. This paper introduces a method to perform the classification at ultra-low power using Sparse Neural Associative Memories (SNAM). Based on recent analog implementation of a SNAM node using the ST CMOS 65 nm design kit, the proposed SNAM uses only 864 fJ per classification. Compared to a digital ultra-low power multi-core architecture, this SNAM consumes several orders of magnitude less energy while achieving classification accuracy of 93.5 %.