Interest in edge inference for biomedical applications has boomed in recent years, given its benefits in terms of data privacy, low latency, and reduced cloud costs. We present Embedded ECG Processing on RISC-V(EEP-V), an end-to-end platform for multi-lead embedded ECG processing on RISC-V processors. EEP-V combines a custom multi-lead acquisition board, real-time digital signal conditioning, and on-device neural network inference in a fully local processing pipeline without cloud offloading. The platform is designed as an open-source hardware/software stack to support reproducible research on embedded cardiac monitoring. Our implementation targets a heterogeneous RISC-V architecture based on GAP9 and supports concurrent processing of up to 12 ECG leads. We validate the complete acquisition-to-inference pipeline using a medical-grade patient simulator and a reference multi-class arrhythmia classification model from PhysioNet/CinC Challenge 2021. On the deployed system, inference completes in 150 ms using 488 kB of L2 memory and consumes less than 5.47 mJ per classification, while the full pipeline consumes about 7 mJ per inference cycle. These results show the feasibility of an end-to-end multi-lead ECG processing platform on RISC-V and provide an open foundation for future embedded cardiac-monitoring research.