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UID:pretalx-eu-summit-2026-WDXS8R@cfp.riscv-europe.org
DTSTART;TZID=CET:20260610T161000
DTEND;TZID=CET:20260610T162000
DESCRIPTION:Interest in edge inference for biomedical applications has boom
 ed in recent years\, given its benefits in terms of data privacy\, low lat
 ency\, 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 in
 ference 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 con
 current processing of up to 12 ECG leads. We validate the complete acquisi
 tion-to-inference pipeline using a medical-grade patient simulator and a r
 eference multi-class arrhythmia classification model from PhysioNet/CinC C
 hallenge 2021. On the deployed system\, inference completes in 150 ms usin
 g 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 res
 ults show the feasibility of an end-to-end multi-lead ECG processing platf
 orm on RISC-V and provide an open foundation for future embedded cardiac-m
 onitoring research.
DTSTAMP:20260522T163128Z
LOCATION:Poster Island C
SUMMARY:Toward an open-source platform for multi-lead Embedded ECG Processi
 ng on RISC-V processors - Da Rocha Carvalho Bruno
URL:https://cfp.riscv-europe.org/eu-summit-2026/talk/WDXS8R/
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