Key result
Novel RISC-V SoC achieves ~96% accuracy for ECG anomaly detection.
Why the study?
Continuous ECG monitoring in wearable biomedical devices requires processing architectures that balance computational simplicity with hardware efficiency.
The proposed silicon-proven System-on-Chip provides a compact, low-power hardware platform for real-time ECG anomaly detection in wearable biomedical devices.
May enable efficient wearable ECG monitoring; extends hardware designs but leaves open clinical validation.
Continuous electrocardiogram (ECG) monitoring in wearable biomedical devices requires processing architectures that balance computational simplicity with hardware efficiency. This paper presents a compact, silicon-proven System-on-Chip (SoC) integrating a PicoRV32 (RV32I) RISC-V processor core with a dedicated multiplierless 1D convolution accelerator for real-time binary ECG anomaly detection. The accelerator implements a hard-wired three-tap Laplacian kernel ([−1,2,−1]) coupled with a programmable threshold comparator. By replacing hardware multipliers with a shift-add-negate tree, the datapath requires O(N) full-adder equivalents for N-bit samples, compared with O(N2) for a multiplier-based 3-tap filter, and executes deterministically in a single clock cycle. The accelerator interfaces with the RISC-V core through a memory-mapped handshake bus. Fabricated in a 180-nm CMOS process, the SoC occupies an active core area of 0.936 mm2 (9373 standard-cell instances at 75.2% density) and was validated on a custom PCB using a UART host interface streaming MIT-BIH Arrhythmia Database records. On five records (10,393 beats) with per-patient threshold calibration, the chip achieves 95.88% Normal/Abnormal accuracy. At 1.8 V and 100 MHz the measured active power is 25.2 mW, and the measured sleep-mode leakage is 44 nW (44 nA at 1.0 V). These results provide a compact, area-efficient ASIC platform for wearable cardiac monitoring, whose power consumption can be further reduced by operating at lower clock frequencies.
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Le et al. (2026) studied Arrhythmia (ECG anomaly detection) (n=5). PicoRV32-based SoC with 1D convolution accelerator was evaluated on Normal/Abnormal accuracy. A PicoRV32-based SoC with a 1D convolution accelerator achieved 95.88% Normal/Abnormal accuracy for ECG anomaly detection on 10,393 beats from the MIT-BIH Arrhythmia Database.
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