Key result
Hermite spline edge framework detects real-time ECG R-peaks with ~99% accuracy on 8-bit microcontrollers.
Why the study?
The expansion of the Internet of Medical Things has increased the demand for real-time ECG monitoring on wearable devices, but implementing signal processing on ultra-low-power edge nodes is constrained by hardware limitations, specifically the absence of a Floating-Point Unit and restricted SRAM capacity.
Does a Hermite spline-based edge framework on 8-bit microcontrollers accurately detect R-peaks and preserve ECG morphology during rest and physical activity?
Does a Hermite spline-based edge framework on 8-bit microcontrollers accurately detect R-peaks and preserve ECG morphology during rest and physical activity?
A Hermite spline-based filtering and R-peak detection framework enables high-accuracy, real-time ECG processing on resource-constrained 8-bit microcontrollers with a minimal 2.3 KB memory footprint.
Enables real-time ECG analysis on resource-constrained wearables; leaves open prospective clinical validation before adoption.
The expansion of the Internet of Medical Things (IoMT) has increased the demand for real-time electrocardiogram (ECG) monitoring on wearable devices. However, implementing signal processing on ultra-low-power edge nodes is constrained by hardware limitations, specifically the absence of a Floating-Point Unit (FPU) and restricted SRAM capacity. This paper presents a resource-efficient framework for real-time R-peak detection optimized for 8-bit microcontrollers. The core component is a piecewise-polynomial smoothing algorithm based on Hermite splines, integrated directly into the ADC sampling loop. To maintain signal integrity under motion artifacts, a cascading filtering architecture is employed, operating on fixed-size frame buffers with a peak memory footprint of approximately 2.3 KB. Detection performance was evaluated on physical hardware signals (AD8232/ATmega2560) recorded during rest and physical activity, while reference recordings from the MIT-BIH Arrhythmia Database served as a morphological reference. Statistical analysis using ensemble averaging and point-wise confidence intervals (96% confidence level, D = 2.05) confirmed that the filtering process suppresses quantization noise and myographic artifacts without distorting the physiological morphology of the ECG. Experimental evaluation demonstrated a sensitivity (SEN) of 94.67–99.83%, a positive predictive value (PPV) of 99.30–100%, and a mean F-score of 98.95%. Extracting and transmitting metadata (RR intervals) instead of raw signal samples reduces network traffic and overall power consumption in autonomous edge-based cardiac monitoring nodes.
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Yefremov et al. (2026) studied Cardiac arrhythmia / ECG monitoring. Resource-efficient edge framework with Hermite spline smoothing and cascading filtering on 8-bit microcontrollers vs. MIT-BIH Arrhythmia Database reference recordings was evaluated on R-peak detection performance (mean F-score). The Hermite spline-based edge framework on 8-bit microcontrollers achieved a mean F-score of 98.95%, sensitivity of 94.67-99.83%, and PPV of 99.30-100% for real-time ECG R-peak detection.
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