Butterworth Filtering at 500 Hz Optimizes PPG-Based Heart Rate Variability Analysis for Wearable Devices: A Comparative Study

Photoplethysmography (PPG)-based heart rate variability (HRV) offers a cost-effective al-ternative to electrocardiography (ECG) for autonomic monitoring in wearable devices. We optimized signal processing on a 16-bit microcontroller by comparing 4th-order equiva-lent Butterworth and Elliptic IIR bandpass filters (0.8–20 Hz, zero-phase) at 1000, 500, and 250 Hz. Paired PPG–ECG recordings from 10 healthy adults were analyzed for ln HF, ln LF, and ln VLF using Lin’s concordance correlation coefficient (CCC), ±5% equivalence testing (TOST), and Passing–Bablok regression (PBR). Butterworth at 500 Hz preserved near-identity with ECG standard (CCC ≥ 0.94; TOST met equivalence; PBR slopes/intercepts: ln HF = 0.97x + 0.10, ln LF = 1.02x − 0.07, ln VLF = 1.01x − 0.03), while halving computational load. In contrast, Elliptic at 250 Hz degraded concordance (CCC ≈ 0.64) and failed equivalence, with greater bias from nonlinear phase and ripple-induced distortion. Elliptic performance improved at higher sampling but offered no benefit over Butterworth. These results support zero-phase Butterworth filtering at ≥500 Hz as the op-timal balance of fidelity, robustness, and efficiency, enabling reliable PPG-HRV monitor-ing on low-power devices. As a pilot investigation (n=10), this study establishes prelimi-nary design parameters and optimal configurations to guide subsequent large-scale clin-ical validation.