Key result
Lightweight DWT denoising improves ECG signal-to-noise ratio by ~6 dB during rapid ESP32 execution.
Why the study?
ECG signals acquired in semi-rural primary health centers using low-cost AD8232 front-ends are routinely corrupted by power-line interference, baseline wander, and electromyographic noise.
Effect estimate: 6.29 dB improvement
A lightweight DWT denoising pipeline using the db6 wavelet effectively improves ECG signal quality on resource-constrained devices, enabling low-cost rural tele-cardiology.
May enhance ECG quality for arrhythmia screening in low-resource clinics; leaves open prospective validation against clinical endpoints before adoption.
Electrocardiogram (ECG) signals acquired in semi-rural primary health centers using low-cost AD8232 front-ends are routinely corrupted by power-line interference (50 Hz), baseline wander caused by electrode motion, and broadband electromyographic noise. This paper presents a lightweight Discrete Wavelet Transform (DWT) denoising pipeline benchmarked on 30 records of the MIT-BIH Arrhythmia Database (fs = 360 Hz). Seven wavelet families are compared at decomposition levels L = 2–7 using Donoho's universal soft-threshold rule with a robust median absolute deviation estimator. The proposed db6 wavelet at L = 5 yields an output SNR of 7.46 dB from a 1.17 dB input — an improvement of 6.29 dB — while preserving the QRS morphology with only 0.0019 mean-square error. The algorithm has a complexity of O(N) and executes in 11.3 ms on an ESP32 (240 MHz), making it deployable on rural tele-cardiology kiosks costing under ₹2,500.
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Pratiksha S. Gawali, Mohammed Irfan Shaikh, Vinay K. Bhure (2026) studied ECG signal denoising (n=30). Discrete Wavelet Transform (DWT) denoising pipeline (db6 wavelet at L = 5) vs. Other wavelet families and decomposition levels was evaluated on Output signal-to-noise ratio (SNR) and mean-square error (6.29 dB improvement). A lightweight DWT denoising pipeline using the db6 wavelet at L=5 improved ECG signal-to-noise ratio by 6.29 dB with a 0.0019 mean-square error, executing in 11.3 ms on an ESP32.
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