Lightweight Deep Autoencoder for ECG Denoising with Morphology Preservation and Near Real-Time Hardware Deployment

TL;DR

This paper introduces a lightweight deep autoencoder for ECG denoising that maintains waveform morphology and can be deployed on edge devices like Raspberry Pi for near real-time processing, ensuring clinical diagnostic integrity.

Contribution

The study presents a novel, compact autoencoder architecture trained under severe noise conditions, validated across multiple noise types, and successfully deployed on low-power hardware for real-time ECG denoising.

Findings

Effective noise suppression across various noise levels and types

Minimal morphological distortion of ECG signals

Inference latency suitable for near-real-time applications

Abstract

Electrocardiogram (ECG) signals are often degraded by various noise sources such as baseline wander, motion artifacts, and electromyographic interference, posing a major challenge in clinical settings. This paper presents a lightweight deep learning-based denoising framework, forming a compact autoencoder architecture. The model was trained under severe noise conditions (-5 dB signal-to-noise ratio (SNR)) using a rigorously partitioned dataset to ensure no data leakage and robust generalization. Extensive evaluations were conducted across seven noise configurations and three SNR levels (-5 dB, 0 dB, and +5 dB), showing consistent denoising performance with minimal morphological distortion, critical for maintaining diagnostic integrity. In particular, tests on clinically vital rhythms such as ventricular tachycardia (VT) and ventricular fibrillation (VF) confirm that the proposed model effectively suppresses noise without altering arrhythmic features essential for diagnosis. Visual and quantitative assessments, including SNR improvement, RMSE, and correlation metrics, validate the model's efficacy in preserving waveform fidelity. To demonstrate real-world applicability, the model was deployed on a Raspberry Pi 4 using TensorFlow Lite with float16 precision. Inference latency was measured at just 1.41 seconds per 14-second ECG segment, indicating feasibility for near-real-time use in edge devices. Overall, this study introduces a lightweight, hardware-validated, and morphologically reliable ECG denoising solution suitable for integration into portable or wearable healthcare systems.