In-ear ECG Signal Enhancement with Denoising Convolutional Autoencoders

TL;DR

This paper presents a denoising convolutional autoencoder that significantly improves the quality of in-ear ECG signals, enabling more accurate heart rate estimation and waveform reconstruction from noisy recordings in wearable devices.

Contribution

The study introduces a novel DCAE model specifically designed for in-ear ECG enhancement, demonstrating its effectiveness on real and synthetic datasets with noise reduction and improved clinical feature extraction.

Findings

Median SNR increase of 5.9 dB

Heart rate estimation error reduced by 70%

R-peak detection accuracy median 90%

Abstract

The cardiac dipole has been shown to propagate to the ears, now a common site for consumer wearable electronics, enabling the recording of electrocardiogram (ECG) signals. However, in-ear ECG recordings often suffer from significant noise due to their small amplitude and the presence of other physiological signals, such as electroencephalogram (EEG), which complicates the extraction of cardiovascular features. This study addresses this issue by developing a denoising convolutional autoencoder (DCAE) to enhance ECG information from in-ear recordings, producing cleaner ECG outputs. The model is evaluated using a dataset of in-ear ECGs and corresponding clean Lead I ECGs from 45 healthy participants. The results demonstrate a substantial improvement in signal-to-noise ratio (SNR), with a median increase of 5.9 dB. Additionally, the model significantly improved heart rate estimation accuracy, reducing the mean absolute error by almost 70% and increasing R-peak detection precision to a median value of 90%. We also trained and validated the model using a synthetic dataset, generated from real ECG signals, including abnormal cardiac morphologies, corrupted by pink noise. The results obtained show effective removal of noise sources with clinically plausible waveform reconstruction ability.