Ear-ECG Denoising Using Heart Sounds and the Extended Kalman Filter

Adam Pullin, Jake Stuchbury-Wass, Mathias Ciliberto, Kayla-Jade Butkow, Philipp Lepold, Tobias Röddiger, Cecilia Mascolo

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Keywords: Earable, Electrocardiogram (ECG), Denoising

TL;DR: We employed in-ear microphones to provide information about the phase of individual cardiac cycles, allowing an Extended Kalman Filter to remove noise from other high-amplitude biosignals and retrieve the ear-ECG signal.

Abstract:

Electrocardiogram (ECG) recording systems are increasingly being integrated into consumer wearable systems such as smartwatches, providing users with access to clinically-relevant information about their heart activity anytime, anywhere. The increasing adoption of in-ear wearables, known as earables, as well as their stable position on the body, makes them an attractive prospect for ECG integration. However, this comes with several challenges.
Other biosignals, including those from the brain and surrounding muscles, are detectable at the ear in the same frequency bands with much higher amplitudes. This means that the ECG signal-to-noise ratio (SNR) can be extremely low at this location. The few existing denoising approaches mostly rely on autoencoders. In some cases they fail to recover the ECG morphology, and their black-box nature does not allow for explainability or understanding of limitations.

To address these issues, we introduce a novel system to record and denoise ear-ECG signals, leveraging open-source hardware and the Extended Kalman Filter. In-ear audio recording of heart sounds is used to accurately determine timings of cardiac cycles. From these timings, a short-term ensemble average ECG signal is calculated, which is used to fit the parameters of a dynamical ECG model to an individual user. The Kalman filter is then applied to the full time series ECG for denoising, using the dynamical model for its state prediction steps, and heart sounds as phase measurements. We have evaluated the system with data collected from 18 participants. The results report a mean SNR of 6.4 dB, mean absolute QT interval error of 54 ms, and heart rate error of 3 BPM, demonstrating the system's potential for continuous, non-invasive, user-friendly ECG monitoring.

Track: 2. Sensors and systems for digital health, wellness, and athletics

NominateReviewer: Adam Pullin, [e-mail]

Submission Number: 98