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.

The term hearables refers to a hybrid technology which combines the advantages of wearable technology with the form factor and functionality of a headphone. The term hearables surfaced around April 2014, when Nick Hunn wrote about a new class of wearable devices that are worn as conventional headphones and has a $5 Billion market by 2018. Hearables are more than just smart-headphones that can play music or help consumers have a conversation over Bluetooth. Hearables have additional biometric, movement, or proximity sensors that can help the smart device gather context about the consumer's behavior. Also, they may have voice-enabled virtual assistants build into them, that can provide real-time insights to consumers.