In-Ear SpO₂ for Classification of Cognitive Workload

Abstract

The Ear-ECG provides a continuous Lead I electrocardiogram (ECG) by measuring the potential difference related to heart activity through the use of electrodes that can be embedded within earphones. The significant increase in wearability and comfort afforded by Ear-ECG is often accompanied by a corresponding degradation in signal quality -a common obstacle that is shared by the majority of wearable technologies. We aim to resolve this issue by introducing a Deep Matched Filter (Deep-MF) for the highly accurate detection of R-peaks in wearable ECG, thus enhancing the utility of Ear-ECG in real-world scenarios. The Deep-MF consists of an encoder stage (trained as part of an encoder-decoder module to reproduce ground truth ECG), and an R-peak classifier stage. Through its operation as a Matched Filter, the encoder section searches for matches with an ECG template pattern in the input signal, prior to filtering the matches with the subsequent convolutional layers and selecting peaks corresponding to true ECG matches. The so condensed latent representation of R-peak information is then fed into a simple R-peak classifier, of which the output provides precise R-peak locations. The proposed Deep Matched Filter is evaluated using leave-one-subject-out cross validation over 36 subjects with an age range of 18-75, with the Deep-MF outperforming existing algorithms for R-peak detection in noisy ECG. The proposed Deep-MF is bench marked against a ground truth ECG in the form of either chest-ECG or arm-ECG, and both R-peak recall and R-peak precision is calculated. The Deep-MF achieves a median R-peak recall of 94.9% and a median precision of 91.2% across subjects when evaluated with leave-one-subject-out cross validation. Moreover, when evaluated across a range of thresholds, the Deep-MF achieves an area under the curve (AUC) value of 0.97. The interpretability of Deep-MF as a Matched Filter is further strengthened by the analysis of its response to partial initialisation with an ECG template. We demonstrate that the Deep Matched Filter algorithm not only retains the initialised ECG kernel structure during the training process, but also amplifies portions of the ECG which it deems most valuable -namely the P wave, and each aspect of the QRS complex. Overall, the Deep Matched Filter serves as a valuable step forward for the real-world functionality of Ear-ECG and, through its explainable operation, the acceptance of deep learning models in e-health.

Hearables: feasibility of recording cardiac rhythms from single in-ear locations

“…However, for the purpose of further demonstrating real-world feasibility of the single ear-ECG, a study of such multi-modal single ear-ECG measurements, partnered with the development of more sophisticated machine learning approaches to both the localization and high SNR extraction of cardiac rhythms should also be investigated. Some progress in these areas has been achieved in recent work on deep-matched filtering for enhanced cardiac rhythm localization in cross-ear ECG [ 38 ].…”

An In-Ear PPG-Based Blood Glucose Monitor: A Proof-of-Concept Study

“…In this study, we used the infrared (880 nm) LED of the MAX30101 digital PPG chip by Maxim Integrated (San Jose, CA, USA), which has been used in a number of studies based on in-ear PPG acquisition [ 26 , 27 , 28 ]. The PPG chip was positioned on a thin rectangular printed circuit board with decoupling capacitors and was then embedded onto a viscoelastic foam earpiece, and attached to a silicon-based ear hook as shown in Figure 2 .…”

“…Given the privileged position of the head on the human body, the ear provides a convenient and stable site for physiological measurement, while the ear canal also acts as an insulator of external electrical noise [ 21 ]. Several studies have used the ear canal for general physiological sensing which includes electroencephalography (EEG) [ 22 , 23 , 24 ], electrocardiography (ECG) [ 25 ], and PPG [ 26 , 27 , 28 ], and have reported promising results when compared to clinical devices.…”

From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People

“…In this context, Hearables [17], devices worn in the ear, have emerged as a practical solution for sleep analysis [18,19,20,21,22]. Such devices can monitor various physiological and non-physiological signals including EEG [23,24], electrocardiogram (ECG) [25,26,27], cognitive workload [28], and daily activities [29]. Findings from studies utilizing standardized ear-EEG sensors reveal a considerable correlation between automatic sleep stage prediction using ear-EEG and the hypnogram derived from a PSG [18,19].…”