Hearables: Feasibility of Recording Cardiac Rhythms from Single Ear Locations

Metin, Yarici,; Rosenberg, Wilhelm von; Hammour, Ghena; Davies, Harry J.; Amadori, Pierluigi Vito; Lingg, Nico; Demiris, Yiannis; Mandic, Danilo P.

doi:10.48550/arxiv.2301.02475

Cited by 2 publications

(2 citation statements)

References 9 publications

(11 reference statements)

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Hammour,

Davies,

Atzori

et al. 2024

IEEE J. Transl. Eng. Health Med.

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Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG. Methods and procedures: The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work [1]. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches [2,3,4,5]. Results: Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p <0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process. Conclusion: Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques. Clinical impact: An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.

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Section: Introductionmentioning

confidence: 99%

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

Hammour,

Davies,

Atzori

et al. 2024

IEEE J. Transl. Eng. Health Med.

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“…However, with the immense gain in comfort and wearability afforded by an in-ear sensor compared to electrodes on the chest, comes a drop in signal to noise ratio. The potential difference across the heart is often as much as 2 orders of magnitude lower from the ear than it is at the chest [7]. Moreover, the Ear-ECG commonly contains other signals comparable in amplitude, such as electrical activity generated by eye movements, known as electrooculography (EOG) [8], and electrical signals generated by neuronal activity in the brain, known as electroencephalography (EEG) [9][10] [11].…”

Section: Introductionmentioning

confidence: 99%

In-Ear SpO₂ for Classification of Cognitive Workload

Davies¹,

Williams²,

Hammour³

et al. 2023

IEEE Trans. Cogn. Dev. Syst.

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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.

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Hearables: Feasibility of Recording Cardiac Rhythms from Single Ear Locations

Cited by 2 publications

References 9 publications

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

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

In-Ear SpO₂ for Classification of Cognitive Workload

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