The Deep-Match Framework: R-Peak Detection in Ear-ECG

Davies, Harry J.; Hammour, Ghena; Zylinski, Marek; Nassibi, Amir; Stanković, Ljubiša; Mandic, Danilo P.

doi:10.1109/tbme.2024.3359752

Cited by 1 publication

(2 citation statements)

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“…To detect R-peaks in Ear-ECG, we used a deep matched filter detector introduced by Davies et al [22]. The detector consists of an encoder stage (trained as part of an encoderdecoder module to reproduce ground truth ECG), which operates as a Matched Filter.…”

Section: Validation Of Methods On In-ear Measurementsmentioning

confidence: 99%

“…This classifier consists of a single-layer 1D convolution, followed by a Sigmoid activation function, flattening, and a linear output layer. The proposed method has been shown to provide higher median R-peak recall and precision than standard matched filters [22]. The detector was previously trained using a separate dataset; we did not modify model weights for this study.…”

Section: Validation Of Methods On In-ear Measurementsmentioning

confidence: 99%

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Hearables: In-Ear Multimodal Data Fusion for Robust Heart Rate Estimation

Żyliński,

Nassibi,

Occhipinti

et al. 2024

BioMedInformatics

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Background: Ambulatory heart rate (HR) monitors that acquire electrocardiogram (ECG) or/and photoplethysmographm (PPG) signals from the torso, wrists, or ears are notably less accurate in tasks associated with high levels of movement compared to clinical measurements. However, a reliable estimation of HR can be obtained through data fusion from different sensors. These methods are especially suitable for multimodal hearable devices, where heart rate can be tracked from different modalities, including electrical ECG, optical PPG, and sounds (heart tones). Combined information from different modalities can compensate for single source limitations. Methods: In this paper, we evaluate the possible application of data fusion methods in hearables. We assess data fusion for heart rate estimation from simultaneous in-ear ECG and in-ear PPG, recorded on ten subjects while performing 5-min sitting and walking tasks. Results: Our findings show that data fusion methods provide a similar level of mean absolute error as the best single-source heart rate estimation but with much lower intra-subject variability, especially during walking activities. Conclusion: We conclude that data fusion methods provide more robust HR estimation than a single cardiovascular signal. These methods can enhance the performance of wearable devices, especially multimodal hearables, in heart rate tracking during physical activity.

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Section: Validation Of Methods On In-ear Measurementsmentioning

confidence: 99%

Section: Validation Of Methods On In-ear Measurementsmentioning

confidence: 99%