Wearable devices: monitoring the future?

Ioannidis, Diomidis C; Kapasouri, Efthymia Maria; Vassiliou, Vassilios

doi:10.1093/omcr/omz143

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Wearable health monitoring devices equipped with plethysmography (PPG) sensors were shown to have strong potential in improving the cardiovascular disease monitoring (McConnell et al 2018, Ioannidis et al 2019, Raja et al 2019. PPG-enabled devices contain an optical sensor that detects blood volume changes through the skin (Castaneda et al 2018).…”

Section: Introductionmentioning

confidence: 99%

A supervised machine learning semantic segmentation approach for detecting artifacts in plethysmography signals from wearables

Guo

Ding

et al. 2021

Physiol. Meas.

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Objective. Wearable devices equipped with plethysmography (PPG) sensors provided a low-cost, long-term solution to early diagnosis and continuous screening of heart conditions. However PPG signals collected from such devices often suffer from corruption caused by artifacts. The objective of this study is to develop an effective supervised algorithm to locate the regions of artifacts within PPG signals. Approach. We treat artifact detection as a 1D segmentation problem. We solve it via a novel combination of an active-contour-based loss and an adapted U-Net architecture. The proposed algorithm was trained on the PPG DaLiA training set, and further evaluated on the PPG DaLiA testing set, WESAD dataset and TROIKA dataset. Main results. We evaluated with the DICE score, a well-established metric for segmentation accuracy evaluation in the field of computer vision. The proposed method outperforms baseline methods on all three datasets by a large margin (≈ 7 percentage points above the next best method). On the PPG DaLiA testing set, WESAD dataset and TROIKA dataset, the proposed method achieved 0.8734±0.0018, 0.9114±0.0033 and 0.8050±0.0116 respectively. The next best method only achieved 0.8068±0.0014, 0.8446±0.0013 and 0.7247±0.0050. Significance. The proposed method is able to pinpoint exact locations of artifacts with high precision; in the past, we had only a binary classification of whether a PPG signal has good or poor quality. This more nuanced information will be critical to further inform the design of algorithms to detect cardiac arrhythmia.

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Section: Introductionmentioning

confidence: 99%