Ata Jedari Golparvar

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Flexible Silk-Based Graphene Bioelectronics For Wearable Multimodal Physiological Monitoring

Ata Jedari Golparvar

We introduce a skin-inspired epidermal bioelectronic patch (EBP) with unprecedented integration of graphene on silk with a facile and cost-effective fabrication process, excluding complex ink formulation. We advance the state-of-the-art by directly screen-printing pristine graphene ink on "donor" substrates like polyethylene terephthalate (PET) and successfully receiving the printed graphene traces onto silk protein-based "carrier" substrates by a controlled drop-casting, curing, and transfer printing process. The presented approach retains the conductivity of the pristine graphene ink prepared by reducing graphene oxide suspension through "green" chemistry based on a water-soluble vitamin C reagent. We demonstrate that silk/graphene epidermal patches (SGEP) can conformally attach to human skin without additional adhesives or gel materials. Our study reveals a superior response of the SGEP by successfully acquiring multimodal physiological signals such as cardiac-ECG, macular-EMG, and ocularEOG with almost perfect correlation reaching a record high of 98.48% to those recorded simultaneously with medical-grade, pre-gelled with adhesive backings, wet Ag/AgCl electrodes.

IEEE2023

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Optimal digital filter selection for remote photoplethysmography (rPPG) signal conditioning

Ata Jedari Golparvar

Remote photoplethysmography (rPPG) using camera-based imaging has shown excellent potential recently in vital signs monitoring due to its contactless nature. However, the optimum filter selection for pre-processing rPPG data in signal conditioning is still not straightforward. The best algorithm selection improves the signal-to-noise ratio (SNR) and therefore improves the accuracy of the recognition and classification of vital signs. We recorded more than 300 temporal rPPG signals where the noise was not motion-induced. Then, we investigated the best digital filter in pre-processing temporal rPPG data and compared the performances of 10 filters with 10 orders each (i.e., a total of 100 filters). The performances are assessed using a signal quality metric on three levels. The quality of the raw signals was classified under three categories; Q1 being the best and Q3 being the worst. The results are presented in SNR scores, which show that the Chebyshev II orders of 2nd, 4th, and 6th perform the best for denoising rPPG signals.

IOP Publishing Ltd2023

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Stimulated by the COVID-19 outbreak, the global healthcare industry better acknowledges the necessity of innovating novel methods for remote healthcare monitoring and treating patients outside clinics. Here we report the development of two different types of graphene textile electrodes differentiated by the employed fabrication techniques (i.e., dip-coating and spray printing) and successful demonstration of ergonomic and truly wearable, single-arm diagnostic electrocardiography (SADE) using only 3 electrodes positioned on only 1 arm. The per-formance of the printed graphene e-textile wearable systems were benchmarked against the "gold standard" silver/silver chloride (Ag/AgCl) "wet" electrodes; achieving excellent correlation up to -96% and -98% in ECG recordings (15 s duration) acquired with graphene textiles fabricated by dip-coating and spray printing techniques, respectively. In addition, we successfully implemented automatic detection of heartrate of 8 vol-unteers (mean value: 74.4 bpm) during 5 min of static and dynamic daily activities and benchmarked their recordings with a standard fingertip photoplethysmography (PPG) device. Heart rate variability (HRV) was calculated, and the root means successive square difference (rMMSD) metric was 30 ms during 5 min of recording. Other cardiac parameters such as R-R interval, QRS complex duration, S-T segment duration, and T -wave duration were also detected and compared to typical chest ECG values.

ELSEVIER SCIENCE SA2023

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