Publications
6 results found
Chanh HQ, Ming DK, Nguyen QH, et al., 2023, Applying artificial intelligence and digital health technologies, Viet Nam, BULLETIN OF THE WORLD HEALTH ORGANIZATION, Vol: 101, Pages: 487-492, ISSN: 0042-9686
Karolcik S, Ming D, Yacoub S, et al., 2023, A multi-site, multi-wavelength PPG platform for continuous non-invasive health monitoring in hospital settings, IEEE Transactions on Biomedical Circuits and Systems, Vol: 17, Pages: 349-361, ISSN: 1932-4545
This paper presents a novel PPG acquisition platform capable of synchronous multi-wavelength signal acquisition from two measurement locations with up to 4 independent wavelengths from each in parallel. The platform is fully configurable and operates at 1ksps, accommodating a wide variety of transmitters and detectors to serve as both a research tool for experimentation and a clinical tool for disease monitoring. The sensing probes presented in this work acquire 4 PPG channels from the wrist and 4 PPG channels from the fingertip, with wavelengths such that surrogates for pulse wave velocity and haematocrit can be extracted.For conventional PPG sensing, we have achieved the mean error of 4.08 ± 3.72 bpm for heart-rate and a mean error of 1.54 ± 1.04% for SpO2 measurement, with the latter lying within the FDA limits for commercial pulse oximeters. We have further evaluated over 700 individual peak-to-peak time differences between wrist and fingertip signals, achieving a normalized weighted average PWV of 5.80 ± 1.58 m/s, matching with values of PWV found for this age group in literature. Lastly, we introduced and computed a haematocrit ratio (Rhct) between the deep IR and deep red wavelength from the fingertip sensor, finding a significant difference between male and female values (median of 1.9 and 2.93 respectively) pointing to devices sensitivity to Hct.
Le V-KD, Hai BH, Karolcik S, et al., 2022, vital_sqi: A Python package for physiological signal quality control, FRONTIERS IN PHYSIOLOGY, Vol: 13
Karolcik S, Miscourides N, Cacho-Soblechero M, et al., 2020, A High-Performance Raspberry Pi-Based Interface for Ion Imaging Using ISFET Arrays, IEEE SENSORS JOURNAL, Vol: 20, Pages: 12837-12847, ISSN: 1530-437X
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Karolcik S, Miscourides N, Georgiou P, 2019, Live Demonstration: A Portable High-Speed Ion-Imaging Platform Using a Raspberry Pi, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
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- Citations: 1
Cacho-Soblechero M, Karolcik S, Haci D, et al., 2019, Live Demonstration: A Portable ISFET Platform for PoC Diagnosis Powered by Solar Energy, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, ISSN: 2163-4025
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