Hybrid circular-square complementary split-ring resonator sensor for monitoring abnormal blood protein levels for disease diagnosis
4 affiliations
- 1Department of Electronics and Communication Engineering, National Institute of Technology, Mizoram
- 2Department of Electronics and Telecommunication Engineering, Kolaghat Government Polytechnic
- 3Department of Electronics and Telecommunication Engineering, Indian Institute of Engineering Science and Technology
- 4College of Medicine and Sagore Datta Hospital
Abstract
The proposed work aims to develop a compact, low-cost, and commercially viable microwave sensor for early monitoring of high protein content in blood, which can act as a biomarker for highly fatal diseases like Cancer, Rheumatoid Arthritis, and human immunodeficiency virus (HIV). To achieve this objective, a highly sensitive, metamaterial-based sensor, incorporating novel hybrid circular-square shaped complementary split ring resonators (CSRR), at ISM band frequency range of 2.4 GHz is proposed. The effectiveness of the proposed sensor is validated on in-house developed blood-mimicking liquid samples placed in a microfluidic channel (polydimethylsiloxane). Minute changes in its dielectric properties, caused by an increase in its protein levels, are reflected in terms of the variation in the magnitude and frequency of the S21 response of the sensor. Specifically, the average transmission amplitude sensitivity (TAS) and the average frequency sensitivity (AFS) of the proposed sensor are 1.37 dB/(mg/ml) and 0.57 GHz/(mg/ml), respectively. Various machine learning classifiers were designed and analysed to categorise the simulation data of the proposed sensor into four different stages based on the protein concentration, among which the performance of the XGBoost-based classifier was found to be most effective. This work presents a proof-of-concept demonstration of the proposed microwave sensing methodology for potential real-world blood protein monitoring applications.
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BibTeX
@article{patel2026hybrid,
title = {{Hybrid circular-square complementary split-ring resonator sensor for monitoring abnormal blood protein levels for disease diagnosis}},
author = {Shalini Patel and Sandip Paul and Adarsh Singh and Subhasish Sarkar and Debasis Mitra and Chaitali Koley},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-63434-x},
url = {https://doi.org/10.1038/s41598-026-63434-x},
note = {CC BY},
}
RIS
TY - JOUR
TI - Hybrid circular-square complementary split-ring resonator sensor for monitoring abnormal blood protein levels for disease diagnosis
AU - Shalini Patel
AU - Sandip Paul
AU - Adarsh Singh
AU - Subhasish Sarkar
AU - Debasis Mitra
AU - Chaitali Koley
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-63434-x
UR - https://doi.org/10.1038/s41598-026-63434-x
AB - The proposed work aims to develop a compact, low-cost, and commercially viable microwave sensor for early monitoring of high protein content in blood, which can act as a biomarker for highly fatal diseases like Cancer, Rheumatoid Arthritis, and human immunodeficiency virus (HIV). To achieve this objective, a highly sensitive, metamaterial-based sensor, incorporating novel hybrid circular-square shaped complementary split ring resonators (CSRR), at ISM band frequency range of 2.4 GHz is proposed. The effectiveness of the proposed sensor is validated on in-house developed blood-mimicking liquid samples placed in a microfluidic channel (polydimethylsiloxane). Minute changes in its dielectric properties, caused by an increase in its protein levels, are reflected in terms of the variation in the magnitude and frequency of the S21 response of the sensor. Specifically, the average transmission amplitude sensitivity (TAS) and the average frequency sensitivity (AFS) of the proposed sensor are 1.37 dB/(mg/ml) and 0.57 GHz/(mg/ml), respectively. Various machine learning classifiers were designed and analysed to categorise the simulation data of the proposed sensor into four different stages based on the protein concentration, among which the performance of the XGBoost-based classifier was found to be most effective. This work presents a proof-of-concept demonstration of the proposed microwave sensing methodology for potential real-world blood protein monitoring applications.
SN - 2045-2322
ER -